Mobile only works in landscape
Michael Moutoussis
Μιχαήλ Μουτούσης


    Msc Thesis

      Executive control of Attention in Alzheimer's Disease :

      A modelling approach

      Thesis for the degree of   Master of Science in Psychiatric Theory and Research Methods


      by Dr. Michael Moutoussis
      at the Department of Psychiatry, University College London Medical School,  Wolfson Building, Middelesex Hospital, London W1 8AA ,
      Supervised by  Dr. Martin Orrell, Dept. of Psychiatry  and  Dr. George Houghton, Dept. of Psychology


      To Prof. Walter Freeman, for the teaching, and to Pam, for everything.


      I. Abstract

      II.  Introduction
      1. The importance of executive functions in Alzheimer's disease
      2. Dual Attention and the Central Executive System
      3. Dual Attention and the Central Executive System
      4.  Functions preserved in AD - Implications for Attention and Planning
      5. Dementia, education and executive function
      6. Mathematical modelling in Psychiatry : some difficulties
      7. Modelling efforts in Alzheimer's disease
      8. Mathematical models of Attentional Control
      9. Cerebral Localisation of Supervisory Attentional / Central Executive processes
      10. Neurochemical findings
      11. Physiologically based modelling
      12. Dynamical aspects of biological models
      III. Aims of the present study
      1. To develop the theory of normal 'central executive' function
      2. To simulate dual task performance deficits demonstrated in AD
      3. To identify important limitations of the proposed models and to suggest ways to overcome them
      IV. Methods
      1. Methods used for Systematic Review of literature
      2. Criteria for setting model structure
          A. Parameter setting
          B. Estimation of model success
      3. Effective use of programming environments
          A. Programming Language
          B. Operating systems
          C. Hardware
      4. Core model structure and programming
      5. Auxiliary programming & software
      6. Program quality control
          A. Code integrity
          B. Program design
      7. Mathematical Analysis
      V. Results
      1. Models directly based on the Houghton attentional control theory
          A. Direct lateral inhibition - only model
          B. Descending attentional control
          C. Descending and ascending 'matching' attentional system
      2. Direct lateral inhibition plus descending/ascending match models
      3. Revised model - preliminary results
          A. Alternative neural unit equations
          B. Alternative matching mechanism
      VI. Discussion
      1. Rationale of the present study
      2. Critique of methods
      3. Interpretation of findings
          A. Models directly based on Houghton's theory
          B. The success of lateral inhibition : Interpretation and novel predictions of the augmented model.
      4. Next step models
      5. Reflections in the light of other findings
      6. New directions for further research
          A. Structure of a revised model
          B. Evaluation of the revised model
          C. Education and Alzheimer's disease
      7. Summary & Conclusion
      VII. Acknowledgements
      VIII. References
      IX. Appendices
      1. Appendix I: C++ code samples
          A. Example of 'main' program code
          B. Example of a C++ class hierarchy
      2. Appendix II: Example of Literature search strategy

      I. Abstract


      Introduction : In Alzheimer's disease (AD) executive and working memory deficits often compromise the safety and independence of patients. Such executive functions are understood to organise working memory. The latter has been described as consisting of "slave" systems co-ordinated by a Central Executive System (CES) that controls attention. The CES is particularly affected in AD. Computer models have contributed greatly to the understanding of selective attention, but not with reference to AD. We explore the potential for an understanding of the loss of executive function in AD using computer modelling and we review recent research from a variety of fields whose integration is central to this approach. Research has suggested that high educational achievement protects against the development of AD, possibly by helping preserve such executive functions. This finding is explored in the light of the modelling studies. Executive function in AD has been investigated experimentally, most notably by the group of Baddeley and colleagues. Dual task performance in normal elderly controls has been compared with that of AD patients. In the AD group, performance in any task was reduced by the presence of a concurrent task. With the passage of time the effect of any concurrent task on any primary one increased for the AD group but not for the controls. This demonstrated a specific executive deficit in the patient group.

      Aims : It is feasible to develop rigorous models of 'central executive' function. The main hypothesis of our study is that the application of the computational model of attentional control of Houghton and co-workers could account for the pattern of deficit observed in AD patients.

      Methods : Dual task performance data was simulated by using models of attentional control derived from those of Houghton and co-workers. In these models for each sensory modality bottom-up and top-down signals are compared in 'match-mismatch' modules. The output of these modules can excite or inhibit lower level sensory modules towards particular input features. Two attentional control systems, one for each modality, were combined in the present simulations to account for dual task performance. Sensory and 'match-mismatch' modules were linked by cross modality inhibitory attentional control whose implementation here was guided by the neuropathology of early AD. Key building blocks of the original Houghton model were analysed mathematically to explain the behaviour of the overall models. Alternative building blocks and structural modifications were explored.

      Results :

      1. The models adequately simulated performance of controls.

      2. Reduction in single task performance in AD was also successfully simulated.

      3. Reduction in dual relative to single task performance in AD was only simulated for narrow parameter ranges (was not robust).

      4. Model capacity for gradual performance deterioration was limited.

      5. Positive feedback loops involving units such as those used by Houghton have drawbacks such as spurious activated states and limited capacity for increases in gain.

      6. These problems can be overcome by using units with a flatter response at their rest state.

      7. Incorporation of direct lateral inhibition at the level of sensory cortex greatly improves model performance.

      8. Preliminary implementation of the matching mechanism as synchronisation in the oscillatory activities of two brain areas and of lateral inhibition as oscillatory noise interference between modalities is successful in qualitatively simulating single and dual task performance for normal controls and for sufferers of Alzheimer's disease.

      Discussion : The models described here were successful in simulating many features of attentional control. However, they do not robustly predict the experimental results without substantial modification. When Houghton's theory is reviewed most of the conceptual essentials are retained, but many important details of implementation are rejected. More physiological details of neural masses help improve models. It is argued that direct lateral inhibition is unlikely to be a biological default between modalities: its essential point is likely to be the presence of pathway interference at specific levels (rather than across levels) which is preserved or even augmented in AD. In the light of recent neuroscientific findings, a modified theory is suggested that postulates 'Match / mismatch' function to involve oscillatory synchronisation. Interference within levels (similar to direct lateral inhibition) is hypothesised to impair synchronisation between levels. Preliminary simulations support this theory but more extensive exploration is required. It is envisaged that in the future the study of executive function may aid the assessment of the ability for independent living of patients and the understanding of the protective role of education in AD.

      II. Introduction

      1. The importance of executive functions in Alzheimer's disease

      Dementia is one of the most frightening and costly diseases to affect humanity, one that eventually destroys most mental abilities. Memory deficits are prominent but not necessarily sufficient to impose severe restrictions in the patient's lifestyle and independence. When, however, executive as well as mnemonic functions are affected, so that the patient cannot carry out the schemas necessary for daily activities, the patient puts herself in danger. An example would be having difficulty coordinating the sequence of actions required to make a cup of tea. It is therefore of great practical importance to understand and support those mental mechanisms concerned with executive and attentional functions.

      Research into Alzheimer's disease (AD), the commonest cause of dementia, has therefore explored in recent years the 'executive' deficits (Baddeley et al, 1991). The term `executive' is used in different ways by different researchers and in different contexts, in line with the ignorance characterising new fields of enquiry. Jaak Panksepp (1998) defines: " 'Executive system' implies that a neural system has a superordinate role in a cascade of hierarchical controls ". 'Executive' is applied to planning, sequencing, control of attention and of working memory and specific functions such as cognitive set shifting or suppression of dominant tendencies.

      2. Psychological theories of executive control of attention

      Specific functional modules, such as the Supervisory Attentional System or the related Central Executive System (CES) have been proposed to malfunction in AD in order to account for executive deficits (Baddeley 1996). According to such psychological models a 'Supervisory Attentional System' is hypothesised to co-ordinate the lower level, or 'slave', components of working memory, i.e. the 'Phonological Loop' and the 'Visuospatial Scratchpad'. Since Baddeley's pioneering work there has been active debate as to the modularity and brain localisation, of functional modules that subserve and co-ordinate working memory (Wickelgren, 1997).

      3. Dual Attention and the Central Executive System

      Baddeley and colleagues (1991) examined executive function in a series of experiments which form the basis for the present analysis. They attempted to test whether AD disproportionately affects Central Executive allocation of attention, so that dual task performance is affected more than the constituent tasks. They compared patients with mild AD with normal elderly controls and followed them up over one year. In these experiments a primary tracking task, following a randomly moving white square, was combined with graded secondary tasks. The difficulty of the tracking task was first individually adjusted in the absence of secondary tasks so that subjects managed to stay on the square for 40-60% of the time. The difficulty was then fixed. The simplest concurrent secondary task was 'articulatory suppression': the subject counted repeatedly from 1 to 5 during pursuit tracking. The next stage was reaction time to tones : the subject had to press a foot switch as soon as an auditory stimulus was presented. Reaction times and percentage of missed tones were recorded. The final stage was a memory span task. In this the maximum length of a random digit sequence that the subject could reliably repeat back was determined in the absence of pursuit tracking prior to each of the three testing sessions. Sequences of this 'subject-tailored' maximum length were then presented during pursuit tracking and recall performance recorded.

      The results obtained were consistent with the hypothesis. In the AD group, performance in any task was reduced by the presence of a concurrent task. A key result was that with the passage of time the effect of any concurrent task on any primary one increased for the AD group but not for the controls, supporting the hypothesis of a specific executive deficit in the patient group.

      4. Functions preserved in AD - Implications for Attention and Planning

      In a series of experiments complementing those of Baddeley, Simone & Baylis (1997) showed that executive function was impaired in AD using a paradigm involving suppression of a dominant response tendency. Subjects had to quickly respond to a green light, but ignore a yellow one. AD subjects showed many false positive responses, consistent with weak executive control. To test for the cognitive level at which errors occurred, subjects were asked how sure they were that they responded correctly. AD patients were aware of their erroneous choices. The authors concluded that the executive deficit did not involve early information processing but rather the efficient implementation of a response.

      5. Dementia, education and executive function

      Recent epidemiological findings in dementia are related to the above concerns. Low educational achievement appears to be an independent risk factor for the development of dementia (Orrell & Sahakian, 1995). The aged of lower educational achievement more commonly fail tests of activities of daily living (Zhang et al, 1990), while the better educated require greater damage to cortical areas important for executive function to get as impaired (Alexander et al, 1997). The mechanisms underlying the protective effect of education are however poorly understood. Mathematical modelling could help clarify relevant hypotheses.

