Enhanced semantic priming in schizophrenia: a computer model based on excessive pruning of local connections in association cortex

Neuronal and Astrocytic Regulations in Schizophrenia: A Computational Modelling Study

According to the tripartite synapse model, astrocytes have a modulatory effect on neuronal signal transmission. More recently, astrocyte malfunction has been associated with psychiatric diseases such as schizophrenia. Several hypotheses have been proposed on the pathological mechanisms of astrocytes in schizophrenia. For example, post-mortem examinations have revealed a reduced astrocytic density in patients with schizophrenia. Another hypothesis suggests that disease symptoms are linked to an abnormality of glutamate transmission, which is also regulated by astrocytes (glutamate hypothesis of schizophrenia). Electrophysiological findings indicate a dispute over whether the disorder causes an increase or a decrease in neuronal and astrocytic activity. Moreover, there is no consensus as to which molecular pathways and network mechanisms are altered in schizophrenia. Computational models can aid the process in finding the underlying pathological malfunctions. The effect of astrocytes on the activity of neuron-astrocyte networks has been analysed with computational models. These can reproduce experimentally observed phenomena, such as astrocytic modulation of spike and burst signalling in neuron-astrocyte networks. Using an established computational neuron-astrocyte network model, we simulate experimental data of healthy and pathological networks by using different neuronal and astrocytic parameter configurations. In our simulations, the reduction of neuronal or astrocytic cell densities yields decreased glutamate levels and a statistically significant reduction in the network activity. Amplifications of the astrocytic ATP release toward postsynaptic terminals also reduced the network activity and resulted in temporarily increased glutamate levels. In contrast, reducing either the glutamate release or re-uptake in astrocytes resulted in higher network activities. Similarly, an increase in synaptic weights of excitatory or inhibitory neurons raises the excitability of individual cells and elevates the activation level of the network. To conclude, our simulations suggest that the impairment of both neurons and astrocytes disturbs the neuronal network activity in schizophrenia.

Comprehensive review: Computational modelling of schizophrenia

Computational modelling has been used to address: (1) the variety of symptoms observed in schizophrenia using abstract models of behavior (e.g. Bayesian models - top-down descriptive models of psychopathology); (2) the causes of these symptoms using biologically realistic models involving abnormal neuromodulation and/or receptor imbalance (e.g. connectionist and neural networks - bottom-up realistic models of neural processes). These different levels of analysis have been used to answer different questions (i.e. understanding behavioral vs. neurobiological anomalies) about the nature of the disorder. As such, these computational studies have mostly supported diverging hypotheses of schizophrenia's pathophysiology, resulting in a literature that is not always expanding coherently. Some of these hypotheses are however ripe for revision using novel empirical evidence. Here we present a review that first synthesizes the literature of computational modelling for schizophrenia and psychotic symptoms into categories supporting the dopamine, glutamate, GABA, dysconnection and Bayesian inference hypotheses respectively. Secondly, we compare model predictions against the accumulated empirical evidence and finally we identify specific hypotheses that have been left relatively under-investigated.

Bridging Levels of Understanding in Schizophrenia Through Computational Modeling

Schizophrenia is an illness with a remarkably complex symptom presentation that has thus far been out of reach of neuroscientific explanation. This presents a fundamental problem for developing better treatments that target specific symptoms or root causes. One promising path forward is the incorporation of computational neuroscience, which provides a way to formalize experimental observations and, in turn, make theoretical predictions for subsequent studies. We review three complementary approaches: (a) biophysically based models developed to test cellular-level and synaptic hypotheses, (b) connectionist models that give insight into large-scale neural-system-level disturbances in schizophrenia, and (c) models that provide a formalism for observations of complex behavioral deficits, such as negative symptoms. We argue that harnessing all of these modeling approaches represents a productive approach for better understanding schizophrenia. We discuss how blending these approaches can allow the field to progress toward a more comprehensive understanding of schizophrenia and its treatment.

