What can neurons do for their brain? Communicate selectivity with bursts

Neurons deep in cortex interact with the environment extremely indirectly; the spikes they receive and produce are pre- and post-processed by millions of other neurons. This paper proposes two information-theoretic constraints guiding the production of spikes, that help ensure bursting activity deep in cortex relates meaningfully to events in the environment. First, neurons should emphasize selective responses with bursts. Second, neurons should propagate selective inputs by burst-firing in response to them. We show the constraints are necessary for bursts to dominate information-transfer within cortex, thereby providing a substrate allowing neurons to distribute credit amongst themselves. Finally, since synaptic plasticity degrades the ability of neurons to burst selectively, we argue that homeostatic regulation of synaptic weights is necessary, and that it is best performed offline during sleep.

[1]  Tai Sing Lee,et al.  Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[2]  Bruno A Olshausen,et al.  Sparse coding of sensory inputs , 2004, Current Opinion in Neurobiology.

[3]  B. McNaughton,et al.  Reactivation of hippocampal ensemble memories during sleep. , 1994, Science.

[4]  Giulio Tononi,et al.  Integrated Information in Discrete Dynamical Systems: Motivation and Theoretical Framework , 2008, PLoS Comput. Biol..

[5]  Mircea Steriade,et al.  The Intact and Sliced Brain , 2001 .

[6]  E T Rolls,et al.  Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. , 1995, Journal of neurophysiology.

[7]  H. Seung,et al.  Learning in Spiking Neural Networks by Reinforcement of Stochastic Synaptic Transmission , 2003, Neuron.

[8]  E. Izhikevich.  Solving the distal reward problem through linkage of STDP and dopamine signaling , 2007, BMC Neuroscience.

[9]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[10]  G. Edelman,et al.  Complexity and coherency: integrating information in the brain , 1998, Trends in Cognitive Sciences.

[11]  Dmitri B. Chklovskii,et al.  Wiring Optimization in Cortical Circuits , 2002, Neuron.

[12]  L. Abbott,et al.  Synaptic plasticity: taming the beast , 2000, Nature Neuroscience.

[13]  David Balduzzi,et al.  Metabolic Cost as an Organizing Principle for Cooperative Learning , 2012, Adv. Complex Syst..

[14]  J. Knott.  The organization of behavior: A neuropsychological theory , 1951 .

[15]  Rajesh P. N. Rao,et al.  Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning , 2001, Neural Computation.

[16]  G. Tononi,et al.  Sleep and synaptic homeostasis: a hypothesis , 2003, Brain Research Bulletin.

[17]  P. Penzes,et al.  Dendritic spine dynamics – a key role for kalirin-7 , 2008, Trends in Neurosciences.

[18]  D. Plenz,et al.  The organizing principles of neuronal avalanches: cell assemblies in the cortex? , 2007, Trends in Neurosciences.

[19]  G. Tononi,et al.  Local sleep and learning , 2004, Nature.

[20]  Giulio Tononi,et al.  Qualia: The Geometry of Integrated Information , 2009, PLoS Comput. Biol..

[21]  D. Johnston,et al.  Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997 .

[22]  G. Tononi,et al.  Cortical Firing and Sleep Homeostasis , 2009, Neuron.

[23]  Zhiping P. Pang,et al.  Distinct Neuronal Coding Schemes in Memory Revealed by Selective Erasure of Fast Synchronous Synaptic Transmission , 2012, Neuron.

[24]  D. Ulrich,et al.  Cellular mechanisms of burst firing‐mediated long‐term depression in rat neocortical pyramidal cells , 2007, The Journal of physiology.

[25]  F. Attneave,et al.  The Organization of Behavior: A Neuropsychological Theory , 1949 .

[26]  O. G. Selfridge,et al.  Pandemonium: a paradigm for learning , 1988 .

[27]  K. Maccorquodale.  Organization of Behavior : A Neuropsychological Theory , 1951 .

[28]  Thomas M. Cover,et al.  Elements of Information Theory: Cover/Elements of Information Theory, Second Edition , 2005 .

[29]  G. Tononi,et al.  Molecular and electrophysiological evidence for net synaptic potentiation in wake and depression in sleep , 2008, Nature Neuroscience.

[30]  Y. Dan,et al.  Spike timing-dependent plasticity: a Hebbian learning rule. , 2008, Annual review of neuroscience.

[31]  Wei Ji Ma,et al.  Bayesian inference with probabilistic population codes , 2006, Nature Neuroscience.

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

[33]  Y. Dan,et al.  Spike timing-dependent plasticity: from synapse to perception. , 2006, Physiological reviews.

[34]  John M. Beggs,et al.  Neuronal Avalanches in Neocortical Circuits , 2003, The Journal of Neuroscience.

[35]  J. Wickens,et al.  Timing is not Everything: Neuromodulation Opens the STDP Gate , 2010, Front. Syn. Neurosci..

[36]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[37]  Daniel Dennett,et al.  Darwin's “strange inversion of reasoning” , 2009, Proceedings of the National Academy of Sciences.

[38]  Sophie Denève,et al.  Bayesian Spiking Neurons I: Inference , 2008, Neural Computation.

[39]  G Tononi,et al.  Some considerations on sleep and neural plasticity. , 2001, Archives italiennes de biologie.

[40]  S. Mahadevan,et al.  Learning Theory , 2001 .

[41]  C. Koch,et al.  Invariant visual representation by single neurons in the human brain , 2005, Nature.

[42]  Robert A. Legenstein,et al.  A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback , 2008, PLoS Comput. Biol..

[43]  L. Abbott,et al.  Cortical Development and Remapping through Spike Timing-Dependent Plasticity , 2001, Neuron.

[44]  G Tononi,et al.  Changes in gene expression during the sleep-waking cycle: a new view of activating systems. , 1995, Archives italiennes de biologie.

[45]  Rajesh P. N. Rao.  Bayesian Computation in Recurrent Neural Circuits , 2004, Neural Computation.

[46]  S. Laughlin,et al.  An Energy Budget for Signaling in the Grey Matter of the Brain , 2001, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[47]  C. Asante,et al.  Spike-Timing Dependent Plasticity in Humans , 2011, The Journal of Neuroscience.