      6. Mathematical Modelling in Psychiatry: some difficulties

      In most sciences modelling has an important role between experimental studies and theoretical analysis. It is not, however, obvious that mathematical modelling has much place in the neurosciences in general and in psychiatry in particular. Psychiatric theories are often conceived in qualitative terms and expected predictions are derived on the basis of semantic inference and common sense. However, many theories are not precise enough (they may be underspecified) to derive accurate predictions from; or the system involved may be too complicated for one to derive predictions by common sense (the theory may be intractable).

      A rigorously specified model has many advantages : First, it forces a more complete description of the problem. The key variables have to be defined and theoretical assumptions become explicit. Second, explicit alternative explanations of the data on which the model is based can be formulated. Third, detailed predictions can be made: therefore falsification of a rigorously defined model is easier i.e. the theory provides better means for its own falsification (Notturno, 1984). Fourth, counterintuitive predictions of the theory may become apparent. These may explain already existing data difficult to understand on the grounds of common sense. It may also be rigorously tested whether data seemingly contradicting the theory could in fact be compatible with it. Fifth, ignorance about the theory can be quantified, e.g. in terms of model parametres. Results of further experiments can be anticipated and such experiments planned. Finally, once successful models are developed, numerical experiments can be performed that would be too difficult to carry out in vivo.

      Although mathematical modelling is thus indispensable in the hard sciences, it has not fared as well in psychiatry. There are several reasons for this. Biological systems are so complicated that people think that a lot more needs to be known about them before meaningful modelling can take place. It is often unclear that biological systems have laws possessing explanatory power independent of the system's fine structure. It is also unclear how components and their interactions at any particular level of description give rise to collective properties in a non-trivial manner. Indeed, the theory of evolution favours a top-down, 'how is this feature teleologically reasonable', view over the reductionist, bottom up modelling approach. In addition it is sometimes possible to construct several different models that explain the data. These models may be difficult to tell from each other by experiment or may be poor at making novel predictions.

      Mathematical modelling is culturally alien to psychiatrists; most importantly the questions that could be investigated by modelling simply do not occur to us. Our heuristics of science do not include the values of rigour, unification of ideas, economical description of phenomena and beauty of mathematical structure that guide the hard sciences.

      Finally, biological phenomena usually involve self-organising structures that consume energy. In such systems measured variables do not change in proportion each other, i.e. the systems are non-linear (Nicolis, 1991; Nicolis, 1989; Kelso, 1995). The theory of nonlinear systems has only recently emerged and its neurobiological applications are still at an early stage.

      7. Modelling efforts in Alzheimer's disease

      Despite these obstacles, considerable inroads have been made in the modelling of AD. Connectionism has aided the understanding of psychiatric disorders and of AD in particular. A connectionist model consists of interconnected units representing neurones or groups of neurones. Units can have simple internal structure yet their networks can perform complex functions, made possible by appropriate patterns of synaptic interconnections (Jeffery & Reid, 1997). This is an anatomically inspired approach that can be used to model brain function at the level of groups of cells (Traub et al, 1997) or of individual cortical areas (Freeman, Yao & Burke, 1988). The role of neurotransmitters can be elucidated. Models can be used to investigate the transition from the level of nerve activities to that of objects of perception and action (Freeman, 1991). Contextual meaning and hence emotion can be taken into account (Armony et al, 1995). At the 'highest' (symbol manipulation) level connectionist models can be used to simulate objects of cognition such as memories (Hartley & Houghton, 1996). At the neurotransmitter level they can help explain observed psychopathology (Cohen & Servan-Schreiber, 1992).

      The first goal of modelling in AD has been the understanding of dysmnesia. Pioneering studies (Carrie, 1993) used highly abstract connectionist models. These explained the distributed storage of memories, their initial resistance in the face of gradual neuronal loss (simulated by unit deletion in the network) and their subsequent smooth decline. The use of fairly realistic learning algorithms allowed important shortcomings of the model to be identified. The greater impairment in new learning relative to memories laid before 'atrophy' could not be explained, and gradual deletion of neurones from biologically more realistic networks failed to produce a gradual decrease in recall. Performance remained intact until a large proportion of network elements were lost, then dropped catastrophically. To simulate the gradual course of the illness it was necessary to introduce synaptic compensation as found in the real brain (Ruppin & Reggia, 1995). Synaptic compensation is a biological constraint which permits the models to explain the gradual degradation of recall performance, the differential sparing of remote memories and the increased rate of false positive retrieval errors found in AD.

      In contrast to these sophisticated studies of memory little attention has been paid to the modelling of executive function, despite its clinical importance. Modelling loss of synapses might explain weakened executive control while synaptic compensation might help explain the excess of false positive responses, accounting for the findings of both Baddeley and Tipper discussed above.

      8. Mathematical models of Attentional Control

      The starting point for the present simulations is the theory of attentional control of Houghton and colleagues (Houghton & Tipper 1994; Houghton 1995). In these models each sensory modality consists of low level sensory modules, high level modules where the behavioural goals or targets of the organism are stored and intermediate, 'match-mismatch', modules that compare percepts with targets (fig 1a).

      Figure 1a. Layout and component units of the Attentional Control mechanism. The Target field (A) carries a partial description of objects to be sought during a given task. Their activation signifies some priority, such as 'look for this'. The Object field (C) represents sensory areas. It carries Property Units representing features of sensory stimuli. Each Property Unit gives excitatory input to two 'gain control' units ('On' and 'Off'). The 'On' gain unit gives positive feedback to the property unit, the 'Off' negative. The 'On' makes the property unit more responsive to the presence of the corresponding feature, the 'Off' less so. The equations governing these units are discussed in the Results section. The Match / Mismatch module (B) consists of pairs of elements. Match units fire (and Mismatch stay silent) when a feature activated in the 'object' field is also present in the top down description of the Target field. If the feature is present only in the Target field, only the Mismatch units fire. Output units correspond to the response schemata relevant to different percepts.


      Figure 1b. Connectivity that implements the Attentional Control mechanism. Filled circles are inhibitory synapses, arrowheads excitatory (as in following figures). The Target field units feed to the corresponding Match/Mismatch unit pairs. For each target feature (see 7) there is a unit pair (6) calculating whether the feature is present, on the basis of input by the corresponding Property unit (e.g. unit d gives bottom up input 5). If a match is present the corresponding Match unit activates the On unit of the Property unit coding the same feature (1) and inhibits its sister Off unit (2), thus increasing the responsiveness of the Property unit. If a mismatch is detected the Mismatch unit becomes active and reduces responsiveness (3, 4). Negative feedback tends to reduce the activation of Property units subject to mismatch. Coactivated features then cooperate to dominate perception and activate an appropriate response. If for example property units a and b belong to the same object, and unit a is activated, it tends to facilitate activation of b both directly (12) and indirectly (10,11). Finally an emergent assembly of features, signifying an object, activates a response schema.

      The output of the 'match-mismatch' modules can excite or inhibit the lower level sensory modules towards particular input features, and thus attend or ignore these particular features: this is the crucial mechanism of attentional control in the model. The activated features are then combined into attended percepts.

      The models of Houghton and co-workers can simulate a large number of experimental data on selective attention. These include perceptual distracter processing, negative priming, response binding in the presence of distractors and inhibition of return. The models are built around important principles such as attentional control of perceptual gain regulation, opponent processing and competitive-cooperative intra-module interactions. Drawbacks of the model include first, its loose basis on physiology. This is most evident in the match / mismatch module. Its units perform logical computations whose physiological implementation is quite unclear. Secondly, the whole model is constructed on the basis of information-processing constraints. This is no bad thing in itself but it ignores how dynamical constraints can give rise to structure. Levine, Parks & Prueitt (1993) in their review of the methodology of simulation of `frontal' cognitive functions expect that functionally and structurally distinct levels of brain activity are separated by underlying dynamical constraints. Thirdly, the Houghton model is over-stable. Once perceptual input is terminated object assemblies tend to persist. This necessitates the introduction of decaying synaptic weights between units of the Object field (connections (9) and (10) in fig. 1b). Target node activities also have to be made to decay. Freeman (1992) has discussed how positive and negative feedback loops in neural systems need not convey excessive stability if allowed to operate within oscillatory regimes.

      Context - dependent allocation of attention has been studied by Cohen and coworkers (1992) using a model of the Stroop task. In this task subjects are presented with dual stimuli, e.g. the name of a colour spelt out and the ink colour in which the word is written. They are asked to either read what the word says or to name its ink colour as quickly as possible. Each 'modality' (reading vs. colour naming) has its own 'pathway' in the model. Both are influenced by a 'task demand' module which the authors identify with a function of the prefrontal cortex. Cohen finds that 'attentional selection can be thought of as the mediating effects that the internal representation of context [here, of the instruction the subject has received from the experimenter] has on processing' (fig. 2).

      Compared to the theory of Houghton and coworkers, the model of Cohen et al includes a simpler 'Matching' mechanism (another way of looking at the function of the hidden units !) capturing essential biological plausibility. It doesn't, however, include any cross-modality interaction except at the output level; Its simplest modification to conform to the dual task layout would therefore completely decouple modalities. It could therefore not predict dual-task interference effects. An important finding from the work of Cohen and coworkers is that 'the degree to which a process relies on attention is determined by the strength of the underlying pathway'. This may mean that a disproportionate attentional deficit may be not because a 'Central Executive' is particularly affected in AD but because eroded underlying pathways would take stronger attentional modulation to perform.

      Figure 2a. Modular structure of the model of attention in the Stroop task according to Cohen and coworkers. External input is received by the input units, C ; In order for it to activate the output units, D, activity passes through the intermediate, 'hidden' units B. These are in part activated by units A which express the context to which the organism must give priority. These are similar to the 'target' units of the Houghton models (fig. 1) in that they are independently and externally activated.

      Figure 2b. Detailed structure of Cohen's Stroop task model. All the synapses in or by the 'reading pathway' are shown. The connections of the colour naming pathway are similar but weaker. Note the absence of cross modality inhibition.

      Figure 2c. The 'hidden' units of the Cohen model perform a graded AND (or 'match') function by becoming activated, and therefore allowing signal to propagate along their respective pathways, when not only bottom-up excitation is present from the stimulus but also descending facilitation.

      9. Cerebral Localisation of Supervisory Attentional / Central Executive processes

      The concurrent tasks involved in the Baddeley experiments were chosen so as to minimise competition for local resources and thereby to highlight the possibly shared 'Central Executive' requirements. Thus the primary task - tracking - is presumed to involve the 'visuospatial scratchpad' slave system while the secondary the 'phonological loop' for example. Of course both are shown to depend on attention 'allocated' to them by the presumed central executive. While earlier imaging studies showed little overlap between the brain areas activated by verbal and non verbal working memory tasks (Raichle, 1993), more recent studies have concluded that the dorsolateral prefrontal cortex contributes to the maintenance of both verbal and nonverbal information (Fiez et al, 1996). Recent imaging studies indicate that rCBF decreases in sensory areas that are not to be attended (Drevets et al, 1995), in agreement with theoretical studies that place emphasis on inhibitory mechanisms in attention.

