Journal
HIPPOCAMPUS
Volume 29, Issue 3, Pages 260-274Publisher
WILEY
DOI: 10.1002/hipo.22994
Keywords
CAN-current; Laplace transform; rescaling; scale-invariance; time cells
Categories
Funding
- National Institute of Biomedical Imaging and Bioengineering [R01EB022864]
- Office of Naval Research [MURI N00014-16-1-2832]
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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).
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