Schizophrenia and cortical blindness: protective effects and implications for language

The repeatedly noted absence of case-reports of individuals with schizophrenia and congenital/early developed blindness has led several authors to argue that the latter can confer protective effects against the former. In this work, we present a number of relevant case-reports from different syndromes that show comorbidity of congenital and early blindness with schizophrenia. On the basis of these reports, we argue that a distinction between different types of blindness in terms of the origin of the visual deficit, cortical or peripheral, is crucial for understanding the observed patterns of comorbidity. We discuss the genetic underpinnings and the brain structures involved in schizophrenia and blindness, with insights from language processing, laying emphasis on the three structures that particularly stand out: the occipital cortex, the lateral geniculate nucleus (LGN), and the pulvinar. Last, we build on previous literature on the nature of the protective effects in order to offer novel insights into the nature of the protection mechanism from the perspective of the brain structures involved in each type of blindness.

Neurobiology of schizophrenia onset

The clinical symptoms and cognitive and functional deficits of schizophrenia typically begin to gradually emerge during late adolescence and early adulthood. Recent findings suggest that disturbances of a specific subset of inhibitory neurons that contain the calcium-binding protein parvalbumin (PV), which may regulate the course of postnatal developmental experience-dependent synaptic plasticity in the cerebral cortex, including the prefrontal cortex (PFC), may be involved in the pathogenesis of the onset of this illness. Specifically, converging lines of evidence suggest that oxidative stress, extracellular matrix (ECM) deficit and impaired glutamatergic innervation may contribute to the functional impairment of PV neurons, which may then lead to aberrant developmental synaptic pruning of pyramidal cell circuits during adolescence in the PFC. In addition to promoting the functional integrity of PV neurons, maturation of ECM may also play an instrumental role in the termination of developmental PFC synaptic pruning; thus, ECM deficit can directly lead to excessive loss of synapses by prolonging the course of pruning. Together, these mechanisms may contribute to the onset of schizophrenia by compromising the integrity, stability, and fidelity of PFC connectional architecture that is necessary for reliable and predictable information processing. As such, further characterization of these mechanisms will have implications for the conceptualization of rational strategies for the diagnosis, early intervention, and prevention of this debilitating disorder.

The formation of categories and the representation of feature saliency: analysis with a computational model trained with an Hebbian paradigm

An important issue in semantic memory models is the formation of categories and taxonomies, and the different role played by shared vs. distinctive and salient vs. marginal features. Aim of this work is to extend our previous model to critically discuss the mechanisms leading to the formation of categories, and to investigate how feature saliency can be learned from past experience. The model assumes that an object is represented as a collection of features, which belong to different cortical areas and are topologically organized. Excitatory synapses among features are created on the basis of past experience of object presentation, with a Hebbian paradigm, including the use of potentiation and depression of synapses, and thresholding for the presynaptic and postsynaptic. The model was trained using simple schematic objects as input (i.e., vector of features) having some shared features (so as to realize a simple category) and some distinctive features with different frequency. Three different taxonomies of objects were separately trained and tested, which differ as to the number of correlated features and the structure of categories. Results show that categories can be formed from past experience, using Hebbian rules with a different threshold for postsynaptic and presynaptic activity. Furthermore, features have a different saliency, as a consequence of their different frequency during training. The trained network is able to solve simple object recognition tasks, by maintaining a distinction between categories and individual members in the category, and providing a different role for salient features vs. not-salient features. In particular, not-salient features are not evoked in memory when thinking about the object, but they facilitate the reconstruction of objects when provided as input to the model. The results can provide indications on which neural mechanisms can be exploited to form robust categories among objects and on which mechanisms could be implemented in artificial connectionist systems to extract concepts and categories from a continuous stream of input objects (each represented as a vector of features).

Developmental heterochrony and the evolution of autistic perception, cognition and behavior

BACKGROUND

Autism is usually conceptualized as a disorder or disease that involves fundamentally abnormal neurodevelopment. In the present work, the hypothesis that a suite of core autism-related traits may commonly represent simple delays or non-completion of typical childhood developmental trajectories is evaluated.