      10. Neurochemical findings

      The deterioration of the cholinergic system in AD is thought to affect attention significantly. Sahakian has demonstrated that when cholinergic function enhancing drugs improve neuropsychological performance in AD this is attributable more to an improvement of attentional rather than memory function (Lawrence & Sahakian, 1995).Imaging studies indicate that Scopolamine, a cholinergic antagonist, attenuates memory-task-induced increases of rCBF in the right anterior cingulate but also bilaterally in the prefrontal cortex.

      Taken together these findings support a model of AD where elements corresponding to particular aspects of prefrontal function are either damaged or functionally dysconnected (Schreiter-Gasser et al, 1993; Morris, 1994) to the rest of the model. The primary candidates for this, in Houghton's terms, would be the 'target field' and the 'match-mismatch' (fig. 1a). Damage to either would result in reduced attentional control on lower-level units, a priori equally impairing excitatory and inhibitory control.

      11. Physiological basis for modelling

      In most connectionist psychological models units are very simplified in biological terms, being derived more on the basis of constraints from psychology. This does not by itself imply that the functional approximation is crude, as biological systems are self-organising, far from equilibrium and hence their emergent properties at any one level may be robust within a functionally important range of conditions. However the adequacy of the approximation is always a matter of concern.

      Numerous abstract models are used to provide tractable equations for modelling neuronal elements. Often used is the (logistic) sigmoid neuron where output is a sigmoid function of the sum of all inputs to the neuronal soma within a preceding short time interval. Continuous time models usually include a differential operator acting on neuronal state variables equated with a sigmoid function of inputs, which may involve delays. The prototype is the model of Wilson & Cowan (1973). The units used in the work of Houghton et al involve first order operators and no delays. More physiological alternatives range from the second-order-operator models of neuronal populations of Freeman (1975) to simulations of large number of neurons with details of their ionic currents and other biophysical properties (Traub et al, 1997). I shall consider the logistic sigmoid neuron as a minimum requirement for physiological relevance and more complicated models when detailed time evolution of the system is simulated.

      12. Dynamical analysis and biological models

      It is important to analyse the performance of the neural systems involved in attention not just in terms of facilitation and inhibition of units, but in terms of attractor dynamics. This involves considering what patterns of activity are open to the network, what is the stability of such patterns and how the 'landscape' (phase space) of all such available patterns changes under different conditions. We may, for example, think that a stable performance of the primary task corresponds to an attractor set of the entire network but one with specific directions of relative instability. Perturbations along such directions (corresponding, for example, to the auditory signal) can 'flip' the system into another, possibly transient, 'attractor' set (corresponding to auditory perception, match and output). The motivation for thinking of this model in terms of attractors and their stability comes from several sources.

      First, the model of Houghton et al is, as discussed, over-stable. Its authors thus introduced modifications with little psychological or physiological support. Dynamical analysis of the model's stability could, alternatively, clarify the causes and solutions to the problem in a less ad hoc way.

      Second, while the attractors that are used in most psychological models are point attractors, the ones found experimentally in investigations of thalamocortical interaction and those extensively studied in limbic structures (e.g. entorrhinal cortex, heavily involved in AD) are par excellence oscillatory (Kay, 1996; Gray, 1994). There is evidence for thalamic and limbic areas being involved in attention (Forstl & Sahakian, 1993) and particularly in the matching process. Freeman and co-workers have demonstrated dramatically the effect that attention and behavioural significance have on the patterns of oscillatory activation of the olfactory cortex (Grajski & Freeman, 1989, Eeckman & Freeman, 1991, Kay,1996). An important component of the binding of sensory features into visual percepts also relies on oscillatory attractors (Gray, 1994; Bressler, 1996). In the present models oscillations would arise naturally if realistic synaptic delays were incorporated in the existing negative feedback loops. Superposition of oscillatory attractors, switching between such attractors and global modulation of oscillatory cortical activity differs from equilibrium (point attractor) dynamics as applied to the same brain functions. It is thus important to consider our present models in terms of attractor dynamics to prepare the ground for incorporation of the above findings. Introducing oscillatory dynamics must however be necessitated by both the probable applicability of the physiological findings and the need to use that level of description to overcome limitations of the information processing approach used so far.

      Another reason to consider analysis in terms of attractors is illustrated by the dynamical analysis of errors in a neural network model of dyslexia. This has demonstrated that dynamical analysis can explain some counterintuitive experimental findings that are replicated by neural network models (Hinton, Plaut & Shallice, 1993). Burgess and Hitch (1996) claim that a model that replicates experiment but "cannot be mapped onto a conceptual understanding of the processes giving rise to behaviour is useless". The dyslexia model demonstrated that this conceptual understanding, and therefore the usefulness of such a model, may well depend on the understanding of the attractors involved.


      Modelling Executive & Attentional function in Alzheimer's Disease - 1999 MSc Thesis - M. Moutoussis

      III. Aims



      1. To develop the theory of normal 'central executive' function.

      Attentional control may depend on the matching between sensory representations of perceived objects and partial, 'target 'representations of sought objects (Houghton & Tipper, 1994). The central hypothesis of the present work is that models of this matching process can simulate executive attentional control during dual tasks.

      I aimed to simulate the influence of attention in normal subjects during single- and dual- task conditions by bringing together previously developed models of attentional control ( Houghton & Tipper,1994) with the dual pathway concept of Cohen and co-workers (1992) .

      Baddeley's reaction time experiment could be simulated with a model of attentional control with modality specific pathways (fig. 3) . This will be used as the nucleus of the present study as it includes all the important experimental findings. Once this is adequately modelled, other dual task experiments could be simulated as further tests of the main hypothesis.

      Figure 3 Basic Auditory Reaction time / Tracking simulation architecture. The cross modality attentional control projections are shown in the a priori most likely site.


      2. To simulate dual task performance deficits demonstrated in AD.

      By introducing neuropsychologically plausible 'lesions' in the above model I aim to replicate the experimental findings in mildly demented subjects. AD lesions can be simulated as an impairment of the match / mismatch field. The modules situated closer to the sensory and motor interfaces will in this approximation be treated as intact, in line with the localisation of such modules within sensory cortices, preserved in early DAT.

      As in the Baddeley experiments, a greater attentional control deficit (corresponding to more advanced AD) should lead to increasing divergence between single-task and dual-task performance. I aim to show that progressive 'damage' would suffice to account for the deterioration of DAT subjects over time.

      'Damage' to the model proposed here does not simulate the AD effects in a trivial manner. Reducing the efficiency of cross modality inhibition might decouple modalities, and dual task performance may improve relative to single task performance. This possibility makes the model proposed more falsifiable.

      3. To identify important limitations of the proposed models and to suggest ways to overcome them.

      When model components or architecture are found to limit ability to simulate experimental results, I aim to improve on these not only by introducing the minimum sufficient modifications but also by guiding such modifications by appropriate physiological considerations.

      Modelling Executive & Attentional function in Alzheimer's Disease - 1999 MSc Thesis - Dr. M. Moutoussis

      IV. Methods

      In order to test whether candidate models accounted for the relevant experimental data the following methodology was pursued. First, the literature was kept under regular survey. Second, important principles of model structure were operationalised. Third, suitable programming platforms were chosen and a simulation strategy exploiting the philosophy of these platforms was outlined. Fourth, a hierarchical programming strategy allowed implementation of increasingly complex models. Fifth, auxiliary programming tools were developed for the handling of input and output of models. Sixth, a quality control strategy was implemented to minimise the risk of programming errors.


      1. Methods used for Systematic Review of literature     Not available online - Please e-mail me for details

      2. Criteria for setting model structure         Not available online - Please e-mail me for details

      3. Effective use of programming environments         Not available online - Please e-mail me for details

      4. Core model structure and programming

      A system of interconnectable modules (that can represent neurons or other connectionist 'units' such as lumped cortical areas) with their connections ('dendritic trees'), outputs ('axons') and state variables, was developed (Appendix I). These 'units' could be driven by arbitrary dynamics to specify how they respond to their inputs so as to produce outputs. Classes were programmed to handle time-dependent equations to drive the 'units'. Classes used for solving systems of ordinary differential equations (ODEs) were based on an adaptive step, fourth order Runge-Kutta method (Press et al, 1988). Classes for return maps were also programmed.

      Modelling emulated the progression from single task to dual task experiments and examined the effects of attention and concurrent task on performance. The evolving structure permitted exploration of the simplest models to help determine and the most plausible characteristics of subsequent ones.

      As the Baddeley experiments that I aimed to simulate do not report detailed discrimination of features within each sensory system (e.g. detection of a tone, rather than discrimination between tones, is used), each cortical area was simulated by a 'lumped' unit. Apart from this being common practice, both psychological (Houghton's work) and physiological (Freeman, 1992) demonstrations have been provided of the validity of treating a cortical area as a 'lumped' neural mass for the purposed of simulation of aspects of its overall behaviour, such as its overall activation. The cortical area corresponding to each of the two sensory modalities in a dual task was initially simulated by an 'attentional triad' consisting of one 'perceptual' and two 'gain control' units (fig. 4; cf. fig. 2a field C).

      Figure 4: Models directly derived from the attentional control theory of Houghton & coworkers . Models use 'attentional triads' (gain units labelled + and -) as lumped representations of perceptual cortices. a. Direct lateral inhibition; also synapse and cortical area labels, which also apply to (b.) and (c.) b. Descending control of attention only. c. Descending and ascending connections forming a 'match' mechanism.

      In successive experiments, the two cortical areas were connected by first, Direct Lateral Inhibition (DLI) only; then, descending attentional control (DAC); and thirdly, a 'Match' attentional system (MAS). This last stage represents the model testing the core hypothesis of this study, that an attentional control system featuring matching between target and percept, plus cross-modality inhibition, can simulate the patterns of performance during dual task experiments in normal and, when modified, in AD subjects. Based on this model alternative units for the attentional triad and matching mechanisms, diverging from the Houghton models, were explored. The three basic simulation steps are now described in more detail.


      1. Direct Lateral Inhibition only. It is thought (Nunez, 1995) that most inter-area corticocortical connections are excitatory. Within the Houghton model a lateral inhibitory effect is however easily implemented through excitation by one area of the inhibitory gain units of the other (fig. 4a).

      2. Descending attentional control. Descending influence from a task-specific attentional area provides excitatory input to its 'own' and inhibitory input to the 'opposite' modality (fig. 4b), according to the principle of cross-modality inhibitory attentional control.

