A neural microcircuit model for a scalable scale-invariant representation of time

Hippocampus. 2019 Mar;29(3):260-274. doi: 10.1002/hipo.22994. Epub 2018 Nov 13.

Abstract

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).

Keywords: CAN-current; Laplace transform; rescaling; scale-invariance; time cells.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Animals
  • Brain / physiology*
  • Humans
  • Models, Neurological*
  • Models, Theoretical*
  • Neural Networks, Computer*
  • Neurons / physiology
  • Time Perception / physiology*