DISCUSSION

A comprehensive review of the literature indicates that, with regard to the four phenotypes of (1) restricted interests and repetitive behavior, (2) short-range and long-range structural and functional brain connectivity, (3) global and local visual perception and processing, and (4) the presence of absolute pitch, the differences between autistic individuals and typically developing individuals closely parallel the differences between younger and older children.

SUMMARY

The results of this study are concordant with a model of 'developmental heterochrony', and suggest that evolutionary extension of child development along the human lineage has potentiated and structured genetic risk for autism and the expression of autistic perception, cognition and behavior.

Cognitive and neuroplasticity mechanisms by which congenital or early blindness may confer a protective effect against schizophrenia

Several authors have noted that there are no reported cases of people with schizophrenia who were born blind or who developed blindness shortly after birth, suggesting that congenital or early (C/E) blindness may serve as a protective factor against schizophrenia. By what mechanisms might this effect operate? Here, we hypothesize that C/E blindness offers protection by strengthening cognitive functions whose impairment characterizes schizophrenia, and by constraining cognitive processes that exhibit excessive flexibility in schizophrenia. After briefly summarizing evidence that schizophrenia is fundamentally a cognitive disorder, we review areas of perceptual and cognitive function that are both impaired in the illness and augmented in C/E blindness, as compared to healthy sighted individuals. We next discuss: (1) the role of neuroplasticity in driving these cognitive changes in C/E blindness; (2) evidence that C/E blindness does not confer protective effects against other mental disorders; and (3) evidence that other forms of C/E sensory loss (e.g., deafness) do not reduce the risk of schizophrenia. We conclude by discussing implications of these data for designing cognitive training interventions to reduce schizophrenia-related cognitive impairment, and perhaps to reduce the likelihood of the development of the disorder itself.

Excessive attractor instability accounts for semantic priming in schizophrenia

One of the most pervasive findings in studies of schizophrenics with thought disorders is their peculiar pattern of semantic priming, which presumably reflects abnormal associative processes in the semantic system of these patients. Semantic priming is manifested by faster and more accurate recognition of a word-target when preceded by a semantically related prime, relative to an unrelated prime condition. Compared to control, semantic priming in schizophrenics is characterized by reduced priming effects at long prime-target Stimulus Onset Asynchrony (SOA) and, sometimes, augmented priming at short SOA. In addition, unlike controls, schizophrenics consistently show indirect (mediated) priming (such as from the prime ‘wedding’ to the target ‘finger’, mediated by ‘ring’). In a previous study, we developed a novel attractor neural network model with synaptic adaptation mechanisms that could account for semantic priming patterns in healthy individuals. Here, we examine the consequences of introducing attractor instability to this network, which is hypothesized to arise from dysfunctional synaptic transmission known to occur in schizophrenia. In two simulated experiments, we demonstrate how such instability speeds up the network’s dynamics and, consequently, produces the full spectrum of priming effects previously reported in patients. The model also explains the inconsistency of augmented priming results at short SOAs using directly related pairs relative to the consistency of indirect priming. Further, we discuss how the same mechanism could account for other symptoms of the disease, such as derailment (‘loose associations’) or the commonly seen difficulty of patients in utilizing context. Finally, we show how the model can statistically implement the overly-broad wave of spreading activation previously presumed to characterize thought-disorders in schizophrenia.

Using computational patients to evaluate illness mechanisms in schizophrenia

BACKGROUND

Various malfunctions involving working memory, semantics, prediction error, and dopamine neuromodulation have been hypothesized to cause disorganized speech and delusions in schizophrenia. Computational models may provide insights into why some mechanisms are unlikely, suggest alternative mechanisms, and tie together explanations of seemingly disparate symptoms and experimental findings.

METHODS

Eight corresponding illness mechanisms were simulated in DISCERN, an artificial neural network model of narrative understanding and recall. For this study, DISCERN learned sets of autobiographical and impersonal crime stories with associated emotion coding. In addition, 20 healthy control subjects and 37 patients with schizophrenia or schizoaffective disorder matched for age, gender, and parental education were studied using a delayed story recall task. A goodness-of-fit analysis was performed to determine the mechanism best reproducing narrative breakdown profiles generated by healthy control subjects and patients with schizophrenia. Evidence of delusion-like narratives was sought in simulations best matching the narrative breakdown profile of patients.

