title: Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1.
abstract: Sparse coding models of natural scenes can account for several physiological properties of primary visual cortex (V1), including the shapes of simple cell receptive fields (RFs) and the highly kurtotic firing rates of V1 neurons. Current spiking network models of pattern learning and sparse coding require direct inhibitory connections between the excitatory simple cells, in conflict with the physiological distinction between excitatory (glutamatergic) and inhibitory (GABAergic) neurons (Dale's Law). At the same time, the computational role of inhibitory neurons in cortical microcircuit function has yet to be fully explained. Here we show that adding a separate population of inhibitory neurons to a spiking model of V1 provides conformance to Dale's Law, proposes a computational role for at least one class of interneurons, and accounts for certain observed physiological properties in V1. When trained on natural images, this excitatory-inhibitory spiking circuit learns a sparse code with Gabor-like RFs as found in V1 using only local synaptic plasticity rules. The inhibitory neurons enable sparse code formation by suppressing predictable spikes, which actively decorrelates the excitatory population. The model predicts that only a small number of inhibitory cells is required relative to excitatory cells and that excitatory and inhibitory input should be correlated, in agreement with experimental findings in visual cortex. We also introduce a novel local learning rule that measures stimulus-dependent correlations between neurons to support "explaining away" mechanisms in neural coding.
authors.affiliation: Redwood Center for Theoretical Neuroscience, Helen Wills Neuroscience Institute, University of California, Berkeley, California 94720, USA. paul@pking.org
authors.forename: Paul D
authors.initials: PD
authors.lastname: King
authors.forename: Joel
authors.initials: J
authors.lastname: Zylberberg
authors.forename: Michael R
authors.initials: MR
authors.lastname: DeWeese
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D007395:Interneurons / Q000502:physiology*; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology*; D009434:Neural Pathways / Q000502:physiology; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D013223:Statistics as Topic; D014793:Visual Cortex / Q000166:cytology*
references.citation: Nat Rev Neurosci. 2005 Nov;6(11):877-88
references.pmid: 16261181
references.citation: J Neurosci. 1998 Dec 15;18(24):10464-72
references.pmid: 9852584
references.citation: Neuron. 2011 Oct 20;72(2):231-43
references.pmid: 22017986
references.citation: Cereb Cortex. 1999 Oct-Nov;9(7):683-96
references.pmid: 10554991
references.citation: Annu Rev Neurosci. 2011;34:89-103
references.pmid: 21438687
references.citation: Neuron. 2012 Jul 26;75(2):230-49
references.pmid: 22841309
references.citation: J Neurosci. 1996 May 15;16(10):3351-62
references.pmid: 8627371
references.citation: Neuroscience. 1983 Oct;10(2):261-94
references.pmid: 6633861
references.citation: J Neurosci. 1986 May;6(5):1284-301
references.pmid: 3711980
references.citation: Front Comput Neurosci. 2012 Jul 25;6:46
references.pmid: 22848199
references.citation: PLoS Biol. 2008 Jan;6(1):e16
references.pmid: 18232737
references.citation: Nat Neurosci. 2010 Mar;13(3):344-52
references.pmid: 20098420
references.citation: J Neurosci. 1998 Jun 1;18(11):4255-70
references.pmid: 9592103
references.citation: J Neurophysiol. 2000 Aug;84(2):909-26
references.pmid: 10938316
references.citation: Curr Opin Neurobiol. 2001 Aug;11(4):475-80
references.pmid: 11502395
references.citation: Nat Neurosci. 2000 Nov;3 Suppl:1178-83
references.pmid: 11127835
references.citation: Neural Comput. 2010 Jul;22(7):1812-36
references.pmid: 20235818
references.citation: Proc Natl Acad Sci U S A. 2000 Jul 5;97(14):8087-92
