Powell, H., Winkel, M., Hopp, A. V. and Linde, H. (2022) A hybrid biological neural network model for solving problems in cognitive planning. Scientific Reports, 12, 10628. (doi: 10.1038/s41598-022-11567-0) (PMID:35739285) (PMCID:PMC9226121)
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Abstract
A variety of behaviors, like spatial navigation or bodily motion, can be formulated as graph traversal problems through cognitive maps. We present a neural network model which can solve such tasks and is compatible with a broad range of empirical findings about the mammalian neocortex and hippocampus. The neurons and synaptic connections in the model represent structures that can result from self-organization into a cognitive map via Hebbian learning, i.e. into a graph in which each neuron represents a point of some abstract task-relevant manifold and the recurrent connections encode a distance metric on the manifold. Graph traversal problems are solved by wave-like activation patterns which travel through the recurrent network and guide a localized peak of activity onto a path from some starting position to a target state.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Powell, Henry |
| Authors: | Powell, H., Winkel, M., Hopp, A. V., and Linde, H. |
| College/School: | College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience |
| Journal Name: | Scientific Reports |
| Publisher: | Nature Research |
| ISSN: | 2045-2322 |
| ISSN (Online): | 2045-2322 |
| Copyright Holders: | Copyright © 2022 The Authors |
| First Published: | First published in Scientific Reports 12: 10628 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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