RESULTS

All mechanisms were equivalent in matching the narrative breakdown profile of healthy control subjects. However, exaggerated prediction-error signaling during consolidation of episodic memories, termed hyperlearning, was statistically superior to other mechanisms in matching the narrative breakdown profile of patients. These simulations also systematically confused autobiographical agents with impersonal crime story agents to model fixed, self-referential delusions.

CONCLUSIONS

Findings suggest that exaggerated prediction-error signaling in schizophrenia intermingles and corrupts narrative memories when incorporated into long-term storage, thereby disrupting narrative language and producing fixed delusional narratives. If further validated by clinical studies, these computational patients could provide a platform for developing and testing novel treatments.

An integrated neural model of semantic memory, lexical retrieval and category formation, based on a distributed feature representation

This work presents a connectionist model of the semantic-lexical system. Model assumes that the lexical and semantic aspects of language are memorized in two distinct stores, and are then linked together on the basis of previous experience, using physiological learning mechanisms. Particular characteristics of the model are: (1) the semantic aspects of an object are described by a collection of features, whose number may vary between objects. (2) Individual features are topologically organized to implement a similarity principle. (3) Gamma-band synchronization is used to segment different objects simultaneously. (4) The model is able to simulate the formation of categories, assuming that objects belong to the same category if they share some features. (5) Homosynaptic potentiation and homosynaptic depression are used within the semantic network, to create an asymmetric pattern of synapses; this allows a different role to be assigned to shared and distinctive features during object reconstruction. (6) Features which frequently occurred together, and the corresponding word-forms, become linked via reciprocal excitatory synapses. (7) Features in the semantic network tend to inhibit words not associated with them during the previous learning phase. Simulations show that, after learning, presentation of a cue can evoke the overall object and the corresponding word in the lexical area. Word presentation, in turn, activates the corresponding features in the sensory-motor areas, recreating the same conditions occurred during learning, according to a grounded cognition viewpoint. Several words and their conceptual description can coexist in the lexical-semantic system exploiting gamma-band time division. Schematic exempla are shown, to illustrate the possibility to distinguish between words representing a category, and words representing individual members and to evaluate the role of gamma-band synchronization in priming. Finally, the model is used to simulate patients with focalized lesions, assuming a damage of synaptic strength in specific feature areas. Results are critically discussed in view of future model extensions and application to real objects. The model represents an original effort to incorporate many basic ideas, found in recent conceptual theories, within a single quantitative scaffold.

A computational model of the lexical-semantic system based on a grounded cognition approach

This work presents a connectionist model of the semantic-lexical system based on grounded cognition. The model assumes that the lexical and semantic aspects of language are memorized in two distinct stores. The semantic properties of objects are represented as a collection of features, whose number may vary among objects. Features are described as activation of neural oscillators in different sensory-motor areas (one area for each feature) topographically organized to implement a similarity principle. Lexical items are represented as activation of neural groups in a different layer. Lexical and semantic aspects are then linked together on the basis of previous experience, using physiological learning mechanisms. After training, features which frequently occurred together, and the corresponding word-forms, become linked via reciprocal excitatory synapses. The model also includes some inhibitory synapses: features in the semantic network tend to inhibit words not associated with them during the previous learning phase. Simulations show that after learning, presentation of a cue can evoke the overall object and the corresponding word in the lexical area. Moreover, different objects and the corresponding words can be simultaneously retrieved and segmented via a time division in the gamma-band. Word presentation, in turn, activates the corresponding features in the sensory-motor areas, recreating the same conditions occurring during learning. The model simulates the formation of categories, assuming that objects belong to the same category if they share some features. Simple exempla are shown to illustrate how words representing a category can be distinguished from words representing individual members. Finally, the model can be used to simulate patients with focalized lesions, assuming an impairment of synaptic strength in specific feature areas.