      3. Descending and ascending projections implement 'match' loops. As there were no discrimination tasks involved, the necessity to consider 'mismatch' loops did not arise. However, the issue of the details of implementation the 'match' units did arise. Rather than the units being programmed to directly perform logical functions, as in the original Houghton models, the functionally approximate but physiologically more plausible use of a simple logistic sigmoid neurons, such as in fig. 2, was adopted. Parameters for these are not available either from physiology or previous work. They were therefore initially set to be such that for the simulation of normals attending to a single task, the presentation of the stimulus changed 'match' unit activation from a low (~10% of max), resting state to a high (~90% max), activated state. For AD, the matching mechanism was impaired; there was no a priori reason to suggest that such impairment should affect the excitatory output of the mechanism more than its inhibitory output. AD was therefore assumed not to affect their balance but only diminish their output.

      Alternative explorations first examined the effect of DLI coexisting with MAS (fig. 5) and simulating attentional triads consisting of units more representative of the physiological properties of cortex.

      Figure 5: Combination of Direct Lateral Inhibition and a 'match' mechanism.
      Finally an alternative form of the matching process, the Oscillatory Match Model (OMM; see Discussion) was explored. Here two neural masses capable of oscillation were interlinked. Their coupling could lead to synchronisation and possibly amplification of activity, and these are taken to indicate 'matching'. The process was inspired from the extensive work of Kelso (1995) on coupled oscillator systems (fig. 6).

      Figure 6a: Oscillatory model of a 'match' mechanism; Adaptation of the dual pathway model of fig 3. to include oscillatory activity. Cross-modality interference is now not associated with descending control but with local effects, as per interpretation of the lateral inhibition findings. This is shown by the asymmetric influence of the 'match' modules of each pathway on each other (grey arrows)

      The OMM was investigated in simulated models with the following structure.  [ Only a schematic description given here - Please e-mail me for details  ] Two KI sets (Freeman, 1975) interlinked by negative feedback were used to simulate each oscillator (each of the two circles at the top of the diagram of fig. 6). The equation governing the state variable x(i) of the i-th KI set is:


      d2x(i)/dt2 + A*dx(i)/dt + B*x(i)

      = S(j) {I(j)} + S(n) { Kni*Q(n)[x(n)] }, Relation (Rln) 1

      For simulation of the interfering modality a source of  broad-band, brain-activity-like 'noise' was required.  The interfering noise was therefore simulated a system displaying '1/f' spectrum oscillations in the form of Sil'nikov chaos (Kelso & Fuchs, 1995). The model is shown in fig. 6.

      Figure 6b. OMM reduced to its essentials for preliminary study.

      5. Auxiliary programming & software          Not available online - Please e-mail me for details
      6. Program Quality control          Not available online - Please e-mail me for details


      7. Mathematical Analysis

      Problematic simulation results were investigated both analytically and graphically, using dynamical systems methods (Abraham & Shaw, 1992, Devaney, 1989). The utilisation of reduced systems of return maps to investigate time-dependent behaviour of biological systems was inspired by the work of Chialvo & coworkers (Chialvo et al, 1990), while the use of simple linear methods for stability analysis of ODE systems followed Glass & Mackey (1988).

      To understand the performance of Houghton's attentional triad, use was made of :

      a. Algebraic determination of steady-state model solutions via setting the rate of change of all variables representing neuronal activities equal to zero.

      b. Determination of stability of solutions thus obtained by deriving linear approximations to the system equations and considering their evolution near the steady-states.

      c. A simplified map preserving key model properties was derived, thus obtaining a return-map, rather than differential-equation, model.

      d. Graphical analysis of the simplified map was performed to determine the characteristics of its steady states.

      Modelling Executive & Attentional function in Alzheimer's Disease - 1999 MSc Thesis - M. Moutoussis

      V. Results

      1. Models directly based on the Houghton attentional control theory


      A. Direct lateral inhibition (DLI) model

      This was used to examine the response of a lumped cortical area under the influence of an external signal, usually inhibitory, when an appropriate stimulus is presented. This mimics the response of Auditory areas to a brief tone, on a background of the constant interference due to Visual (tracking) processing. It was found that the baseline state of the secondary task unit was typically reduced by 50% of its maximum response under single task conditions. The maximum response itself was reduced by 20% only, while the difference in the timing of the response was very small and depended on the risetime of the stimulus (figures given here refer to the following set of parameters: Ext. stimulus I=0.75; W1=0.5; W+ = 0.3; W- = -0.3; D=0.4; Wi(Vis->Aud)=0.7; Wi(Aud->Vis)=0.35. The exact percentages depend on the parameters used, but their comparative relations don't: for example, the effect on the baseline is always stronger than that on maximum activation).

      As the onset of response to the Auditory signal was little affected by the presence of an inhibiting, concurrent task, it was decided, following Cohen et al (1992), to take the cumulative activation of a pathway rather than its risetime behaviour to correspond to behavioural reaction time.

      Model behaviour on stimulus offset was anomalous, units sometimes remaining 'switched on' after stimulus offset. This difficulty was also encountered in the much more complicated Houghton models of attention, as described in the introduction. Here further use of the model was limited to parameter ranges where stimulus offset is accompanied by decay of cortical activation. The matter is investigated analytically as follows.

      Following Houghton, we consider the limit where the contribution of inhibitory gain units is negligible, for example because some external cause has switched that unit off (the attending dyad approximation). The equations of the system are :

      da /dt = -D*a + ( 1 - a ) * W * a           } Rln. 2a
        p          p         p         on         }
      da  /dt = -D*a  + ( 1 - a  ) * W1 * [ a ]   } Rln. 2b
        on          on         on            p


      where a is a variable describing the state of each unit, D the time decay constant of the units and Wx is the weight of synapse x (as in fig. 4a). The index 'on' refers to the excitatory gain unit, while 'p' to the perceptual unit. [x] = 0 if x <= 0, while [x] = x if x > 0. For the equilibrium state, setting both Relns. 1 = 0 gives :

               +       2
             W1 * W - D
      a  = ----------------
       p                +
           W1 * ( D + W   )      } Rln. 3
      Stability analysis shows this solution to be stable if it is positive, which is guaranteed if

            +    2
      W1 * W  - D    > 0, } Rln. 4


      i.e. the synaptic weights around the positive feedback loop dominate over the decay constant. This explains why the units of the Houghton model, arranged in 'attentional triads', can get stuck in an activated state. It can be further proven that an attentional triad with inputs to each unit has single unique equilibrium solution if inhibitory drive is adequate to drive the activation of the perception unit negative and is potentially multistable for positive driving.

      A simplified return map model was constructed for the attending dyad (or triad) by observing that for da/dt = 0, each unit in the triad will have an activation governed by its steady-state input-output curve,

      a = I / (I + D) , } Rln. 5

      where I is any positive input. This curve is convex upwards. The construction of the map (fig. 7) confirms the analytical results of the ODE model and suggests that they can be overcome by the adoption of activation curves which at the point of zero input are concave upwards (fig. 8).

      Figure 7 a. We examine an 'attending' state where some signal has completely suppressed the Off-gain unit. Results would be qualitatively similar whenever there the On unit is more active than the Off. b. An input I arrives at time t=0 and excites the Property unit to a level p(0)=P(I). [The activation of the Property unit at time t is p(t) ; the input/output (i/o) function of the same unit for input i is P(i); similar notation for the On unit.] In the next time step t=1activity propagates to the On unit and produces an activation on(1)=On(p(0)) . c. The On unit i/o function On(i') is plotted 'on its side', with the origin of axis at the point (p,i)=(0,I) . This allows visualisation of how p(0) feeds into the On unit and produces on(1) , and in turn how on(1) adds to I and feeds into the Property unit, causing p(2)=P(I+on(1)). The process then repeats itself, p(2) producing on(3) etc. (This analysis can easily be extended to incorporate the Off unit). The activation of the two units is thus represented by a 'staircase' between the two curves P(i) and On(i') producing a so-called 'return map', a function giving the state of the system s(t) in terms of its state s(t-1). The system will come to rest at a point where the two curves cross. If we were to put the system there to start with, it would not move: these crossings are equilibrium points. If the 'staircase' leads towards such a point, the latter is an attractor (a point attractor); if away from it, a repellor. It can now be examined whether model properties correspond to real life. The resting state (the origin of axes) of this 'attending' system is a repellor: the perceptual units can be activated in the absence of stimulus !


      Figure 8: As in fig. 7, but a shallow gradient near the origin guarantees low output for low input. This in turn allows activation of the On unit to result in a true increase in gain of Property unit output.


      A given increase in the drive of an off-gain unit always has a larger impact on reducing perceptual unit activation than an equal increase in the drive of an on-gain unit, as indeed one might expect by inspection of the general state equation for a unit:

                                  +          -
      da/dt = -D*a + ( 1 - a ) * I - (1+a)* I } Rln. 6
      Where I+ and I- are the excitatory and inhibitory inputs respectively (both positive). Further, in a simplified triad with all synaptic weights equal to w, and input to the perceptual unit only, the ratio w/D limits the maximum value that the perceptual activation can attain. Increasing w/D reduces the equilibrium value attained by perceptual unit and makes its input - response curve flatter.


      In summary, the simplest models reveal parameter ranges that have to be avoided in more complex simulations. The DLI model is however sufficient to demonstrate how a very simple mechanism could explain cross-modality interference.

      B. Descending attentional control (DAC)

      This is shown in fig. 4b. Attention is mediated by modality specific top-down activation. This increases response in its own, 'attended', modality and reduces cross-modality activation. Thus this is the minimal model of the effect of attention in suppressing non-attended modalities. Top-down activation or inhibition again affects baseline activation more than response plateau. In an experiment, for example, where the visual task is ongoing, but the subject concentrates on detecting the auditory tone when this is to arrive, the baseline visual state is suppressed by about 80% of the maximum it then attained when the visual task was under way (from 0 to -.35 in arbitrary activation units; visual task response was +0.42 ). The auditory baseline activation increases instead by 38% of its task activation level (from 0 to +0.22; auditory task response +0.58). The precise figures depend on the parameters used, but if chosen not to fulfil Rln. 4 - i.e. not to have any spurious stable states - the pattern of results remains robust. Note that this includes a greater activation, in absolute terms, for the attended modality compared to the non-attended one, but a lesser activation (lesser gain) if the baseline state is taken into account (fig. 9).

      Thus the DAC model simulates the normal pattern of cross-modality interference, and has some success in simulating the qualitative pattern of increased performance for an attended modality. However, as there are no ascending connections the cross-modality effects are independent of stimulus processing and thus the model does not simulate the Baddeley experiments.

