Modeling Inhibitory Interneurons in Efficient Sensory Coding Models

PLoS Computational Biology

Volumn 11, Issue 7, 2015, Pages

  Zhu, Mengchen ^a   Rozell, Christopher J ^b  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; CODES (SYMBOLS); DYNAMICAL SYSTEMS; INFERENCE ENGINES; NEURONS;

BIOLOGICAL COMPLEXITY; CODING MODELS; COMPUTATIONAL FRAMEWORK; DYNAMICAL SYSTEMS MODEL; INHIBITORY CELLS; INHIBITORY INTERNEURONS; MODELLING STUDIES; SENSORY CODING; SENSORY CORTICES; TUNING PROPERTIES;

CONVEX OPTIMIZATION;

ARTICLE; BAYES THEOREM; CONNECTOME; DALE LAW; INTERNEURON; MATHEMATICAL MODEL; MATHEMATICAL PARAMETERS; NEURAL CODING MODEL; PREDICTION; PROCESS OPTIMIZATION; SENSORY CORTEX; SENSORY STIMULATION; SIMULATION; SYSTEM ANALYSIS; ACTION POTENTIAL; ANIMAL; BIOLOGICAL MODEL; COMPUTER SIMULATION; HUMAN; NERVE CELL INHIBITION; NERVE CELL NETWORK; PHYSIOLOGY; VISION; VISUAL CORTEX;

ACTION POTENTIALS; ANIMALS; COMPUTER SIMULATION; HUMANS; INTERNEURONS; MODELS, NEUROLOGICAL; NERVE NET; NEURAL INHIBITION; VISUAL CORTEX; VISUAL PERCEPTION;

EID: 84938635448     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004353     Document Type: Article

References (67)