The role of dopamine in the maintenance of working memory in prefrontal cortex neurons: input-driven versus internally-driven networks

How do organisms select and organize relevant sensory input in working memory (WM) in order to deal with constantly changing environmental cues? Once information has been stored in WM, how is it protected from and altered by the continuous stream of sensory input and internally generated planning? The present study proposes a novel role for dopamine (DA) in the maintenance of WM in the prefrontal cortex (Pfc) neurons that begins to address these issues. In particular, DA mediates the alternation of the Pfc network between input-driven and internally-driven states, which in turn drives WM updates and storage. A biologically inspired neural network model of Pfc is formulated to provide a link between the mechanisms of state switching and the biophysical properties of Pfc neurons. This model belongs to the recurrent competitive fields(33) class of dynamical systems which have been extensively mathematically characterized and exhibit the two functional states of interest: input-driven and internally-driven. This hypothesis was tested with two working memory tasks of increasing difficulty: a simple working memory task and a delayed alternation task. The results suggest that optimal WM storage in spite of noise is achieved with a phasic DA input followed by a lower DA sustained activity. Hypo and hyper-dopaminergic activity that alter this ideal pattern lead to increased distractibility from non-relevant pattern and prolonged perseverations on presented patterns, respectively.

Dominance of objects over context in a mediotemporal lobe model of schizophrenia

Background

A large body of evidence suggests impaired context processing in schizophrenia. Here we propose that this impairment arises from defective integration of mediotemporal ‘what’ and ‘where’ routes, carrying object and spatial information to the hippocampus.

Methodology and Findings

We have previously shown, in a mediotemporal lobe (MTL) model, that the abnormal connectivity between MTL regions observed in schizophrenia can explain the episodic memory deficits associated with the disorder. Here we show that the same neuropathology leads to several context processing deficits observed in patients with schizophrenia: 1) failure to choose subordinate stimuli over dominant ones when the former fit the context, 2) decreased contextual constraints in memory retrieval, as reflected in increased false alarm rates and 3) impaired retrieval of contextual information in source monitoring. Model analyses show that these deficits occur because the ‘schizophrenic MTL’ forms fragmented episodic representations, in which objects are overrepresented at the expense of spatial contextual information.

Conclusions and Significance

These findings highlight the importance of MTL neuropathology in schizophrenia, demonstrating that it may underlie a broad spectrum of deficits, including context processing and memory impairments. It is argued that these processing deficits may contribute to central schizophrenia symptoms such as contextually inappropriate behavior, associative abnormalities, conversational drift, concreteness and delusions.

Evidence of multistability in a realistic computer simulation of hippocampus subfield CA1

The manner in which hippocampus processes neural signals is thought to be central to the memory encoding process. A theoretically oriented literature has suggested that this is carried out via "attractors" or distinctive spatio-temporal patterns of activity. However, these ideas have not been thoroughly investigated using computational models featuring both realistic single-cell physiology and detailed cell-to-cell connectivity. Here we present a 452 cell simulation based on Traub et al.'s pyramidal cell [Traub RD, Jefferys JG, Miles R, Whittington MA, Toth K. A branching dendritic model of a rodent CA3 pyramidal neurone. J Physiol (Lond) 1994;481:79-95] and interneuron [Traub RD, Miles R, Pyramidal cell-to-inhibitory cell spike transduction explicable by active dendritic conductances in inhibitory cell. J Comput Neurosci 1995;2:291-8] models, incorporating patterns of synaptic connectivity based on an extensive review of the neuroanatomic literature. When stimulated with a one second physiologically realistic input, our simulated tissue shows the ability to hold activity on-line for several seconds; furthermore, its spiking activity, as measured by frequency and interspike interval (ISI) distributions, resembles that of in vivo hippocampus. An interesting emergent property of the system is its tendency to transition from stable state to stable state, a behavior consistent with recent experimental findings [Sasaki T, Matsuki N, Ikegaya Y. Metastability of active CA3 networks. J Neurosci 2007;27:517-28]. Inspection of spike trains and simulated blockade of K(AHP) channels suggest that this is mediated by spike frequency adaptation. This finding, in conjunction with studies showing that apamin, a K(AHP) channel blocker, enhances the memory consolidation process in laboratory animals, suggests the formation of stable attractor states is central to the process by which memories are encoded. Ways that this methodology could shed light on the etiology of mental illness, such as schizophrenia, are discussed.