      Figure 9: Simulation of descending attentional control. Panels a & b show perceptual unit activation, c & d the input to the respective modalities. Vertical axes are calibrated so that peak and trough values can be read off the graphs, rather than compared visually. This format is retained in all figures. A short auditory tone is presented during a prolonged visual task. Note the effect of attention on baseline activation.


      C. Descending and ascending 'match' attentional system (MAS)

      This model (fig 4c) affects lateral inhibition indirectly, and such inhibition is shown to be dependent both on descending (attentional) activation of the cross-modality perceptual unit and on the presence of the cross-modality stimulus. This is the minimal model that simulates features of the dual-task performance in normal people.

      The basic function of the model is shown in fig. 10. In the first part of this figure (a) both stimuli of the dual task are presented, but only one is attended. This is achieved by setting the 'target activation' of the auditory but not the visual pathways to a high value. It is evident that attention changes baseline activation even in the absence of stimuli (cf. behaviour near time = 50). Successful properties of the system include first, peak activation of the attended system being higher than of the non-attended one (0.51 vs. 0.48 in this example: a small difference); and second, a cross-modality effect evident by a drop in visual activation by about 15% of its peak value during the time when both stimuli are present. Fig. 11b shows model behaviour under true dual task conditions, i.e. when attentional activation is present for both modalities. As it should, the model shows greater activation of the visual modality than in fig 10a, and lesser activation of the auditory modality. If we take cumulative activation to correspond to performance, the reduction in area-under-the-curve between the two figures (shaded) is 17%, consistent with experiment in order of magnitude terms. No attempt was made here to simulate the fact that the primary task affects the secondary more than vice versa in most of Baddeley's experiments - this would be straightforward.

      Figure 10: Dual-task model relying on 'matching', for normal subjects. In this case however only the auditory modality is attended (only the auditory match unit receives top-down activation). The shaded area is taken to be a measure of performance in the task. However due to the shape of the response this is approximately proportional to the plateau value.
      Figure 11: Dual-task model with same structural parameters as in fig. 10, but now both modalities are attended (both match units receive top-down activation).
      The relative effect of dual task performance in AD can be successfully simulated by this system, through making the match units less efficient. This is simulated here by reducing the top-down activations, (T1 and T2 in fig. 4c). A relatively successful simulation example is shown in figs 12 and 13. Here Normals have better performance than AD in any task, Dual task has worse performance than Single for any group, and the relative drop in performance during dual task is greater in the AD than in the Normals. Further reduction in the drive to the 'match' units, T1 and T2, decreases dual task performance more. Some differences are however very small, unlike Baddeley's experiments; and reduction in T1/T2 soon ceases to further reduce dual task performance, the relative performance bottoming out at about 0.74. Furthermore, this general qualitative congruity with experiment is only present for fairly specific parameter ranges and is not part of the structure of the model as such. Even then, quantitative match is not achieved, AD -Normal activation differences being in general too small to account for experimental results. Indeed, detailed parameter exploration shows that it is more common for anomalous results to appear (e.g. AD increasing perceptual unit activation or reducing dual task related drop in performance). If the MAS is to be used as basis for further dual task simulations development must be limited to parameter ranges that do not exhibit such anomalies. These include low stimulus amplitudes and a high ratio of synaptic weights of the own-modality over the cross-modality projections.


      2. Direct lateral inhibition plus desceding/ascending match model

      The simplest alteration of the basic MAS model that will provide a robust effect of increasing the dual task effect when the efficiency of the matching mechanism is impaired is to combine the DLI and MAS systems. Simulation of this system allows quantitative match with the Baddeley experiments over a large range of parameter values.

      Figure 12: Simulation of the performance of normal subjects under dual task conditions. The parameters in this and the next figure have been selected to optimise accordance with experimental results. Note, for example, the lesser amplitude of the input stimuli.
      Figure 13: Simulation of performance of AD subjects. This is achieved by decrease in the performance of the match mechanism, here due to reduced top-down stimulation. Other parameters are identical to fig. 12. Detailed comparison of output levels with fig. 12 shows qualitative agreement with the Baddeley results.


      3. Revised model - preliminary results

      A. Alternative neural unit equations

      Simulations were performed while substituting the Grossberg equations on which the original Houghton model is based with the Freeman 'KI' equations. The DAC model was implemented using the KI equations, and it was confirmed by simulation that activation of the on-gain unit by positive attentional input here not only increases activation on presentation of a stimulus, but also the difference between baseline and response activations, thus truly increasing the gain of the triad. A second result is although high synaptic weight products around the positive feedback loop can still result in spuriously activated states, the system is not as sensitive to this effect, again as predicted by graphical analysis (fig 8). Secondly, activation of attentional triads made of KI units can easily be made to oscillate, consistent with physiological studies. Finally, KI units respond much more slowly to step inputs than the original Grossberg units. They may thus lend themselves more readily to investigation of time-dependent phenomena such as reaction times.

      As an example, the set of W1 = W+ = - W- = 0.7, T1 = 0.2 T2 = 0 produces a baseline activation of 0.17, increasing to 0.83 on stimulus presentation. When T1 = T2 = 0, baseline activation is 0 and response to stimulus 0.5. In this example the increase in gain is about 30%.

      B. Alternative matching mechanism

      Here the criterion for a successful 'match' is the synchronised activation of `perceptual / object field' and `association / match' areas, rather than just the level of activation of these areas. In the normal case the models shows synchronisation dependent on first, the presence of a stimulus and second, the presence of descending ('target') activation (data not shown). In the case of dual task synchronisation is again achieved, but not as smoothly (fig. 14); for high levels of interference the speed with which it is achieved varies with the precise characteristics of the interfering noise within each trial. Also achieved is the establishment of a cooperative oscillation with increase in the overall level of activity of the cooperating areas much as in the models above.

      In the case of AD and in the absence of interfering activity (single task condition), synchronisation appears to be achieved more slowly and the cooperative activity has lesser magnitude, consistent with a decreased performance (fig. 15). This was suggested by preliminary exploration of the model. The introduction of interference (dual task) in the case of AD can disrupt synchronisation gravely or even abolish it completely (consistent with 'missed responses' in Baddeley's experiments) . This model therefore appears to have the potential to simulate the progression of AD from a prolongation of reaction times to complete task failure.  Full exploration of this promising model has been carried out. Please contact me for details

      Figure 14: Simulation of dual task interference for normal controls (Revised model) a., b. Response of the coupled cortical areas of fig. 6b,c to a stimulus presented at time t=20 and withdrawn at t=60. The 'match' units are subject to top-down, 'attentional' activation. Note that during this period an oscillation of gradually increasing magnitude is produced cooperatively between the two areas. c. Noise represents cross-modality coupling (therefore interference) d. Estimate of inter-areal synchronisation. This is shown for clarity for the period of stimulus presentation only. The timing (in units of phase angle, rad) of each peak-to-peak wave of the two areas is compared. The zero, flat baseline describes absolute synchronisation and is drawn for purposes of demonstration only - it is not calculated from oscillations outside the stimulus epoch ! The graph shows that at the start of stimulus the association / match area lags behind by about 1.9 rad (a random value) but quickly converges to a near-zero (synchronised) phase value. The irregular path to convergence is because of the noise.

      Figure 15: Simulation of AD during single task performance, showing a. the response of the association area and b. synchronisation (cf. fig 14b & d). AD is simulated by a reduction in the inter-area coupling Kxx (cf. fig 6c). A cooperative oscillation is again produced, but is of smaller amplitude. There is no interfering modality, so the system smoothly but slowly converges to a near-zero phase value.

      VI. Discussion

      1. Rationale of the present study


      This study is novel in bringing together rigorous neuropsychology, current theories of attentional control and computer modelling to bear upon the study of Alzheimer's disease. Although models of dysmnesia have been developed in AD, models of executive function have not. Rigorous neuropsychological experiments are seldom used as a basis for modelling in AD.

      Even more novel and important is the use of dynamical systems theory to bridge the informational and biological domains. This is crucial as neural activity shows complex patterns, including oscillations, whose emergence is intimately linked to the brain's information processing ability. On the other hand, even simple psychological models have important, unexplored dynamical properties.

      The present approach does not apply simply the minimal modifications that would fix model shortcomings, without reference to the biological substrate. Lack of such reference can be likened to trying to understand how a hummingbird flies by building helicopters: both might be able to hover, but the biological implementation may be fundamentally different to the engineering one. Models are better developed by examining hitherto neglected biological data while recognising that the relevant biology may be as yet unknown. Another objection to the 'minimal modification' approach is that it is Kuhn's 'normal science' par excellence (Notturno 1984), designed to avoid fundamental issues.

      2. Critique of methods

      'Executive dysfunction' in the dual task may not have the same origin or importance as the impairment of ADL related functions. Hence it may not be the most relevant starting point. The underlying attentional theory of Houghton et al is not the only possible starting point for modelling either. The information processing theory of EPIC (Meyer & Kieras, 1997) could be a different one; The methodology of Cohen et al (1992) yet another, particularly as a lot of work has examined the Stroop task in AD. The EPIC model was not used as it has very coarse graining of the biological processes involved. It involves however a detailed consideration of cognitive strategy, which has been neglected in our study. The Baddeley experiments did not consider differences in cognitive strategy and thus would be difficult to reconcile with EPIC. It would not be surprising if AD patients had difficulties inventing efficient strategies, and these would have to be considered in the modelling of more complex tasks. This is relevant to the conjecture that better strategies help the more educated preserve function in the face of AD.

      Baddeley's papers provide little detail into the particular ways in which performance deteriorated under different conditions. They do not, for example, report false-positive responses or evidence of strategy failure. Such information could lead to more detailed modelling. Access to the original data might help. In addition the conclusion of an 'Executive lesion' in AD reached by Baddeley and assumed in this study is not the only possible one. An alternative could be deficient automatization. Rather than AD impairing executive control it could impair the process of automatisation (Cohen et al, 1992), so that AD subjects find dual tasks harder because it is harder to combine non-automatised tasks.

      Modelling studies make many assumptions, which can be both a weakness and a strength. Unproved assumptions used in the Houghton derived models which could reduce the validity of our conclusions include that: 1. Performance is directly related to level of activation of cortical areas 2. The most relevant cortical areas are perceptual; 3. Diffuse influences (e.g. ACh) can be neglected; 4. Cortices can be treated in a 'lumped' manner; 5. Neural noise can be neglected; 6. The way in which 'match' unit performance is impaired is immaterial; 7. Cross-modality interference can be assumed to take place via descending projections of the attentional mechanism 8. Parameter exploration was adequate. The related strength of the approach is that such assumptions are inherent in much thinking about attentional control, only they remain hidden. On the other hand in this study they serve as incentives for meaningful exploration. The manner in which some of these assumptions were dealt with has been described in Methods. With respect to assumption 2., simulation of variables more directly related to behavioural performance would be highly desirable. Assumption 7 is of particular concern. It is adopted on the grounds of consistency with imaging studies showing suppression of non-attended modalities, of it providing a task-specific mechanism of attentional control and of it being a priori unbiased with respect to the study hypothesis. These assumptions will be discussed further.

