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A Computational Model of Prefrontal Cortex Function

Conference Paper Artificial Intelligence ยท Machine Learning

Abstract

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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Context

Venue
Annual Conference on Neural Information Processing Systems
Archive span
1987-2025
Indexed papers
30776
Paper id
575374122382995381
v2026.09.13