1. Isaacson J, Scanziani M, How Inhibition Shapes Cortical Activity. Neuron. 2011;72(2):231–243. Available from: http://www.sciencedirect.com/science/article/pii/S0896627311008798. doi: 10.1016/j.neuron.2011.09.027 22017986
2. Lee SH, Kwan AC, Zhang S, Phoumthipphavong V, Flannery JG, Masmanidis SC, et al. Activation of specific interneurons improves V1 feature selectivity and visual perception. Nature. 2012 Aug;488(7411):379–383. doi: 10.1038/nature11312 22878719
3. Wilson NR, Runyan CA, Wang FL, Sur M, Division and subtraction by distinct cortical inhibitory networks in vivo. Nature. 2012 Aug;488(7411):343–348. doi: 10.1038/nature11347 22878717
4. Olshausen BA, Field DJ, Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision research. 1997;37(23):3311–3325. doi: 10.1016/S0042-6989(97)00169-7 9425546
5. Rao RPN, Ballard DH, Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience. 1999;2:79–87. doi: 10.1038/4580 10195184
6. Schwartz O, Simoncelli EP, Natural signal statistics and sensory gain control. Nature neuroscience. 2001;4(8):819–825. doi: 10.1038/90526 11477428
7. King PD, Zylberberg J, DeWeese MR, Inhibitory Interneurons Decorrelate Excitatory Cells to Drive Sparse Code Formation in a Spiking Model of V1. The Journal of Neuroscience. 2013;33(13):5475–5485. doi: 10.1523/JNEUROSCI.4188-12.2013 23536063
8. Rasch MJ, Schuch K, Logothetis NK, Maass W, Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1. Journal of Neurophysiology. 2011;105(2):757–778. Available from: http://jn.physiology.org/content/105/2/757.abstract. doi: 10.1152/jn.00845.2009 21106898
9. Strata P, Harvey R, Dale’s principle. Brain Research Bulletin. 1999;50(5–6):349–350. Available from: http://www.sciencedirect.com/science/article/pii/S0361923099001008. doi: 10.1016/S0361-9230(99)00100-8 10643431
10. Meyer HS, Schwarz D, Wimmer VC, Schmitt AC, Kerr JND, Sakmann B, et al. Inhibitory interneurons in a cortical column form hot zones of inhibition in layers 2 and 5A. Proceedings of the National Academy of Sciences. 2011;108(40):16807–16812. Available from: http://www.pnas.org/content/108/40/16807.abstract. doi: 10.1073/pnas.1113648108
11. Meyer HS, Egger R, Guest JM, Foerster R, Reissl S, Oberlaender M, Cellular organization of cortical barrel columns is whisker-specific. Proceedings of the National Academy of Sciences. 2013;110(47):19113–19118. doi: 10.1073/pnas.1312691110
12. Markram H, Toledo-Rodriguez M, Wang Y, Gupta A, Silberberg G, Wu C, Interneurons of the neocortical inhibitory system. Nat Rev Neurosci. 2004 Oct;5(10):793–807. doi: 10.1038/nrn1519 15378039
13. Hirsch JA, Martinez LM, Circuits that build visual cortical receptive fields. Trends in neurosciences. 2006;29(1):30–39. doi: 10.1016/j.tins.2005.11.001 16309753
14. Lee WCA, Reid RC, Specificity and randomness: structure-function relationships in neural circuits. Current Opinion in Neurobiology. 2011;21(5):801–807. Available from: http://www.sciencedirect.com/science/article/pii/S0959438811001279. doi: 10.1016/j.conb.2011.07.004 21855320
15. Rao RPN, Olshausen BA, Lewicki MS, Probabilistic models of the brain: Perception and neural function. The MIT Press; 2002.
16. Boyd SP, Vandenberghe L, Convex optimization. Cambridge university press; 2004.
17. Olshausen BA, Field DJ, Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature. 1996;381(6583):607–609. doi: 10.1038/381607a0 8637596
18. Rehn M, Sommer FT, A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields. Journal of Computational Neuroscience. 2007;22(2):135–146. doi: 10.1007/s10827-006-0003-9 17053994
19. Zhu M, Rozell CJ, Visual Nonclassical Receptive Field Effects Emerge from Sparse Coding in a Dynamical System. PLoS Comput Biol. 2013 08;9(8):e1003191. doi: 10.1371/journal.pcbi.1003191 24009491
20. Olshausen BA, Field DJ, Sparse coding of sensory inputs. Current Opinion in Neurobiology. 2004;14(4):481–487. doi: 10.1016/j.conb.2004.07.007 15321069
21. Niven JE, Laughlin SB, Energy limitation as a selective pressure on the evolution of sensory systems. Journal of Experimental Biology. 2008;211(11):1792–1804. Available from: http://jeb.biologists.org/content/211/11/1792.abstract. doi: 10.1242/jeb.017574 18490395
22. Baum EB, Moody J, Wilczek F, Internal representations for associative memory. Biological Cybernetics. 1988;59(4):217–228. doi: 10.1007/BF00332910
23. Charles AS, Yap HL, Rozell CJ, Short term memory capacity in networks via the restricted isometry property. Neural Computation. 2014;26:1198–1235. doi: 10.1162/NECO_a_00590 24684446
24. Haider B, Krause MR, Duque A, Yu Y, Touryan J, Mazer JA, et al. Synaptic and Network Mechanisms of Sparse and Reliable Visual Cortical Activity during Nonclassical Receptive Field Stimulation. Neuron. 2010;65(1):107–121. doi: 10.1016/j.neuron.2009.12.005 20152117