      3. Interpretation of findings

      A. Models directly based on Houghton's theory

      The 'attentional triad' proved a useful model. It is susceptible to attentional modulation, easily allowing the implementation of excitatory and inhibitory attentional control, including lateral inhibition and the match mechanism. It was, however, found to have important shortcomings. First, it can show spuriously excited states. Second, it is a lot more responsive to inhibitory rather than excitatory modulation. Third, the 'gain' units alter the level of activation but do not, in fact, change gain itself. These problems were traced (fig. 7) to the steady-state input-output function of the specific units involved. This curve has its maximum sensitivity at the resting state. The use of excitation functions which are concave upwards at the origin can solve these problems (fig. 8; section 3A of Results). This is an illustration of why knowledge of dynamical properties of simple unit combinations can be important.

      In addition, the triad involves no conduction delays. More realistic, KI-like units improve the points raised above, have physiological support and also address the issue of time dependence in a more substantial way. They provide a relatively well-explored avenue to the simulation of oscillatory cortical dynamics. Objections can be raised: The KI equations have been derived from archicortical rather than neocortical physiology; and the timescales on which KI units operate do not relate to behavioural reaction times (their response is still too fast). Therefore an indirect index of their response would still have to be used to infer behavioural performance.

      The limitations of the 'attentional triad' caused problems in dual-task simulations. If cross-modality inhibition is used, then performance of each task has to overcome inhibition by the other. This is difficult to achieve if excitatory control is inherently weaker than inhibitory. It can result in attention paradoxically decreasing performance during the dual task and in damage to the attentional system dis-inhibiting perceptual units more than de-exciting them. The result is a tendency for the AD condition to perform relatively better in the dual task than normals, except within a limited parameter range. This includes: 1. low stimulus intensities, which essentially linearise perceptual unit response; 2. within-pathway, excitatory synapses from match units to on-gain units much (e.g. four times) stronger than the cross-modality, inhibitory ones. It can be seen that both these properties serve to offset the tendency of the original ('Grossberg') units to saturate and to favour inhibitory inputs. These considerations also explain why even successful models showed quantitatively small effects.

      Another drawback of the initial models is that progressive impairment of the 'match' mechanism does not lead to the profound effects of advancing AD; rather, impairment shows a floor effect. This can be understood if the positive feedback loop formed by the perceptual-match connections is considered. The participation of Grossberg-type units necessitates a low gain around the loop to avoid spurious states. An improved high-gain loop (as in fig. 8) would be more suitable for the simulation of the profoundly deleterious effect of AD.

      On the other hand, the Houghton-based model with suitable qualifications succeeded in simulating qualitatively the overall pattern of the Baddeley results. It seems that the essential features of this model include: 1. the matching process 2. the presence of an active description of behavioural targets and 3. change of responsiveness of perceptual units towards stimuli through both inhibitory (decreasing responsiveness) and excitatory (increasing it) attentional control. Shortcomings of the original models include use of the Grossberg units and the symbolic implementation of the match / mismatch mechanism. Features that do not keep with cortical physiology include the zero transmission delays and system operation via point attractors.

      B. The success of lateral inhibition : Interpretation and novel predictions of the augmented model.

      The combination of direct lateral inhibition and match-based attentional control provided a strikingly robust model, simulating a number of results in a quantitative manner. This is because it involves excitatory attentional control based at the level of the match mechanism, with cross-modality inhibition at a lower level, less prone to the ravages of AD. This model leads to some novel predictions that are physiologically and psychologically testable. Cross-modality inhibition is independent of attention for a given sensory cortical activation. As in the Baddeley experiments it takes place between procedurally unrelated tasks it is likely to operate in a general, non-specific manner: different modalities laterally inhibit each other by default. Such laterally inhibitory effects would be equivalent in AD and normals as long as subjects attended to neither of the interfering modalities, performing instead some third, neutral task. Such effects should be visible in human imaging activation studies as well as in invasive animal experiments.

      What could be the biological function of cross-modality interference effects observed in the normal elderly, and more specifically of lateral inhibition? Cohen and co-workers reach the conclusion in their discussion of automatic task performance that tasks should interfere only to the extent that they utilise overlapping brain modules, as opposed to some ill defined 'attentional resource'. Similar principles underlie the dual task performance limitations of the conceptually different EPIC (Meyer & Kieran, 1997). However, Baddeley's experiments specifically minimise overlap between resources required to perform the constituent tasks. Only then does the finding of interference between the tasks, particularly between response-to-tones and tracking, lend support to the theory of a 'central executive'. This is considered as a shared resource, the homuncular manager of a limited attentional 'budget'. There is, however, poor support for such a shared resource.

      Cross-interference, so far modelled by direct lateral inhibition, could happen for a number of reasons. First, the 'central executive' could be a brain processor which can only deal in terms of serial procedures. The interference between increasingly more demanding tasks would arise because the only way of multitasking this serial processor is timesharing - and high performance biological serial computation has not yet evolved. This is broadly consistent with the analysis of H.A. Simon (1995), who claims that human thought is largely serial because parallel processing becomes inefficient in a general purpose reasoning device. Second, it may be that the organism doesn't know a priori that two tasks won't clash. The default is mutual inhibition, as per the direct lateral inhibition paradigm. Still, this isn't a good explanation for an inhibitory setup. The cross-inhibitory 'setup' might be an example of a synergy phenomenon. Synergies are ensembles of large numbers of biological components (e.g. muscles, sensory organs, nerves, central circuits) which, during a task, are coordinated and behave as a whole. As these are all functionally bound together they lack freedom to behave relative to each other except as prescribed by the pattern of coordination (Bernstein 1967, as quoted in Kelso, 1995; Kelso et al, 1984). It may be that the ability for coordination is a ubiquitous characteristic of behaving organisms. This ability is achieved through couplings between all system levels and components. In the presence of such couplings random relative component activity (as is forced by the random relative timing of the two tasks in the dual tasks paradigm) runs contrary to the general propensity for coordination. Contrary to the dual task paradigm, organisms are good at multi-task performance (e.g. walking, breathing and talking) if constraints of random relative timing are lifted. This may be important in AD, where walking while talking becomes difficult. Failure in this routine dual task may contribute to some dangerous falls (Camicioli et al, 1997). In fact many behaviours can be considered to involve multiple tasks having arbitrary but non-random relations to each other. Cross-modality inhibition could be not necessarily inhibition per se, but a vestige of cross-modality propensity for coordination. Again, however, impaired dual task performance is just a side effect.

      Finally the lateral inhibition may have another function. In normal environments animals utilise multi-sensory inputs. It would be disastrous for mice to be impaired from hearing cats simply by looking for them. On the contrary, cross-modality control could highlight in the 'interfered' modality features of survival value in a context defined by the 'primary' modality, while inhibiting known distractors. Irrelevant cross-modality stimuli would be neither highlighted nor inhibited.

      Thus several questions arise: What dual tasks show cooperative, and what neutral, effects in normals ? and what happens in these tasks in AD ?

      4. Next step models

      It is important to run the whole series of simulations in this study, and particularly the MAS ones, by substituting units most compatible with cortical physiology for the Grossberg ones. These in the first instance should be KI sets. It is also important to simulate output processes including the 'binding' between perception and response selection. This would allow direct comparisons with experimental results such as reaction times and recall errors. It may be possible to simulate the digit-span experiments of Baddeley by using the successful models of Burgess & Hitch (1996) for short term memory for serial order (fig. 17).

      Figure 16. Combination of attentional control model with one that simulates short-term memory (STM) for digits. Detailed description of the latter is beyond the scope of the present discussion, with can be found in such as Hartely & Houghton, 1996. The model relies on retrieval of memorised items from a long-term memory store (LTM) through learned connections with 'context' units. Attentional control could enhance activation of both types of unit.


      5. Reflections in the light of other findings

      As the discussion of the possible roles and biological substrates for cross-modality inhibition has shown, not only is it important to consider model development but also fundamental assumptions, while retaining successful general features of the present theory. This can be done by considering what ways of implementing these successful features would be most compatible with the current understanding of the neurophysiology of AD. Successful matching in the Houghton models involves an increase in activation of both the 'match / mismatch' and the perceptual neurons. While both sensory and frontal cortices are thought to be activated by attention and by concurrent tasks, it is not clear how the pattern of activation changes in AD.

      Some important studies (Leuchter et al, 1992; Schreiter-Gasser et al, 1993) largely gave rise to the hypothesis that intercortical functional connectivity is especially impaired in AD and that it may account for 'dysexecutive' neuropsychological deficits. These studies influenced our modelling of AD as reducing the influence of the 'match' on the perceptual units. These studies however measured levels of EEG synchronisation of cortical oscillations, rather than levels of activation, between different cortical areas. It may therefore be that the matching process depends on such oscillatory processing.

      Three further lines of evidence, support this alternative view of matching. First, animal data suggest that neural structures closer to the sensory receptors receive bias, thus favouring some responses over others, through descending oscillatory signals (Kay et al, 1996; Sillito et al, 1994). This is in addition to the oscillatory signals of primary sensory cortex found during percept recognition (Gray, 1994). Second, during visuomotor tasks human EEG shows that successful trials are accompanied by increased synchronisation between different but relevant brain areas. Gevins & Cutillo (1995) describe increased evoked potential covariance between left prefrontal cortex and the relevant motor and parietal cortices preceding successful responses. Third, Bressler and coworkers (1993) showed how attention-demanding tasks involve intermittent synchronisation of oscillatory activities among multiple brain areas in the monkey. Synchronisation takes place very robustly, and in discrete time frames, over cortical areas of obvious relevance. However the findings are difficult to interpret because synchronisation epochs do not map to information processing stages in an obvious way (unlike the work of Kay et al in the olfactory system). Synchronisation may indicate 'consensual resolution of processing', whereby synchronous activity in two or more areas allows amplification of the signal transmitted to common targets that they project to (Bressler, 1995).

      Thus matching may involve synchronisation and AD could disrupt it through functional disconnection, as demonstrated by the EEG studies. This could explain attentional impairment in a general way. Cross-modality interference need not become attenuated: Leuchter and coworkers (1992) clearly showed that coherence of oscillatory activity between areas connected by broad, complex networks is preserved. Most interestingly, imaging studies (Becker et al, 1996) have shown that AD subjects activate cortical areas according to task demands more diffusely than controls, activation 'spilling' into neighbouring areas. Synaptic compensatory changes that account for the excess of false-positive events during memory tasks in AD (Ruppin & Reggia, 1995) could also account for this 'spill-over' effect. With respect to dual task performance, such 'spill-over' would be consistent with a preserved or enhanced cross-task interference.

