A neural microcircuit model for a scalable scale‐invariant representation of time. Issue 3 (13th November 2018)

• Record Type: Journal Article
• Authors: Liu, Yue; Tiganj, Zoran; Hasselmo, Michael E.; Howard, Marc W.
• Other Names: Eichenbaum Howard guestEditor.
• Is Part Of: Hippocampus. Volume 29:Issue 3(2019)
• Page Start: 260
• Page End: 274
• Publication Date: 2018-11-13
• Subjects: CAN‐current -- Laplace transform -- rescaling -- scale‐invariance -- time cells; Hippocampus (Brain) -- Periodicals; 612.825
• DOI: 10.1002/hipo.22994
• Languages: English
• ISSNs: 1050-9631

Abstract

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).