Broadband Coding with Dynamic Synapses

Short-term synaptic plasticity (STP) can significantly alter the amplitudes of synaptic responses in ways that depend on presynaptic history. Thus, it is widely assumed that STP acts as a filter for specific patterns of presynaptic inputs, and as a result can play key roles in neuronal information processing. To evaluate this assumption and directly quantify the effects of STP on information transmission, we consider a population of independent synaptic inputs to a model neuron. We show using standard information theoretic approaches that the changes in synaptic response amplitude resulting from STP interact with the related effects on fluctuations in membrane conductance, such that information transmission is broadband (no frequency-dependent filtering occurs), regardless of whether synaptic depression or facilitation dominates. Interestingly, this broadband transmission is preserved in the postsynaptic spike train as long as the postsynaptic neuron's baseline firing rate is relatively high; in contrast, low baseline firing rates lead to STP-dependent effects. Thus, background inputs that control the firing state of a postsynaptic neuron can gate the effects of STP on information transmission.

[1]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[2]  L. Maler,et al.  Plastic and Nonplastic Pyramidal Cells Perform Unique Roles in a Network Capable of Adaptive Redundancy Reduction , 2004, Neuron.

[3]  Christof Koch,et al.  Subthreshold voltage noise of rat neocortical pyramidal neurones , 2005, The Journal of physiology.

[4]  Albert Compte,et al.  Integrated Mechanisms of Anticipation and Rate-of-Change Computations in Cortical Circuits , 2007, PLoS Comput. Biol..

[5]  A. V. Holden,et al.  The estimation of the frequency response function of a mechanoreceptor , 1972, Kybernetik.

[6]  R. L. Stratonovich,et al.  Topics in the theory of random noise , 1967 .

[7]  Kelvin E. Jones,et al.  Neuronal variability: noise or part of the signal? , 2005, Nature Reviews Neuroscience.

[8]  E. Fortune,et al.  Short-term synaptic plasticity as a temporal filter , 2001, Trends in Neurosciences.

[9]  G. L. Masson,et al.  Feedback inhibition controls spike transfer in hybrid thalamic circuits , 2002, Nature.

[10]  W. Regehr,et al.  Short-term synaptic plasticity. , 2002, Annual review of physiology.

[11]  Leonard Maler,et al.  Dynamics of electrosensory feedback: short-term plasticity and inhibition in a parallel fiber pathway. , 2002, Journal of neurophysiology.

[12]  Anatol C. Kreitzer,et al.  Interplay between Facilitation, Depression, and Residual Calcium at Three Presynaptic Terminals , 2000, The Journal of Neuroscience.

[13]  C. Stevens,et al.  Excitatory and Feed-Forward Inhibitory Hippocampal Synapses Work Synergistically as an Adaptive Filter of Natural Spike Trains , 2006, PLoS biology.

[14]  Wulfram Gerstner,et al.  Phenomenological models of synaptic plasticity based on spike timing , 2008, Biological Cybernetics.

[15]  M. Tsodyks,et al.  Synaptic Theory of Working Memory , 2008, Science.

[16]  C. Gardiner Handbook of Stochastic Methods , 1983 .

[17]  Frances S. Chance,et al.  Gain Modulation from Background Synaptic Input , 2002, Neuron.

[18]  L. Abbott,et al.  Redundancy Reduction and Sustained Firing with Stochastic Depressing Synapses , 2002, The Journal of Neuroscience.

[19]  Leonard Maler,et al.  Synaptic dynamics on different time scales in a parallel fiber feedback pathway of the weakly electric fish. , 2004, Journal of neurophysiology.

[20]  Bruce W. Knight,et al.  Dynamics of Encoding in a Population of Neurons , 1972, The Journal of general physiology.

[21]  A. Aertsen,et al.  Neuronal Integration of Synaptic Input in the Fluctuation-Driven Regime , 2004, The Journal of Neuroscience.

[22]  L. Abbott,et al.  Synaptic Depression and Cortical Gain Control , 1997, Science.

[23]  J. Lisman Bursts as a unit of neural information: making unreliable synapses reliable , 1997, Trends in Neurosciences.

[24]  Brent Doiron,et al.  Subtractive and Divisive Inhibition: Effect of Voltage-Dependent Inhibitory Conductances and Noise , 2001, Neural Computation.

[25]  W. Catterall,et al.  Regulation of Presynaptic CaV2.1 Channels by Ca2+ Sensor Proteins Mediates Short-Term Synaptic Plasticity , 2008, Neuron.

[26]  J Overbaugh,et al.  Lymphokines modulate the growth and survival of thymic tumor cells containing a novel feline leukemia virus/Notch2 variant. , 1999, Veterinary immunology and immunopathology.

[27]  Brent Doiron,et al.  Parallel Processing of Sensory Input by Bursts and Isolated Spikes , 2004, The Journal of Neuroscience.

[28]  Benjamin Lindner,et al.  Superposition of many independent spike trains is generally not a Poisson process. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  E. Marder,et al.  Plasticity in single neuron and circuit computations , 2004, Nature.

[30]  H. Markram,et al.  Redistribution of synaptic efficacy between neocortical pyramidal neurons , 1996, Nature.

[31]  Henry Markram,et al.  Spike frequency adaptation and neocortical rhythms. , 2002, Journal of neurophysiology.

[32]  Gregoire Nicolis,et al.  Stochastic resonance , 2007, Scholarpedia.

[33]  Wiesenfeld,et al.  Theory of stochastic resonance. , 1989, Physical review. A, General physics.

[34]  H. Markram,et al.  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[35]  A. Longtin Stochastic resonance in neuron models , 1993 .