      In summary, a more satisfactory model of cross-modality interference could include the physiological feature of oscillatory synchronisation and, in AD, an impairment of the balance between long-range functional connectivity and more local interference. This is in addition to the target units, the matching mechanism based on recurrent connections and the perceptual mechanism modulated by attention central to the successful psychological models.

      6. New directions for further research

      A. Structure of a revised model

      The overall structure of the models used in this study (fig. 3) can be retained in incorporating the above improvements. However, the cross-modality inhibitory attentional projections are now omitted and local (between areas of the same level) interference is postulated. The 'match' units are again identified with association cortices. Each lumped cortical area is now capable of oscillatory activity. Activation of motor schemata is through 'consensual resolution of processing'. A diagram of the revised model is shown in fig 6a

      The pattern of the Baddeley results permits however drastic simplifications to be made. In both all subjects the influence of the primary on the secondary task was much greater than vice versa. We can therefore ignore the latter influence. Also ignoring motor schema binding gives a preliminary model of only two coupled oscillators. This bare bones model is shown in fig. 6b. Here the influence of the primary task is through a non-specific broad band signal.

      B. Evaluation of the revised model

      Simulations showed that the model is successful in simulating the overall pattern of experimental results. Attentional activation improves its performance (indeed for certain ranges of parametres attention is necessary for model response). Simulation of AD by functional dysconnection of modules gradually impairs performance, disproportionately so in the presence of an interfering task (This is a summary of preliminary results. Please email me for  fuller info). In the normal case the presence of interference does not greatly reduce performance.

       If information processing in the real brain involves a series of intermittent synchronisations like the one successfully simulated here, the demonstrated delays in synchronisation might be possible to directly link with prolongation of reaction time. The problem here is that the broad band nature of cortical oscillations does not clearly reveal a timescale with which the period of oscillation of the simulated areas can be identified.

      The revised model is still inadequate in simulating physiology. Possible important features that are omitted are the explicitly episodic nature of brain synchronisation; the detailed description of the oscillators used; and most importantly, all structure of information processing that the real episodic synchronisations reflect. Another omitted feature is the non-negligible transmission delays of real intercortical pathways. The model however captures enough of the physiology to support the hypothesis in a preliminary manner.

      C. Education & Alzheimer's disease

      Many aspects of AD related to executive function particularly lend themselves to modelling. A most important example has to do with the possible effect of lack of education, an independent risk factor for the development of AD (Orrell & Sahakian, 1995). Functional imaging indicates greater deterioration for a given degree of cognitive impairment in the brain areas subserving attentional and executive functions of the better educated (Alexander et al, 1997). This supports the hypothesis that better education is associated with greater 'cognitive reserve'. The biological and psychological substrates of the protective effect of education are however little understood and could in the future be explored according to our paradigm. This might be of preventative value.

      7. Summary & Conclusion

      In Alzheimer's dementia research has recently explored not only the memory but also the so called 'dysexecutive' deficits (Baddeley et al, 1991). The latter are thought to reflect difficulties in the coordination of daily activities that compromise the independence of patients with AD at least as much as memory deficits. The understanding of these executive deficits is thus of great importance. Baddeley and co-workers (1991) postulated a Central Executive System (CES) coordinating attention. Using the dual-task paradigm they showed that the CES is particularly affected in DAT, accounting for the severe attentional /executive deficits (Baddeley et al, 1991). These experiments provided the necessary data to constrain the development of rigorous models of executive control of attention.

      Houghton and coworkers (Houghton & Tipper, 1994) have simulated successfully many aspects of selective allocation of attention. We formulated the hypothesis that "an application of the model of attentional control of Houghton et al can account for the pattern of deficit observed by Baddeley in AD patients during the performance of dual tasks". We aimed further to improve the model heuristically but also on the basis of current neuroscientific findings.

      Two attentional control systems, one for each dual task modality, were combined (fig. 3). The two systems were linked by cross modality inhibitory attentional control (Houghton & Tipper, 1994). Damage due to AD was modelled as an impairment of the influence of the attentional ('match') module on perceptual areas, guided by the neuropathological finding that in early AD association areas and corresponding long range projections are damaged (Morris, 1994).

      It was found that within a small parameter range the model could reproduce qualitatively the general pattern of experimental results. However it had a tendency to show spuriously activated states, lacked robustness, had difficulty reproducing results in a quantitative way and failed to simulate the progressive deterioration of executive function in AD. Dynamical analysis showed that the units used to simulate individual cortical areas in the original models were largely to blame for these failures. The model could be dramatically improved by the introduction of lateral inhibition between sensory areas, postulated to be largely unaffected by AD. Preliminary studies showed than association function based on synchronisation of oscillations between different brain areas, known to deteriorate in AD, could also account for Baddeley's results.

      The study has contributed to the understanding of psychological mechanisms of executive function particularly as involved in AD. It has demonstrated the importance of dynamical understanding of psychological models. It brought together rigorous neuropsychology and computer modelling, something not previously attempted in this field but essential for the linking of the pathology with the clinical features of AD. It has also indicated directions for future research, mainly the investigation of the 'matching' function in the context of oscillatory cortical dynamics and the disruption of intercortical coordination of neuroactivity in AD.

      VII. Acknowledgements

      This thesis would have been impossible without the help of Drs. Martin Orrell and George Houghton. I would further like to thank Dr. Ann Moutoussi and Dr. David Frost for precious glimpses into their world-views, and the staff of the Medical Education Centre, Whipp's Cross Hospital, Leytonstone for their help with literature searching and provision of reference papers.


      Modelling Executive & Attentional function in Alzheimer's Disease - 1999 MSc Thesis - M. Moutoussis