references.pmid: 10869422
references.citation: Science. 2010 Jan 29;327(5965):584-7
references.pmid: 20110506
references.citation: Nature. 2006 Feb 23;439(7079):978-82
references.pmid: 16495999
references.citation: Biol Cybern. 1990;64(2):165-70
references.pmid: 2291903
references.citation: J Neurosci. 2002 Apr 1;22(7):2904-15
references.pmid: 11923455
references.citation: J Comput Neurosci. 2012 Jun;32(3):425-41
references.pmid: 21938439
references.citation: PLoS Comput Biol. 2011 Oct;7(10):e1002250
references.pmid: 22046123
references.citation: Psychol Rev. 1954 May;61(3):183-93
references.pmid: 13167245
references.citation: Neuron. 2009 Nov 12;64(3):404-18
references.pmid: 19914188
references.citation: J Math Biol. 1982;15(3):267-73
references.pmid: 7153672
references.citation: Z Naturforsch C Biosci. 1981 Sep-Oct;36(9-10):910-2
references.pmid: 7303823
references.citation: J Neurosci. 2010 Mar 3;30(9):3531-43
references.pmid: 20203213
references.citation: J Physiol. 2007 Apr 1;580(Pt 1):149-69
references.pmid: 17234697
references.citation: J Neurosci. 2009 Feb 25;29(8):2355-70
references.pmid: 19244512
references.citation: Nat Rev Neurosci. 2004 Oct;5(10):793-807
references.pmid: 15378039
references.citation: J Neurosci. 2012 Mar 21;32(12):4179-95
references.pmid: 22442081
references.citation: J Comput Neurosci. 1994 Jun;1(1-2):89-107
references.pmid: 8792227
references.citation: J Neurosci. 1993 Jul;13(7):2801-20
references.pmid: 8331373
references.citation: Vision Res. 1997 Dec;37(23):3327-38
references.pmid: 9425547
references.citation: Neuron. 2011 Aug 11;71(3):542-54
references.pmid: 21835349
references.citation: Nat Neurosci. 1999 Jan;2(1):79-87
references.pmid: 10195184
references.citation: Curr Opin Neurobiol. 2010 Jun;20(3):340-6
references.pmid: 20307968
references.citation: PLoS Comput Biol. 2010 Apr 22;6(4):e1000757
references.pmid: 20421937
references.citation: Curr Biol. 2003 Mar 18;13(6):493-7
references.pmid: 12646132
references.citation: Neural Comput. 2008 Oct;20(10):2526-63
references.pmid: 18439138
references.citation: Neural Comput. 2009 May;21(5):1259-76
references.pmid: 19718815
references.citation: Annu Rev Neurosci. 2009;32:33-55
references.pmid: 19400721
references.citation: J Neurosci. 2003 Aug 27;23(21):7940-9
references.pmid: 12944525
references.citation: Notes Rec R Soc Lond. 1976 Jan;30(2):219-30
references.pmid: 12152632
references.citation: Nature. 1996 Jun 13;381(6583):607-9
references.pmid: 8637596
references.citation: J Comput Neurosci. 2007 Apr;22(2):135-46
references.pmid: 17053994
references.citation: Neuron. 2010 Jan 14;65(1):107-21
references.pmid: 20152117
references.citation: Front Neurosci. 2007 Oct 15;1(1):19-42
references.pmid: 18982117
references.citation: Neuron. 2010 Sep 9;67(5):858-71
references.pmid: 20826316
references.citation: Adv Neural Inf Process Syst. 2011 Dec;24:999-1007
references.pmid: 26273180
references.citation: Nature. 2006 Sep 7;443(7107):81-4
references.pmid: 16929304
references.citation: Vis Neurosci. 1992 Aug;9(2):181-97
references.pmid: 1504027
references.citation: PLoS Comput Biol. 2011 Aug;7(8):e1002123
references.pmid: 21887121
references.citation: Annu Rev Neurosci. 2000;23:441-71
references.pmid: 10845071
references.citation: Nat Neurosci. 2003 Dec;6(12):1300-8
references.pmid: 14625553
references.citation: Nature. 2005 Feb 24;433(7028):868-73
references.pmid: 15729343
references.citation: Vision Res. 1997 Dec;37(23):3311-25
references.pmid: 9425546
references.citation: Curr Opin Neurobiol. 2011 Oct;21(5):774-81
references.pmid: 21742484
references.citation: Neuron. 2004 Sep 30;44(1):23-30
references.pmid: 15450157
references.citation: PLoS Comput Biol. 2012;8(7):e1002594
references.pmid: 22807665
references.citation: Science. 2000 Feb 18;287(5456):1273-6
references.pmid: 10678835