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David Servan-Schreiber

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4 papers

1 author row

NeurIPS Conference 1994 Conference Paper

A Computational Model of Prefrontal Cortex Function

Todd Braver
Jonathan Cohen
David Servan-Schreiber

Accumulating data from neurophysiology and neuropsychology have suggested two information processing roles for prefrontal cor(cid: 173) tex (PFC): 1) short-term active memory; and 2) inhibition. We present a new behavioral task and a computational model which were developed in parallel. The task was developed to probe both of these prefrontal functions simultaneously, and produces a rich set of behavioral data that act as constraints on the model. The model is implemented in continuous-time, thus providing a natural framework in which to study the temporal dynamics of processing in the task. We show how the model can be used to examine the be(cid: 173) havioral consequences of neuromodulation in PFC. Specifically, we use the model to make novel and testable predictions regarding the behavioral performance of schizophrenics, who are hypothesized to suffer from reduced dopaminergic tone in this brain area.

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NeurIPS Conference 1989 Conference Paper

The Effect of Catecholamines on Performance: From Unit to System Behavior

David Servan-Schreiber
Harry Printz
Jonathan Cohen

At the level of individual neurons. catecholamine release increases the responsivity of cells to excitatory and inhibitory inputs. We present a model of catecholamine effects in a network of neural-like elements. We argue that changes in the responsivity of individual elements do not affect their ability to detect a signal and ignore noise. However. the same changes in cell responsivity in a network of such elements do improve the signal detection performance of the network as a whole. We show how this result can be used in a computer simulation of behavior to account for the effect of eNS stimulants on the signal detection performance of human subjects.

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NeurIPS Conference 1988 Conference Paper

Learning Sequential Structure in Simple Recurrent Networks

David Servan-Schreiber
Axel Cleeremans
James McClelland

We explore a network architecture introduced by Elman (1988) for predicting successive elements of a sequence. The network uses the pattern of activation over a set of hidden units from time-step t-l, together with element t, to predict element t+ 1. When the network is trained with strings from a particular finite-state grammar, it can learn to be a perfect finite-state recognizer for the grammar. Cluster analyses of the hidden-layer patterns of activation showed that they encode prediction-relevant information about the entire path traversed through the network. We illustrate the phases of learning with cluster analyses performed at different points during training. Several connectionist architectures that are explicitly constrained to capture sequential infonnation have been developed. Examples are Time Delay Networks (e. g. Sejnowski & Rosenberg. 1986) -- also called 'moving window' paradigms -- or algorithms such as back-propagation in time (Rumelhart. Hinton & Williams. 1986), Such architectures use explicit representations of several consecutive events. if not of the entire history of past inputs. Recently. Elman (1988) has introduced a simple recurrent network (SRN) that has the potential to master an infinite corpus of sequences with the limited means of a learning procedure that is completely local in time (see Figure I. ).

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AAAI Conference 1987 Conference Paper

From Intelligent Tutoring to Computerized Psychotherapy

David Servan-Schreiber

Building on the successes and shortcomings of previous experiences with computerized psychotherapy, we have attempted to extend the paradigm of intelligent tutoring systems to the domain of therapeutic interaction. Based on canonical examples, I present three dimensions of the task of tutoring systems: teaching problem-solving vs. domain knowledge; teaching isolated domains vs domains where students have prior misconceptions; teaching with the use of functional models of the domain vs no functional models. I then show how implications of these dimensions have helped us determine the specifications of a tutoring system for sexual therapy. Our approach has consisted of engaging patients in a tutoring dialogue driven by the identification of problem areas and their associated misconceptions. A diagnostic module, implemented as a traditional expert system, uses an extensive bug library to derive an internal model of patients. A dialogue driver relies on a hierarchy of dialogue plans and demons in order to preserve a logical grouping of related topics while remaining flexible to adapt itself, at each level of the dialogue hierarchy, to the unfolding case.

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