The information efficacy of a synapse

We provide a functional measure, the synaptic information efficacy (SIE), to assess the impact of synaptic input on spike output. SIE is the mutual information shared by the presynaptic input and postsynaptic output spike trains. To estimate SIE we used a method based on compression algorithms. This method detects the effect of a single synaptic input on the postsynaptic spike output in the presence of massive background synaptic activity in neuron models of progressively increasing realism. SIE increased with increases either in time locking between the input synapse activity and the output spike or in the average number of output spikes. SIE depended on the context in which the synapse operates. We also measured SIE experimentally. Systematic exploration of the effect of synaptic and dendritic parameters on the SIE offers a fresh look at the synapse as a communication device and a quantitative measure of how much the dendritic synapse informs the axon.

[1]  F. Golla The Central Nervous System , 1960, Nature.

[2]  Wilfrid Rall,et al.  Theoretical significance of dendritic trees for neuronal input-output relations , 1964 .

[3]  W. Rall Distinguishing theoretical synaptic potentials computed for different soma-dendritic distributions of synaptic input. , 1967, Journal of neurophysiology.

[4]  R. Iansek,et al.  The amplitude, time course and charge of unitary excitatory post‐synaptic potentials evoked in spinal motoneurone dendrites , 1973, The Journal of physiology.

[5]  J Rinzel,et al.  Transient response in a dendritic neuron model for current injected at one branch. , 1974, Biophysical journal.

[6]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[7]  N T Carnevale,et al.  Electrophysiological characterization of remote chemical synapses. , 1982, Journal of neurophysiology.

[8]  D. Oertel Synaptic responses and electrical properties of cells in brain slices of the mouse anteroventral cochlear nucleus , 1983, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[9]  E E Fetz,et al.  Relation between shapes of post‐synaptic potentials and changes in firing probability of cat motoneurones , 1983, The Journal of physiology.

[10]  D. O. Hebb,et al.  The organization of behavior , 1988 .

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

[12]  C. Koch,et al.  Synaptic background activity influences spatiotemporal integration in single pyramidal cells. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[13]  P. Brodal The Central Nervous System , 1992 .

[14]  C Blakemore,et al.  Modulation of EPSP shape and efficacy by intrinsic membrane conductances in rat neocortical pyramidal neurons in vitro. , 1993, The Journal of physiology.

[15]  Eberhard E. Fetz,et al.  Effects of Input Synchrony on the Firing Rate of a Three-Conductance Cortical Neuron Model , 1994, Neural Computation.

[16]  Marius Usher,et al.  The Effect of Synchronized Inputs at the Single Neuron Level , 1994, Neural Computation.

[17]  Frans M. J. Willems,et al.  The context-tree weighting method: basic properties , 1995, IEEE Trans. Inf. Theory.

[18]  R. Malinow,et al.  Activation of postsynaptically silent synapses during pairing-induced LTP in CA1 region of hippocampal slice , 1995, Nature.

[19]  Stefano Panzeri,et al.  The Upward Bias in Measures of Information Derived from Limited Data Samples , 1995, Neural Computation.

[20]  John P. Miller,et al.  Broadband neural encoding in the cricket cereal sensory system enhanced by stochastic resonance , 1996, Nature.

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

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

[23]  T. Sejnowski,et al.  The Monetary Transmission Mechanism in the United Kingdom: Pass-Through and Policy Rules. manuscript , 1996 .

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

[25]  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.

[26]  H. Markram,et al.  Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.

[27]  Nicholas T. Carnevale,et al.  The NEURON Simulation Environment , 1997, Neural Computation.

[28]  D. Contreras,et al.  Intracellular and computational characterization of the intracortical inhibitory control of synchronized thalamic inputs in vivo. , 1997, Journal of neurophysiology.

[29]  M. Häusser,et al.  Tonic Synaptic Inhibition Modulates Neuronal Output Pattern and Spatiotemporal Synaptic Integration , 1997, Neuron.

[30]  E. De Schutter,et al.  Dendritic voltage and calcium-gated channels amplify the variability of postsynaptic responses in a Purkinje cell model. , 1998, Journal of neurophysiology.

[31]  Y. Frégnac,et al.  Visual input evokes transient and strong shunting inhibition in visual cortical neurons , 1998, Nature.

[32]  B. Sakmann,et al.  A new cellular mechanism for coupling inputs arriving at different cortical layers , 1999, Nature.

[33]  R. G. Morris D.O. Hebb: The Organization of Behavior, Wiley: New York; 1949 , 1999, Brain Research Bulletin.

[34]  J. Magee Dendritic Ih normalizes temporal summation in hippocampal CA1 neurons , 1999, Nature Neuroscience.

[35]  Jeffrey C. Magee,et al.  Dendritic I h normalizes temporal summation in hippocampal CA 1 neurons , 1999 .

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

[37]  J. Magee Dendritic lh normalizes temporal summation in hippocampal CA1 neurons. , 1999, Nature neuroscience.

[38]  A. Destexhe,et al.  Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. , 1999, Journal of neurophysiology.

[39]  D. Johnston,et al.  Voltage-dependent properties of dendrites that eliminate location-dependent variability of synaptic input. , 1999, Journal of neurophysiology.

[40]  C. Koch,et al.  Encoding of visual information by LGN bursts. , 1999, Journal of neurophysiology.

[41]  B. Connors,et al.  Efficacy of Thalamocortical and Intracortical Synaptic Connections Quanta, Innervation, and Reliability , 1999, Neuron.

[42]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[43]  I Segev,et al.  Untangling dendrites with quantitative models. , 2000, Science.

[44]  J. Magee,et al.  Somatic EPSP amplitude is independent of synapse location in hippocampal pyramidal neurons , 2000, Nature Neuroscience.

[45]  Richard Miles,et al.  EPSP Amplification and the Precision of Spike Timing in Hippocampal Neurons , 2000, Neuron.

[46]  M. Häusser,et al.  Dendritic coincidence detection of EPSPs and action potentials , 2001, Nature Neuroscience.

[47]  Idan Segev,et al.  Synaptic scaling in vitro and in vivo , 2001, Nature Neuroscience.

[48]  A. Reyes,et al.  Influence of dendritic conductances on the input-output properties of neurons. , 2001, Annual review of neuroscience.

[49]  Christof Koch,et al.  Detecting and Estimating Signals over Noisy and Unreliable Synapses: Information-Theoretic Analysis , 2001, Neural Computation.

[50]  S Panzeri,et al.  Temporal correlations and neural spike train entropy. , 2001, Physical review letters.

[51]  Henry Markram,et al.  Coding of temporal information by activity-dependent synapses. , 2002, Journal of neurophysiology.

[52]  S Yamada,et al.  Information theoretic analysis of action potential trains , 2004, Biological Cybernetics.

[53]  John Rinzel,et al.  Intrinsic and network rhythmogenesis in a reduced traub model for CA3 neurons , 2004, Journal of Computational Neuroscience.