We investigate the effect of synaptic compensation on the dynamic behavior of an attractor neural network receiving its input stimuli as external fields projecting on the network. It is shown how, in face of weakened inputs, memory performance may be preserved by strengthening internal synaptic connections and increasing the noise level. Yet, these compensatory changes necessarily have adverse side effects, leading to spontaneous, stimulus-independent retrieval of stored patterns. These results can support Stevens' recent hypothesis that the onset of Schizophrenia is associated with frontal synaptic regeneration, occurring subsequent to the degeneration of temporal neurons projecting on these areas. 1 Introduction A prominent feature of attractor neural networks (ANN) as models for associative memory is their robustness, i.e. their ability to maintain performance in face of damage to their neurons and synapses. Robustness of biological systems is due, however, not just to their dist...

This paper presents an Attractor Neural Network (ANN) model of Recall and Recognition. It is shown that an ANN Hopfield-based network can qualitatively account for a wide range of experimental psychological data pertaining to these two main aspects of memory retrieval. After providing simple, straight-forward definitions of Recall and Recognition in the model, a wide variety of `high-level' psychological phenomena are shown to emerge from the `low-level' neural-like properties of the network. It is shown that modeling the effect of memory load on the network's retrieval properties requires the incorporation of noise into the network's dynamics. External projections may account for phenomena related with the stored items' associative links, but are not sufficient for representing context. With low memory load, the network generates retrieval response times which have the same distribution form as that observed experimentally. Finally, estimations of the probabilities of successful Recall and Recognition are obtained, possibly enabling further quantitative examination of the model.

This paper reviews recent neural modeling studies of psychiatric disorders. Numerous aspects of psychiatric disturbances have been investigated, such as the role of synaptic changes in the pathogenesis of Alzheimer's disease, the study of spurious attractors as possible neural correlates of schizophrenic positive symptoms, and the exploration of the ability of feedforward and recurrent networks to quantitatively model the cognitive performance of schizophrenic patients. Current models all employ considerable simplifications, both on the level of the behavioral phenomenology they seek to explore, and on the level of their structure and dynamics. However, it is encouraging to realize that the disruption of just a few simple computational mechanisms can lead to behaviors which correspond to some of the clinical features of psychiatric disorders, and can shed light on their pathogenesis. 1 Introduction Neural modeling research is currently a very active and growing scientific field with i...

We implement and study a computational model of Stevens' [1992] theory of the pathogenesis of schizophrenia. This theory hypothesizes that the onset of schizophrenia is associated with reactive synaptic regeneration occurring in brain regions receiving degenerating temporal lobe projections. Concentrating on one such area, the frontal cortex, we model a frontal module as an associative memory neural network whose input synapses represent incoming temporal projections. Modeling Stevens' hypothesized pathological synaptic changes in this framework results in adverse side effects reminiscent of hallucinations and delusions seen in schizophrenia: spontaneous, stimulusindependent retrieval of stored memories focused on just a few of the stored patterns. These could account for the occurrence of schizophrenic delusions and hallucinations without any apparent external trigger, and for their tendency to concentrate on a few central cognitive and perceptual themes. The model explains why schizo...

: During the last several years there has been a growing interest in developing computational models of phenomena associated with brain and cognitive disorders. Work in this area has included neural modeling studies of Alzheimer's disease, aphasia and dyslexia, epilepsy, stroke, Parkinson's disease, schizophrenia, depression, and related problems. Here we suggest that this computational work represents a new research paradigm for the understanding of disease, complementing traditional methods such as clinical studies and animal models. While computational models have so far focused most prominently on neurological and psychiatric disorders, there is no reason that this approach cannot be extended productively to any area of medicine. Acknowledgement: Work supported by NINDS awards NS35460 and NS294l4, and NIDCD award DC00699. 1 1. Introduction During the last few years neural modeling methods have gained increasing visibility in clinical computing. Perhaps most widely appreciated i...

There have been many theories about and computational models of the schizophrenic disease state. Brain imaging techniques have suggested that abnormalities of the thalamus may contribute to the pathophysiology of schizophrenia. Several studies have found the thalamus to be altered in schizophrenia, and the thalamus has connections with other brain structures implicated in the disorder. This paper describes an experiment examining thalamic levels of the metabolite N-acetylaspartate (NAA), taken from schizophrenics and controls using in-vivo proton magnetic resonance spectroscopic imaging. Automatic relevance determination was performed on neural networks trained on this data, identifying NAA group differences in the pulvinar and mediodorsal nucleus, underscoring the importance of examining thalamic subregions in schizophrenia.

This paper presents the hypothesis that NMDA receptor delayed maturation (NRDM) may lead to the pathogenesis of schizophrenic psychotic symptoms. This hypothesis is further analyzed in the language of a neural modeling formulation. This formulation points to a possible chain of pathological events, leading from molecular-level NRDM to over-increased synaptic plasticity, and to the formation of pathological attractors, a putative macroscopic-level correlate of schizophrenic positive symptoms. The relations of the NRDM hypothesis to other alterations which are assumed to take place in schizophrenia are discussed, together with possible ways to test this hypothesis. 1 The NRDM Hypothesis NMDA receptor antagonist drugs exacerbate schizophrenic symptoms and, in animals, produce neurodegenerative effects in brain areas thought be to involved in the pathogenesis of this disorder. Consequently, it has been proposed that spontaneously occurring NMDA receptor hypofunction causes schizophrenia...

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