nips nips2012 nips2012-347 nips2012-347-reference knowledge-graph by maker-knowledge-mining
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Author: David Balduzzi, Michel Besserve
Abstract: This paper suggests a learning-theoretic perspective on how synaptic plasticity benefits global brain functioning. We introduce a model, the selectron, that (i) arises as the fast time constant limit of leaky integrate-and-fire neurons equipped with spiking timing dependent plasticity (STDP) and (ii) is amenable to theoretical analysis. We show that the selectron encodes reward estimates into spikes and that an error bound on spikes is controlled by a spiking margin and the sum of synaptic weights. Moreover, the efficacy of spikes (their usefulness to other reward maximizing selectrons) also depends on total synaptic strength. Finally, based on our analysis, we propose a regularized version of STDP, and show the regularization improves the robustness of neuronal learning when faced with multiple stimuli. 1
[1] Friston K, Kilner J, Harrison L: A free energy principle for the brain. J. Phys. Paris 2006, 100:70–87.
[2] Rosenblatt F: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 1958, 65(6):386–408.
[3] Rumelhart DE, Hinton GE, Williams RJ: Learning representations by back-propagating errors. Nature 1986, 323:533–536.
[4] Hinton G, Osindero S, Teh YW: A Fast Learning Algorithm for Deep Belief Nets. Neural Computation 2006, 18:1527–1554. 8
[5] Song S, Miller KD, Abbott LF: Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience 2000, 3(9).
[6] Seung HS: Learning in Spiking Neural Networks by Reinforcement of Stochastic Synaptic Transmission. Neuron 2003, 40(1063-1073).
[7] Bohte SM, Mozer MC: Reducing spike train variability: A computational theory of spike-timing dependent plasticity. In Advances in Neural Information Processing Systems (NIPS) 2005.
[8] Legenstein R, Maass W: A criterion for the convergence of learning with spike timing dependent plasticity. In Advances in Neural Information Processing Systems (NIPS) 2006.
[9] Buesing L, Maass W: Simplified rules and theoretical analysis for information bottleneck optimization and PCA with spiking neurons. In Adv in Neural Information Processing Systems (NIPS) 2007.
[10] Legenstein R, Pecevski D, Maass W: Theoretical analysis of learning with reward-modulated spiketiming-dependent plasticity. In Advances in Neural Information Processing Systems (NIPS) 2008.
[11] Tishby N, Pereira F, Bialek W: The information bottleneck method. In Proc. of the 37-th Annual Allerton Conference on Communication, Control and Computing. Edited by Hajek B, Sreenivas R 1999.
[12] Balduzzi D, Tononi G: What can neurons do for their brain? Communicate selectivity with spikes. To appear in Theory in Biosciences 2012.
[13] Balduzzi D, Ortega PA, Besserve M: Metabolic cost as an organizing principle for cooperative learning. Under review, 2012.
[14] Nere A, Olcese U, Balduzzi D, Tononi G: A neuromorphic architecture for object recognition and motion anticipation using burst-STDP. PLoS One 2012, 7(5):e36958.
[15] Schmiedt J, Albers C, Pawelzik K: Spike timing-dependent plasticity as dynamic filter. In Advances in Neural Information Processing Systems (NIPS) 2010.
[16] Anthony M, Bartlett PL: Neural Network Learning: Theoretical Foundations. Cambridge Univ Press 1999.
[17] Freund Y, Schapire RE: Large Margin Classification Using the Perceptron Algorithm. Machine Learning 1999, 37(3):277–296.
[18] Ecker AS, Berens P, Keliris GA, Bethge M, Logothetis NK, Tolias AS: Decorrelated neuronal firing in cortical microcircuits. Science 2010, 327(5965):584–7.
[19] Dan Y, Poo MM: Spike timing-dependent plasticity of neural circuits. Neuron 2004, 44:23–30.
[20] Gerstner W: Time structure of the activity in neural network models. Phys. Rev. E 1995, 51:738–758.
[21] Geman S, Bienenstock E, Doursat R: Neural Networks and the Bias/Variance Dilemma. Neural Comp 1992, 4:1–58.
[22] Freund Y, Schapire RE: Experiments with a New Boosting Algorithm. In Machine Learning: Proceedings of the Thirteenth International Conference 1996.
[23] Schapire RE, Freund Y, Bartlett P, Lee WS: Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods. The Annals of Statistics 1998, 26(5).
[24] Boucheron S, Bousquet O, Lugosi G: Theory of classification: A survey of some recent advances. ESAIM: PS 2005, 9:323–375.
[25] Hasenstaub A, Otte S, Callaway E, Sejnowski TJ: Metabolic cost as a unifying principle governing neuronal biophysics. Proc Natl Acad Sci U S A 2010, 107(27):12329–34.
[26] Fusi S, Drew P, Abbott L: Cascade Models of Synaptically Stored Memories. Neuron 2005, 45:599– 611.
[27] Fusi S, Abbott L: Limits on the memory storage capacity of bounded synapses. Nature Neuroscience 2007, 10(4):485–493.
[28] Tononi G, Cirelli C: Sleep function and synaptic homeostasis. Sleep Med Rev 2006, 10:49–62.
[29] Vyazovskiy VV, Cirelli C, Pfister-Genskow M, Faraguna U, Tononi G: Molecular and electrophysiological evidence for net synaptic potentiation in wake and depression in sleep. Nat Neurosci 2008, 11(2):200–8.
[30] Vyazovskiy VV, Olcese U, Lazimy Y, Faraguna U, Esser SK, Williams JC, Cirelli C, Tononi G: Cortical firing and sleep homeostasis. Neuron 2009, 63(6):865–78.
[31] Maret S, Faraguna U, Nelson AB, Cirelli C, Tononi G: Sleep and waking modulate spine turnover in the adolescent mouse cortex. Nat Neurosci 2011, 14(11):1418–1420.
[32] Masquelier T, Guyonneau, R and Thorpe SJ: Competitive STDP-Based Spike Pattern Learning. Neural Computation 2009, 21(5):1259–1276.
[33] Roelfsema PR, van Ooyen A: Attention-gated reinforcement learning of internal representations for classification. Neural Comput 2005, 17(10):2176–2214. 9