25. Vinje WE, Gallant JL, Sparse coding and decorrelation in primary visual cortex during natural vision. Science. 2000;287(5456):1273–1276. doi: 10.1126/science.287.5456.1273 10678835
26. Wolfe J, Houweling AR, Brecht M, Sparse and powerful cortical spikes. Current Opinion in Neurobiology. 2010;p. 306–312. doi: 10.1016/j.conb.2010.03.006 20400290
27. Rozell CJ, Johnson DH, Baraniuk RG, Olshausen BA, Sparse coding via thresholding and local competition in neural circuits. Neural Computation. 2008;20(10):2526–2563. doi: 10.1162/neco.2008.03-07-486 18439138
28. Zylberberg J, Murphy JT, DeWeese MR, A Sparse Coding Model with Synaptically Local Plasticity and Spiking Neurons Can Account for the Diverse Shapes of V1 Simple Cell Receptive Fields. PLoS Comput Biol. 2011 10;7(10):e1002250. doi: 10.1371/journal.pcbi.1002250 22046123
29. Shapero S, Rozell CJ, Hasler P, Configurable hardware integrate and fire neurons for sparse approximation. Neural Networks. 2013;45(0):134–143. Available from: http://www.sciencedirect.com/science/article/pii/S089360801300097X. doi: 10.1016/j.neunet.2013.03.012 23582485
30. Hu T, Genkin A, Chklovskii DB, A Network of Spiking Neurons for Computing Sparse Representations in an Energy-Efficient Way. Neural Computation. 2012 Aug;24(11):2852–2872. doi: 10.1162/NECO_a_00353 22920853
31. Balavoine A, Romberg J, Rozell CJ, Convergence and Rate Analysis of Neural Networks for Sparse Approximation. IEEE Transactions on Neural Networks and Learning Systems. 2012 sept;23(9):1377–1389. doi: 10.1109/TNNLS.2012.2202400 24199030
32. Balavoine, A, Rozell, CJ, Romberg, J. Convergence of a Neural Network for Sparse Approximation using the Nonsmooth Łojasiewicz Inequality. In: Int. Joint Conf. Neural Netw. (IJCNN); 2013.
33. Charles AS, Garrigues P, Rozell CJ, A Common Network Architecture Efficiently Implements a Variety of Sparsity-Based Inference Problems. Neural Computation. 2012 Sep;24(12):3317–3339. doi: 10.1162/NECO_a_00372 22970876
34. Shapero S, Charles AS, Rozell CJ, Hasler P, Low power sparse approximation on reconfigurable analog hardware. Emerging and Selected Topics in Circuits and Systems, IEEE Journal on. 2012 sept;2(3):530–541. doi: 10.1109/JETCAS.2012.2214615
35. Shapero S, Zhu M, Hasler J, Rozell C, Optimal sparse approximation with integrate and fire neurons. International Journal of Neural Systems. 2014;24(05):1440001. doi: 10.1142/S0129065714400012 24875786
36. Ringach DL, Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex. Journal of Neurophysiology. 2002;88(1):455. 12091567
37. Hirsch JA, Martinez LM, Pillai C, Alonso JM, Wang Q, Sommer FT, Functionally distinct inhibitory neurons at the first stage of visual cortical processing. Nat Neurosci. 2003 Dec;6(12):1300–1308. doi: 10.1038/nn1152 14625553
38. Simoncelli EP, Vision and the statistics of the visual environment. Current opinion in Neurobiology. 2003;13(2):144–149. doi: 10.1016/S0959-4388(03)00047-3 12744966
39. Buonomano DV, Maass W, State-dependent computations: spatiotemporal processing in cortical networks. Nat Rev Neurosci. 2009 Feb;10(2):113–125. doi: 10.1038/nrn2558 19145235
40. Olshausen BA, Field DJ, How close are we to understanding V1? Neural Computation. 2005;17(8):1665–1699. doi: 10.1162/0899766054026639 15969914
41. Laughlin SB, Sejnowski TJ, Communication in neuronal networks. Science. 2003;301(5641):1870–1874. doi: 10.1126/science.1089662 14512617
42. Koulakov A, Rinberg D, Sparse Incomplete Representations: A Potential Role of Olfactory Granule Cells. Neuron. 2011;72(1):124–136. Available from: http://www.sciencedirect.com/science/article/pii/S0896627311006891. doi: 10.1016/j.neuron.2011.07.031 21982374
43. Lee AB, Pedersen KS, Mumford D, The nonlinear statistics of high-contrast patches in natural images. International Journal of Computer Vision. 2003;54(1–3):83–103. doi: 10.1023/A:1023705401078
44. Srivastava A, Lee AB, Simoncelli EP, Zhu SC, On Advances in Statistical Modeling of Natural Images. Journal of Mathematical Imaging and Vision. 2003;18:17–33. doi: 10.1023/A:1021889010444
45. Galarreta M, Erdélyi F, Szabó G, Hestrin S, Cannabinoid Sensitivity and Synaptic Properties of 2 GABAergic Networks in the Neocortex. Cerebral Cortex. 2008;18(10):2296–2305. Available from: http://cercor.oxfordjournals.org/content/18/10/2296.abstract. doi: 10.1093/cercor/bhm253 18203691
46. Candès E, Li X, Ma Y, Wright J, Robust Principal Component Analysis? Journal of the ACM—Association for Computing Machinery. 2011;58(3).
