title: A single spiking neuron that can represent interval timing: analysis, plasticity and multi-stability.
abstract: The ability to represent interval timing is crucial for many common behaviors, such as knowing whether to stop when the light turns from green to yellow. Neural representations of interval timing have been reported in the rat primary visual cortex and we have previously presented a computational framework describing how they can be learned by a network of neurons. Recent experimental and theoretical results in entorhinal cortex have shown that single neurons can exhibit persistent activity, previously thought to be generated by a network of neurons. Motivated by these single neuron results, we propose a single spiking neuron model that can learn to compute and represent interval timing. We show that a simple model, reduced analytically to a single dynamical equation, captures the average behavior of the complete high dimensional spiking model very well. Variants of this model can be used to produce bi-stable or multi-stable persistent activity. We also propose a plasticity rule by which this model can learn to represent different intervals and different levels of persistent activity.
authors.affiliation: Department of Neurobiology and Anatomy, The University of Texas Medical School at Houston, 6431 Fannin St., Suite MSB 7.046, Houston, TX 77030, USA. harel.shouval@uth.tmc.edu
authors.forename: Harel Z
authors.initials: HZ
authors.lastname: Shouval
authors.forename: Jeffrey P
authors.initials: JP
authors.lastname: Gavornik
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D002118:Calcium / Q000378:metabolism; D020013:Calcium Signaling / Q000502:physiology; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D013997:Time Factors
chemical_list: D002118:Calcium
references.citation: Proc Natl Acad Sci U S A. 2009 Apr 21;106(16):6826-31
references.pmid: 19346478
references.citation: J Exp Anal Behav. 1999 Mar;71(2):293-301
references.pmid: 16812896
references.citation: Eur J Neurosci. 2003 Jun;17(12):2727-34
references.pmid: 12823479
references.citation: Science. 2006 Mar 17;311(5767):1606-9
references.pmid: 16543459
references.citation: Trends Neurosci. 2001 Aug;24(8):455-63
references.pmid: 11476885
references.citation: Behav Neurosci. 2002 Apr;116(2):291-7
references.pmid: 11996314
references.citation: J Neurophysiol. 2002 Sep;88(3):1197-211
references.pmid: 12205141
references.citation: Neuroreport. 2004 Apr 9;15(5):745-9
references.pmid: 15073507
references.citation: Neuron. 1995 Feb;14(2):385-97
references.pmid: 7531987
references.citation: Learn Mem. 2003 Nov-Dec;10(6):456-65
references.pmid: 14657257
references.citation: J Neurosci. 2003 Jun 15;23(12):5342-53
references.pmid: 12832560
references.citation: J Neurosci. 2002 Feb 1;22(3):1081-97
references.pmid: 11826137
references.citation: Nat Neurosci. 2003 Sep;6(9):961-7
references.pmid: 12937421
references.citation: J Biol Chem. 2009 Dec 4;284(49):34423-32
references.pmid: 19815560
references.citation: J Neurophysiol. 2003 Nov;90(5):3441-54
references.pmid: 12773500
references.citation: Nature. 2002 Nov 14;420(6912):173-8
references.pmid: 12432392
references.citation: Annu Rev Neurosci. 2004;27:307-40
references.pmid: 15217335
references.citation: Front Neurosci. 2008 Jul 14;2(1):114-22
references.pmid: 18982114
references.citation: Neuron. 1995 Mar;14(3):477-85
references.pmid: 7695894
references.citation: Psychopharmacology (Berl). 2006 Oct;188(2):201-12
references.pmid: 16937099
references.citation: J Neurosci. 2000 Dec 15;20(24):8987-95
references.pmid: 11124974
references.citation: J Comput Neurosci. 2011 Apr;30(2):501-13
references.pmid: 20830512
references.citation: Brain Res Cogn Brain Res. 1996 Jun;3(3-4):227-42
references.pmid: 8806025
references.citation: Neuron. 2006 Mar 2;49(5):735-46
references.pmid: 16504948