Effective Computing in the Brain: A Whole-Task Spiking Neural Network Model of Associative Recognition

Cognitive Computing 2018, 2018

Jelmer P. Borst, Sean Aubin, Terrence C. Stewart

Abstract

We present a whole-task spiking neural network model of associative recognition, developed in the Nengo framework. Because the resulting model is very complex (>750,000 neurons) we used magnetoencephalographic (MEG) data to constrain the model. The model matches data in occipital, temporal, prefrontal, and motor cortices, and shows how the associative recognition process could be effectively implemented in the human brain.

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Cognitive Computing
Booktitle
Cognitive Computing 2018
Address
Philadelphia, Pennsylvania

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