Technical Program

Paper Detail

Paper: PS-1A.42
Session: Poster Session 1A
Location: Symphony/Overture
Session Time: Thursday, September 6, 16:30 - 18:30
Presentation Time:Thursday, September 6, 16:30 - 18:30
Presentation: Poster
Publication: 2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania
Paper Title: A Neural Microcircuit Model for a Scalable Scale-invariant Representation of Time
Manuscript:  Click here to view manuscript
Authors: Yue Liu, Zoran Tiganj, Michael Hasselmo, Marc Howard, Boston University, United States
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 paper 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). Because of the generality of the Laplace transform and the flexibility of this neural model, this neural architecture could contribute to many neural computations over functions.