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A neural microcircuit model for a scalable scale‐invariant representation of time
Author(s) -
Liu Yue,
Tiganj Zoran,
Hasselmo Michael E.,
Howard Marc W.
Publication year - 2019
Publication title -
hippocampus
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.767
H-Index - 155
eISSN - 1098-1063
pISSN - 1050-9631
DOI - 10.1002/hipo.22994
Subject(s) - laplace transform , invariant (physics) , artificial neural network , receptive field , scale invariance , neural substrate , inverse , biological neural network , inverse laplace transform , computer science , scalability , neuroscience , algorithm , biological system , artificial intelligence , mathematics , psychology , mathematical analysis , machine learning , biology , statistics , cognition , geometry , database , mathematical physics
Scale‐invariant timing has been observed in a wide range of behavioral experiments. The firing properties of recently described time cells provide a possible neural substrate for scale‐invariant behavior. Earlier neural circuit models do not produce scale‐invariant neural sequences. In this article, we present a biologically detailed network model based on an earlier mathematical algorithm. The simulations incorporate exponentially decaying persistent firing maintained by the calcium‐activated nonspecific (CAN) cationic current and a network structure given by the inverse Laplace transform to generate time cells with scale‐invariant firing rates. This model provides the first biologically detailed neural circuit for generating scale‐invariant time cells. The circuit that implements the inverse Laplace transform merely consists of off‐center/on‐surround receptive fields. Critically, rescaling temporal sequences can be accomplished simply via cortical gain control (changing the slope of the f–I curve).