10.1007%2Fs11538-018-0415-5.pdf (2.28 MB)
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High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons

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journal contribution
posted on 02.05.2018, 15:22 by Ivan Tyukin, Alexander N. Gorban, Carlos Calvo, Julia Makarova, Valeri A. Makarov
Codifying memories is one of the fundamental problems of modern Neuroscience. The functional mechanisms behind this phenomenon remain largely unknown. Experimental evidence suggests that some of the memory functions are performed by stratified brain structures such as the hippocampus. In this particular case, single neurons in the CA1 region receive a highly multidimensional input from the CA3 area, which is a hub for information processing. We thus assess the implication of the abundance of neuronal signalling routes converging onto single cells on the information processing. We show that single neurons can selectively detect and learn arbitrary information items, given that they operate in high dimensions. The argument is based on stochastic separation theorems and the concentration of measure phenomena. We demonstrate that a simple enough functional neuronal model is capable of explaining: (i) the extreme selectivity of single neurons to the information content, (ii) simultaneous separation of several uncorrelated stimuli or informational items from a large set, and (iii) dynamic learning of new items by associating them with already "known" ones. These results constitute a basis for organization of complex memories in ensembles of single neurons. Moreover, they show that no a priori assumptions on the structural organization of neuronal ensembles are necessary for explaining basic concepts of static and dynamic memories.

History

Citation

Bulletin of Mathematical Biology, 2018

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Mathematics

Version

VoR (Version of Record)

Published in

Bulletin of Mathematical Biology

Publisher

Springer Verlag for Society for Mathematical Biology

issn

0092-8240

eissn

1522-9602

Acceptance date

04/03/2018

Copyright date

2018

Available date

02/05/2018

Publisher version

https://link.springer.com/article/10.1007/s11538-018-0415-5

Language

en