Effect on information transfer of synaptic pruning driven by spike-timing-dependent plasticity.

Spike-timing-dependent plasticity (STDP) is an important driving force of self-organization in neural systems. With properly chosen input signals, STDP can yield a synaptic pruning process, whose functional role needs to be further investigated. We explore this issue from an information theoretic standpoint. Temporally correlated stimuli are introduced to neurons of an input layer. Then synapses on the dendrite, and thus the receptive field, of an output neuron are refined by STDP. The mutual information between input and output spike trains is calculated with the context tree method. The results show that synapse removal can enhance information transfer, i.e., that "less can be more" under certain constraints that stress the balance between potentiation and depression dictated by the parameters of the STDP rule, as well as the temporal scale of the input correlation.

[1]  Adilson E Motter,et al.  Network synchronization landscape reveals compensatory structures, quantization, and the positive effect of negative interactions , 2009, Proceedings of the National Academy of Sciences.

[2]  Seunghwan Kim,et al.  Self-organized criticality and scale-free properties in emergent functional neural networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Andrew Philippides,et al.  Reconciling the STDP and BCM Models of Synaptic Plasticity in a Spiking Recurrent Neural Network , 2010, Neural Computation.

[4]  Alexander Borst,et al.  Information theory and neural coding , 1999, Nature Neuroscience.

[5]  Wolf Singer,et al.  The brain as a self-organizing system , 2004, European archives of psychiatry and neurological sciences.

[6]  G. Bi,et al.  Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.

[7]  William Bialek,et al.  Entropy and Information in Neural Spike Trains , 1996, cond-mat/9603127.

[8]  Jonathon Shlens,et al.  Estimating Information Rates with Confidence Intervals in Neural Spike Trains , 2007, Neural Computation.

[9]  Naoki Masuda,et al.  Self-organization of feed-forward structure and entrainment in excitatory neural networks with spike-timing-dependent plasticity. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Sander M. Bohte,et al.  Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity , 2007, Neural Computation.

[11]  Nicolas Brunel,et al.  Fast Global Oscillations in Networks of Integrate-and-Fire Neurons with Low Firing Rates , 1999, Neural Computation.

[12]  Daniel D. Lee,et al.  Equilibrium properties of temporally asymmetric Hebbian plasticity. , 2000, Physical review letters.

[13]  Jeff W. Lichtman,et al.  Axon Branch Removal at Developing Synapses by Axosome Shedding , 2004, Neuron.

[14]  L. Abbott,et al.  Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.

[15]  J. Jost,et al.  Evolution of network structure by temporal learning , 2008, 0811.4306.

[16]  G. Bi,et al.  Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.

[17]  Areejit Samal,et al.  STDP-driven networks and the C. elegans neuronal network , 2010, 1004.5060.

[18]  Gal Chechik.  Spike timing dependent plasticity and mutual information in spiking neurons , 2001, Neurocomputing.

[19]  Jonathon Shlens,et al.  Estimating Entropy Rates with Bayesian Confidence Intervals , 2005, Neural Computation.

[20]  W. Gerstner,et al.  Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Jean-Pascal Pfister,et al.  STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission , 2010, Front. Comput. Neurosci..

[22]  R. Reid,et al.  Temporal Coding of Visual Information in the Thalamus , 2000, The Journal of Neuroscience.