47. Lin Z, Chen M, Ma Y. The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices. ArXiv e-prints. 2010 Sep;p.
48. Chandrasekaran V, Sanghavi S, Parrilo PA, Willsky AS, Rank-sparsity incoherence for matrix decomposition. SIAM Journal on Optimization. 2011;21(2):572–596. doi: 10.1137/090761793
49. Charles A, Ahmed A, Joshi A, Conover S, Turnes C, Davenport M. Cleaning up toxic waste: removing nefarious contributions to recommendation systems. In: ICASSP 2013; 2013.
50. Lochmann T, Ernst UA, Denève S, Perceptual Inference Predicts Contextual Modulations of Sensory Responses. The Journal of Neuroscience. 2012;32(12):4179–4195. Available from: http://www.jneurosci.org/content/32/12/4179.abstract. doi: 10.1523/JNEUROSCI.0817-11.2012 22442081
51. Boerlin M, Machens CK, Denève S, Predictive coding of dynamical variables in balanced spiking networks. PLoS computational biology. 2013;9(11):e1003258. doi: 10.1371/journal.pcbi.1003258 24244113
52. Hofer SB, Ko H, Pichler B, Vogelstein J, Ros H, Zeng H, et al. Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex. Nat Neurosci. 2011 Aug;14(8):1045–1052. doi: 10.1038/nn.2876 21765421
53. Packer AM, Yuste R, Dense, Unspecific Connectivity of Neocortical Parvalbumin-Positive Interneurons: A Canonical Microcircuit for Inhibition? The Journal of Neuroscience. 2011;31(37):13260–13271. doi: 10.1523/JNEUROSCI.3131-11.2011 21917809
54. Ma Wp, Liu Bh, Li Yt, Huang ZJ, Zhang LI, Tao HW, Visual representations by cortical somatostatin inhibitory neurons—selective but with weak and delayed responses. The Journal of Neuroscience. 2010;30(43):14371–14379. doi: 10.1523/JNEUROSCI.3248-10.2010 20980594
55. Nowak LG, Sanchez-Vives MV, McCormick DA, Lack of Orientation and Direction Selectivity in a Subgroup of Fast-Spiking Inhibitory Interneurons: Cellular and Synaptic Mechanisms and Comparison with Other Electrophysiological Cell Types. Cerebral Cortex. 2008;18(5):1058–1078. Available from: http://cercor.oxfordjournals.org/content/18/5/1058.abstract. doi: 10.1093/cercor/bhm137 17720684
56. Adesnik H, Bruns W, Taniguchi H, Huang ZJ, Scanziani M, A neural circuit for spatial summation in visual cortex. Nature. 2012 Oct;490(7419):226–231. doi: 10.1038/nature11526 23060193
57. Song S, Sjöström PJ, Reigl M, Nelson S, Chklovskii DB, Highly Nonrandom Features of Synaptic Connectivity in Local Cortical Circuits. PLoS Biol. 2005 03;3(3):e68. doi: 10.1371/journal.pbio.0030068 15737062
58. Ikeda K, Bekkers JM, Autapses. Current Biology. 2006;16(9):R308–R308. Available from: http://www.sciencedirect.com/science/article/pii/S0960982206014187. doi: 10.1016/j.cub.2006.03.085 16682332
59. Larkum M, A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends in Neurosciences. 2013;36(3):141–151. Available from: http://www.sciencedirect.com/science/article/pii/S0166223612002032. doi: 10.1016/j.tins.2012.11.006 23273272
60. Sámano C, Cifuentes F, Morales MA, Neurotransmitter segregation: Functional and plastic implications. Progress in Neurobiology. 2012;97(3):277–287. Available from: http://www.sciencedirect.com/science/article/pii/S0301008212000524. doi: 10.1016/j.pneurobio.2012.04.004 22531669
61. Runyan CA, Schummers J, Wart AV, Kuhlman SJ, Wilson NR, Huang ZJ, et al. Response Features of Parvalbumin-Expressing Interneurons Suggest Precise Roles for Subtypes of Inhibition in Visual Cortex. Neuron. 2010;67(5):847–857. Available from: http://www.sciencedirect.com/science/article/B6WSS-50YV1JT-M/2/785cac652dc78dc532f215b1b43a4f2d. doi: 10.1016/j.neuron.2010.08.006 20826315
62. Huberman AD, Niell CM, What can mice tell us about how vision works? Trends in Neurosciences. 2011;34(9):464–473. Available from: http://www.sciencedirect.com/science/article/pii/S0166223611001068. doi: 10.1016/j.tins.2011.07.002 21840069
63. Pfeffer CK, Xue M, He M, Huang ZJ, Scanziani M, Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat Neurosci. 2013 Aug;16(8):1068–1076. doi: 10.1038/nn.3446 23817549
64. Silberberg G, Polysynaptic subcircuits in the neocortex: spatial and temporal diversity. Current Opinion in Neurobiology. 2008;18(3):332–337. Signalling mechanisms. Available from: http://www.sciencedirect.com/science/article/pii/S0959438808000822. doi: 10.1016/j.conb.2008.08.009 18801433
65. DiMattina C, Zhang K, How to Modify a Neural Network Gradually Without Changing Its Input-Output Functionality. Neural Computation. 2009 Oct;22(1):1–47. doi: 10.1162/neco.2009.05-08-781
66. Hertz J, Krogh A, Palmer RG, Introduction to the theory of neural computation. vol. 1. Westview press; 1991.
67. Boyd S, Parikh N, Chu E, Peleato B, Eckstein J, Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning. 2011;3(1):1–122. doi: 10.1561/2200000016