      VIII. References

      1. Abraham, R.H. & Shaw, C.D. (1992) Dynamics - the geometry of behavior (2nd Edition) Addison-Wesley, Redwood City (CA).
      2. Anonymous (1991) Turbo C++ (2nd Edition). Borland International, Scotts Valley (CA).
      3. Armony, L.J., Servan-Schreiber, D., Cohen, J.D., et al (1995) An Anatomically Constrained Neural Network Model of Fear Conditioning. Behavioral Neuroscience, 109, 246-257.
      4. Baddeley, A.D., Bressi, S., Della Salla, S., et al (1991), The decline of Working Memory in Alzheimer's disease - a longitudinal study. Brain, 114, 2521-2542.
      5. Baddeley, A.D. (1996), The concept of working memory. In Models of Short Term Memory (Ed. Gathercole, S.E.), Psychology Press.
      6. Becker, J.T., Lopez, O.L. and Butters, M.A. (1996) Episodic memory: differential patterns of breakdown. In The cognitive neuropsychology of Alzheimer-type dementia, ( Ed. Morris R.G.), Oxford University Press, Oxford.
      7. Bernstein, N.A. (1967) The coordination and regulation of movements. Pergamon Press, London.
      8. Binetti, G., Magni, E., Padovani, A. et al (1996) Executive dysfunction in early Alzheimer's disease. Journal of Neurology, Neurosurgery & Psychiatry. 60, 91-93.
      9. Bressler, S.L. (1995) Large scale cortical networks and cognition. Brain Research Reviews, 20, 288-304.
      10. Bressler, S.L. (1996) Interareal synchronization in the visual cortex. Behavioural & Brain Research, 76, 37-49.
      11. Bressler, S.L., Coppola, R. & Nakamura, R (1993) Episodic multiregional coherence at multiple frequencies during visual task performance. Nature, 366, 153-156.
      12. Burgess, N. & Hitch, J.H. (1996) A connectionist model of STM for serial order. In Models of Short-term Memory (ed Gathercole, S.E.). Psychology Press.
      13. Buzzi-Ferraris, G. (1993) Scientific C++ - Building Numerical Libraries the Objected-Oriented Way. Addison - Wesley, Wokingham.
      14. Camicioli, R., Howieson, D., Lehman, S., & Kaye J. (1997) Talking while walking: the effect of a dual task in aging and Alzheimer's disease. Neurology, 48, 955-958.
      15. Carrie, J.R.G., (1993) Evaluation of a neural network model of amnesia in diffuse cerebral atrophy. British Journal of Psychiatry, 163, 217-222.
      16. Chialvo, D.R., Michaels, D.C. & Jalife, J. (1990) Supernormal excitability as a mechanism of chaotic dynamics of activation in cardiac Purkinje fibers. Circulation Research, 66, 525 - 545.
      17. Cohen, J.D. & Servan-Schreiber, D. (1992) Context, Cortex and Dopamine: A Connectionist Approach to Behavior and Biology in Schizophrenia. Psychological Review, 99, 45-77.
      18. Cohen, J.D., Servan-Schreiber, D. & McClelland, J.L. (1992) A parallel distributed processing approach to automaticity. American Journal of Psychology, 105, 239-269
      19. Devaney, R.L. (1989) An introduction to chaotic dynamical systems (2nd Edition). Addison-Wesley, Redwood City (CA).
      20. Drevets, W.C., Burton, H., Videen, T.O. et al (1995) Blood flow changes in human somatosensory cortex during anticipated stimulation. Nature. 373, 249-252.
      21. Eeckman, F.H. & Freeman, W.J. (1991) Asymmetric sigmoid non-linearity in the rat olfactory system. Brain Research, 557, 13-21.
      22. Elliott, R., McKenna, P.J., Robbins, T.W. et al (1995) Neuropsychological evidence for frontostriatal dysfunction in schizophrenia. Psychological Medicine, 25, 619-630.
      23. Fiez, J.A., Raife, E.A., Balota, D.A. et al (1996) A positron emission tomography study of the short-term maintenance of verbal information. Journal of Neuroscience, 16, 808-822.
      24. Freeman, C. & Tyrer, P., (1992) Research Methods in Psychiatry : A Beginner's Guide. The Royal College of Psychiatrists, Gaskell.
      25. Freeman, W.J. (1975) Mass action in the nervous system. Academic Press, New York.
      26. Freeman, W.J. (1991) The physiology of perception. Scientific American, 264, 78-85.
      27. Freeman, W.J. (1992) Tutorial in neurobiology: from single neurons to brain chaos. International Journal of Bifurcation and Chaos, 2, 452-481.
      28. Freeman, W.J., Yao, Y. & Burke, B. (1988) Central pattern generating and recognizing in olfactory bulb: A correlation learning rule. Neural Networks, 1, 277-288.
      29. Forstl H., Sahakian B., (1993), Thalamic radiodensity and cognitive performance in mild and moderate dementia of the Alzheimer type. Journal of Psychiatry & Neuroscience, 18, 33-37.
      30. Gevins, A.S. & Cutillo, B.A. (1995) Neuroelectric measures of mind. In Neocortical dynamics and human EEG Rhythms (Nunez, P.L., Ed.) . Oxford University Press, New York.
      31. Glass, L, & Mackey, M.C. (1988) From clocks to chaos. Princeton University Press, Princeton.
      32. Grasby PM, Frith CD, Paulesu E, Friston KJ, Frackowiak RS and Dolan RJ (1995) : The effect of the muscarinic antagonist scopolamine on regional cerebral blood flow during the performance of a memory task.. Experimental Brain Research 104, 337-48.
      33. Gray, C.M., (1994) Synchronous Oscillations in Neuronal Systems : Mechanisms and Functions. Journal of Computational Neuroscience, 1, 11-38.
      34. Grajski, K.A. & Freeman, W.J. (1989) Spatial EEG correlates of nonassociative and associative olfactory learning in rabbits. Behavioral Neuroscience, 103, 790-804.
      35. Hartley, T. & Houghton, G. (1996) A linguistically constrained model of short term memory for non-words. Journal of Memory and Language, 35, 1-31.
      36. Hasselmo, M.E. & Bower, J.M. (1993) Acetylcholine and memory. Trends in the Neurosciences, 16, 218-222.
      37. Hasselmo, M.E. (1994) Runaway synaptic modification in models of cortex: Implications for Alzheimer's disease. Neural Networks, 7, 13-40
      38. Hinton, G.E., Plaut D.C., & Shallice T. (1993) , Simulating Brain Damage. Scientific American, 269, 58-65.
      39. Hoffman, R.E., & McGlashan, T.H., (1997) Synaptic Elimination, Neurodevelopment, and the Mechanism of Hallucinated "Voices" in Schizophrenia. American Journal of Psychiatry,154, 1683-1689
      40. Houghton, G., & Hartley, T., (1995), Parallel Models of Serial Behaviour: Lashley Revisited. Psyche (Electronic Journal),
      41. Houghton, G., & Tipper, S.P. (1994) A Model of Inhibitory Mechanisms in Selective Attention. In Inhibitory Processes in Attention, Memory and Language, Academic Press.
      42. Jeffery, K.J. & Reid, I.C, (1997) Modifiable neuronal connections: an overview for psychiatrists. American Journal of Psychiatry, 154,156-164.
      43. Kay L.M.., Lancaster L.R. & Freeman, W.J. (1996) Reafference and attractors in the olfactory system during odor recognition. International Journal of Neural Systems, 7, 489-495.
      44. Kelso, J.A., (1995) Dynamic Patterns : The self-organisation of brain and behavior. Cambridge, Massachusetts : MIT Press.
      45. Kelso, J.A., Tuller, B., Vatikiotis-Bateson, E. & Fowler, C.A. (1984) Functionally specific articulatory cooperation following jaw perturbations during speech: Evidence for coordinative structures. Journal of Experimental Psychology: Human Perception and Performance, 10, 812-832.
      46. Kelso, J.A. & Fuchs, A. (1995) Self-organizing dynamics of the human brain: Critical instabilities and Sil'nikov chaos. Chaos, 5, 65-69.
      47. Lawrence, A.D., & Sahakian, B.J., (1995) Alzheimer disease, attention, and the cholinergic system. Alzheimer Disease & Associated Disorders, 9 Suppl. 2, 43-49.
      48. Leuchter, A.F., Newton, T.F., Cook, I.A. et al, (1992) Changes in brain functional connectivity in Alzheimer-type and multi-infarct dementia. Brain, 115, 1543-1561.
      49. Levine, D.S., Parks, R.W, Prueitt, P.S. (1993) Methodological and theoretical issues in neural network models of frontal cognitive functions. International Journal of Neuroscience, 72, 209-233.
      50. Meyer, D.E. & Kieras, D.E. (1997) A computational theory of executive cognitive processes and multiple-task performance: Part 1. Basic mechanisms. Psychological Review, 104, 3-65.
      51. Morris, R.G. (1994) Recent developments in the neuropsychology of dementia. International Review of Psychiatry, 7, 85-107.
      52. Myers, C.E. & Gluck, M.A. (1994) Context, conditioning, and hippocampal representation in animal learning. Behavioral Neuroscience, 108, 835-847.
      53. Nicolis, J.S., (1991) Chaos and Information Processing : A Heuristic Outline. Singapore : World Scientific.
      54. Nicolis, G., (1989) Physics of far-from-equilibrium systems and self-organisation. In The New Physics (Ed P. Davies), Cambridge University Press, Cambridge.
      55. Notturno, M.A. (1984) The Popper/Kuhn debate: truth and two faces of relativism. Psychological Medicine, 14, 273-289.
      56. Orrell, M. & Sahakian, B. (1995) Education and dementia. British Medical Journal, 310, 951-952.
      57. Panksepp, J. (1998) Affective Neuroscience: The foundations of human and animal emotions. Oxford University Press, New York.
      58. Paulesu, E., Frith, C.D. & Frackowiak, R.S., (1993) The neural correlates of the verbal component of working memory. Nature, 362, 342-345.
      59. Pearson RCA, Esiri MM, Hiorns RW, Wilock GK, Powell TPS (1985) Anatomical correlates of the distribution of the pathological changes in the neocortex in Alzheimer disease. Proceedings of the National Academy of Sciences of the USA, 82,4531-4534.
      60. Perry, R.J. & Hodges, J.R. (1999) Attention and executive deficits in Alzheimer's disease: a critical review. Brain, 122, 383-404.
      61. Press, W.H., Rlannery, B.P., Teukolsky, S.A & Vetterling, W.T (1988) Numerical Recipes in C - The art of scientific computing. Cambridge University Press, Cambridge.
      62. Raichle, M.E., (1993) The Scratchpad of the Mind. Nature, 363, 623-625.
      63. Reeke, G.N. & Sporns, O. (1993) Behaviorally based modelling and computational approaches to neuroscience. Annual Review of Neuroscience, 16, 597-623.
      64. Rumelhart, D.E. (1995) Affect and neuro-modulation: a connectionist approach. In The Mind, The Brain and Complex Adaptive Systems (Eds Morowitz, H.J. & Singer, J.L.) Addison - Wesley, Reading (MA).
      65. Ruppin, E. & Reggia, J.A. (1995) A neural model of memory impairment in diffuse cerebral atrophy. British Journal of Psychiatry, 166,19-28.
      66. Schreiter-Gasser, U., Gasser, T. & Ziegler, P. (1993) Quantitative EEG analysis in early onset Alzheimer's disease : a controlled study. Electroencephalography and clinical Neurophysiology, 86,15-22.
      67. Sillito, A.M., Jones, H.E., Gerstein, G.L. & West, D.C. (1994) Feature-linked synchronization of thalamic relay cell firing induced by feedback from the visual cortex. Nature, 369, 479-482.
      68. Simon, H.A. (1995) Near Decomposability & Complexity : How a Mind Resides in a Brain. In The Mind, The Brain and Complex Adaptive Systems (Eds Morowitz, H.J. & Singer, J.L.) Addison - Wesley, Reading (MA).
      69. Simone, P.M. & Baylis, G.C. (1997) Selective attention in a reaching task: Effect of normal aging and Alzheimer's disease. Journal of Experimental Psychology: Human Perception and Performance, 23, 595-608.
      70. Gordon E (2000) Integrative neuroscience: The big picture. In Gordon E, editor. Integrative Neuroscience: Bringing together biological, psychological and clinical models of the human brain. Amsteldijk: Harwood Academic Publishers, pp 1-28.
      71. Teasdale, J.D., Dritschel, B.H., Taylor, M.J. et al (1995) Stimulus-independent thought depends on central executive resources. Memory & Cognition, 23,551-559.
      72. Traub, R.D., Whittington, M.,A. & Jefferys, J.G.R. (1997) Gamma oscillation model predicts intensity coding by phase rather than frequency. Neural Computation, 9, 1251-1264.
      73. Weinberger, D.R. (1993) A connectionist approach to the prefrontal cortex. Journal of Neuropsychiatry & Clinical Neurosciences, 5, 241-253.
      74. Wickelgren, I. (1997) Getting a grasp on working memory. Science, 275, 1580-1582
      75. Wilson, M.A. & Cowan, J.D. (1973) A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik, 13, 55-80.
      76. Zhang, M., Katzman, R., Salmon, D. et al (1990) The Prevalence of Dementia and Alzheimer's Disease in Shanghai, China: Impact of Age, Gender and Education. Annals of Neurology, 27, 428-437

      IX. Appendices

      1. Appendix I
      This appendix contains two code samples. The first is the main code of a program for simulation of dual task, where the overall structure of the programming is evident. The second is an example of C++ classes developed for this project, demonstrating the building of more complex classes on the basis of simpler ones ('class inheritance'), modular programming and encapsulation.

       Email me for a gzip - compressed version of these samples

      2. Appendix II

      Example of literature search strategy showing progression from wider to more specific terms. This search was undertaken with librarian guidance.

      Database: Medline <1996 to June 1999>

      Set Search Results


      001 alzheimer disease/ 5941

      002 alzheimer' 5926

      003 alzheimer's 5519

      004 5926

      005 1 or 2 or 3 or 4 7555

      006 dementia/ 2552

      007 4541

      008 6 or 7 5266

      009 5 or 8 10618

      010 executive 148

      011 central 40

      012 supervisory attentional 3

      013 supervisory 4

      014 attentional 29

      015 control of 11

      016 attention/ 3502

      017 13531

      018 dual 95

      019 dual task 1

      020 concurent 0

      021 concurrent 13

      022 simultaneous 3

      023 dual task$.tw. 101

      024 simultaneous task$.tw. 3

      025 concurrent task$.tw. 27

      026 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 15808

      027 234

      028 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 27 352

      029 computer simulation/ 6221

      030 75696

      031 model$.tw. 96966

      032 connectionist model$.tw. 37

      033 "neural networks (computer)"/ 1395

      034 neural network$.tw. 1412

      035 dynamical 30

      036 dynamical model$.tw. 34

      037 29 or 32 or 33 or 34 or 35 or 36 7900

      038 9 and 26 and 28 and 37 1

      039 28 and 37 7

      040 9 and 26 325

      041 9 and 28 10

      042 from 39 keep 1-3,6 4

      043 26 and 37 164

      044 43 and 9 4

      045 from 41 keep 3-8 6

      046 45 or 42 10

        Contents   Abstract    Introduction    Aims    Methods    Results    Discussion
       Acknowledgements    References    Appendices