Learning Context Sensitive Logical Inference in a Neurobiolobical Simulation

Chris Eliasmith

There remains a large difference between the kinds of models typical of cognitive neuroscience versus those typical of systems neuroscience. In this paper I apply the neural engineering framework (NEF) to generate a model that is both cognitive and neural. I do this by constructing a biologically realistic model of logical inference. This model consists of a large-scale, spiking neural network that learns, encodes and manipulates language-like structures. I apply this model to the well-known Wason card selection task, and demonstrate that the model performs much like human subjects for both errors and hits.

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.