Cortical plasticity: A proposed mechanism by which genomic factors lead to the behavioral and neurological phenotype of autism spectrum and psychotic-spectrum disorders

Crespi & Badcock (C&B) hypothesize that biases toward expression of paternally or maternally imprinted genes lead to the symptoms of autism spectrum disorders (ASD) and psychotic-spectrum disorders (PSD), respectively. We suggest that such genetic risk factors may act by inducing abnormalities in developmental and learning-related plasticity. We provide preliminary evidence of abnormal plasticity in ASD and suggest transcranial magnetic stimulation as a useful tool to investigate as well as influence cortical plasticity.

Psychosis and autism as diametrical disorders of the social brain

Autistic-spectrum conditions and psychotic-spectrum conditions (mainly schizophrenia, bipolar disorder, and major depression) represent two major suites of disorders of human cognition, affect, and behavior that involve altered development and function of the social brain. We describe evidence that a large set of phenotypic traits exhibit diametrically opposite phenotypes in autistic-spectrum versus psychotic-spectrum conditions, with a focus on schizophrenia. This suite of traits is inter-correlated, in that autism involves a general pattern of constrained overgrowth, whereas schizophrenia involves undergrowth. These disorders also exhibit diametric patterns for traits related to social brain development, including aspects of gaze, agency, social cognition, local versus global processing, language, and behavior. Social cognition is thus underdeveloped in autistic-spectrum conditions and hyper-developed on the psychotic spectrum.;>We propose and evaluate a novel hypothesis that may help to explain these diametric phenotypes: that the development of these two sets of conditions is mediated in part by alterations of genomic imprinting. Evidence regarding the genetic, physiological, neurological, and psychological underpinnings of psychotic-spectrum conditions supports the hypothesis that the etiologies of these conditions involve biases towards increased relative effects from imprinted genes with maternal expression, which engender a general pattern of undergrowth. By contrast, autistic-spectrum conditions appear to involve increased relative bias towards effects of paternally expressed genes, which mediate overgrowth. This hypothesis provides a simple yet comprehensive theory, grounded in evolutionary biology and genetics, for understanding the causes and phenotypes of autistic-spectrum and psychotic-spectrum conditions.

Aspects of Piaget's cognitive developmental psychology and neurobiology of psychotic disorders - an integrative model

Psychological, neurobiological and neurodevelopmental approaches have frequently been used to provide pathogenic concepts on psychotic disorders. However, aspects of cognitive developmental psychology have hardly been considered in current models. Using a hypothesis-generating approach an integration of these concepts was conducted. According to Piaget (1896-1980), assimilation and accommodation as forms of maintenance and modification of cognitive schemata represent fundamental processes of the brain. In general, based on the perceived input stimuli, cognitive schemata are developed resulting in a conception of the world, the realistic validity and the actuality of which is still being controlled and modified by cognitive adjustment processes. In psychotic disorders, however, a disproportion of environmental demands and the ability to activate required neuronal adaptation processes occurs. We therefore hypothesize a failure of the adjustment of real and requested output patterns. As a consequence autonomous cognitive schemata are generated, which fail to adjust with reality resulting in psychotic symptomatology. Neurobiological, especially neuromodulatory and neuroplastic processes play a central role in these perceptive and cognitive processes. In conclusion, integration of cognitive developmental psychology into the existing pathogenic concepts of psychotic disorders leads to interesting insights into basic disease mechanisms and also guides future research in the cognitive neuroscience of such disorders.

