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

AbstractScale ‐invariant timing has been observed in a wide range of behavioral experiments. The firing properties of recently describedtime 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 circu it 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 thef –I curve).

Source: Hippocampus - November 13, 2018 Category: Neurology Authors: Yue Liu, Zoran Tiganj, Michael E. Hasselmo, Marc W. Howard Tags: RESEARCH ARTICLE Source Type: research