Attentional window in schizophrenia and schizotypal personality: Insight from negative priming studies

One of the core deficits that characterizes schizophrenia is an increase in distractibility and disinhibition at all levels of information processing. Patients with schizophrenia seem unable to focus attention on the relevant events while ignoring the irrelevant stimuli. This pattern of behavior is also observed in unmedicated schizotypal individuals who may carry liability for schizophrenia. In this review, we focus on studies of attentional inhibition, as assessed by the negative priming paradigm, to elucidate the relationships among deficits in inhibition, clinical symptoms and medication effects. We then consider models of the etiology of deficits in negative priming in schizophrenia and schizotypal personality. Finally, we discuss the potential power of utilizing hypothesis-driven cognitive paradigms in psychiatric research.

A neural plasticity perspective on the schizophrenic condition

Imbalanced plasticity of neural networks in the brain is proposed to underlie deficits in the integration of efferent and afferent processes in schizophrenia. These deficits affect the priming of the behavior implementing systems by prior knowledge, and thus impair both controlled regulation and automatic activation of mental and motor processes. The sense of self as a distinct entity can consequently be undermined. In predominantly reality-distorting patients, hypo-plasticity of neural connectivity may cause the emergence of highly focused but inflexible patterns of activation in their representation and response systems. This may lead to dominance of prepotent patterns of activity in these systems and a relative inability of higher control systems to bias lower level activity towards congruence with the ongoing cognitive and motor context. By contrast, predominantly disorganized patients are characterized by hyper-plastic connectivity. This leads to a weakening of prepotent response tendencies but also, as in reality-distorting patients, to less effective top-down contextual constraining.

Modeling of context-dependent retrieval in hippocampal region CA1: implications for cognitive function in schizophrenia

The symptoms of schizophrenia may be associated with reductions in NMDA receptor (NMDAR) function. This is suggested by the psychotomimetic effects of NMDA antagonists, the ameliorative effects of NMDAR indirect agonists, elevated levels of the NMDA antagonist N-acetyl-aspartyl-glutamate (NAAG) in schizophrenic brain, and findings from recent genetic studies. However, the link between reduced NMDAR function and the behavioral features of schizophrenics has not been made explicit. Here we present a network simulation of hippocampal function, focused on retrieval of verbal stimuli in human memory tasks. Specifically, we trained a computational model of the hippocampal complex to perform a context-dependent paired associate task, a free recall task with category clustering, and the transitive inference (TI) task. In this network, direct perforant pathway input from entorhinal cortex to region CA1 provides the basis for semantic context cueing during initial encoding and retrieval, allowing selective retrieval on the basis of category cues. Alterations in the magnitude of this direct perforant pathway input to region CA1 causes impairments in use of organizational strategies for memory, accounting for specific features of memory dysfunction in schizophrenics and in normals treated with ketamine. This model provides a theoretical link between cellular physiological changes and specific cognitive symptoms. As such, it can shed light on the etiology of schizophrenia in a fundamental way, and also holds the promise of pointing the way to more effective treatments.

Computational neuropharmacology: dynamical approaches in drug discovery

Computational approaches that adopt dynamical models are widely accepted in basic and clinical neuroscience research as indispensable tools with which to understand normal and pathological neuronal mechanisms. Although computer-aided techniques have been used in pharmaceutical research (e.g. in structure- and ligand-based drug design), the power of dynamical models has not yet been exploited in drug discovery. We suggest that dynamical system theory and computational neuroscience--integrated with well-established, conventional molecular and electrophysiological methods--offer a broad perspective in drug discovery and in the search for novel targets and strategies for the treatment of neurological and psychiatric diseases.

Neural stability and flexibility: a computational approach

This paper addresses the issue of stability and flexibility of neural systems, and how a balance can be achieved. Assuming a close correspondence with cognitive and mental processes, we use a cortical neural network model to investigate how regulation of the neurodynamics can result in an efficient information processing, in terms of learning and associative memory. In particular, we use this model to investigate relations between structure, dynamics, and function of a neural system, and how the stability-flexibility dilemma may be solved by proper regulation. We focus on the complex neurodynamics and its modulation, and how this is related to the neural circuitry, where synaptic modification and network pruning are considered. Finally, we discuss the relevance of these results to clinical and experimental neuroscience and speculate on a link between neural instability and mental disorders.