Correlation Between Uncoupled Conductance-Based Integrate-and-Fire Neurons Due to Common and Synchronous Presynaptic Firing

We investigate the firing characteristics of conductance-based integrate- and-fire neurons and the correlation of firing for uncoupled pairs of neurons as a result of common input and synchronous firing of multiple synaptic inputs. Analytical approximations are derived for the moments of the steady state potential and the effective time constant. We show that postsynaptic firing barely depends on the correlation between inhibitory inputs; only the inhibitory firing rate matters. In contrast, both the degree of synchrony and the firing rate of excitatory inputs are relevant. A coefficient of variation CV > 1 can be attained with low inhibitory firing rates and (Poisson-modulated) synchronized excitatory synaptic input, where both the number of presynaptic neurons in synchronous firing assemblies and the synchronous firing rate should be sufficiently large. The correlation in firing of a pair of uncoupled neurons due to common excitatory input is initially increased for increasing firing rates of independent inhibitory inputs but decreases for large inhibitory firing rates. Common inhibitory input to a pair of uncoupled neurons barely induces correlated firing, but amplifies the effect of common excitation. Synchronous firing assemblies in the common input further enhance the correlation and are essential to attain experimentally observed correlation values. Since uncorrelated common input (i.e., common input by neurons, which do not fire in synchrony) cannot induce sufficient postsynaptic correlation, we conclude that lateral couplings are essential to establish clusters of synchronously firing neurons.

[1]  Henry C. Tuckwell,et al.  Introduction to theoretical neurobiology , 1988 .

[2]  Prof. Dr. Dr. Valentino Braitenberg,et al.  Cortex: Statistics and Geometry of Neuronal Connectivity , 1998, Springer Berlin Heidelberg.

[3]  A. Aertsen,et al.  Dynamics of neuronal interactions in monkey cortex in relation to behavioural events , 1995, Nature.

[4]  E. Fetz,et al.  Synaptic Interactions between Primate Precentral Cortex Neurons Revealed by Spike-Triggered Averaging of Intracellular Membrane Potentials In Vivo , 1996, The Journal of Neuroscience.

[5]  A. Aertsen,et al.  Spike synchronization and rate modulation differentially involved in motor cortical function. , 1997, Science.

[6]  John H. R. Maunsell,et al.  On the relationship between synaptic input and spike output jitter in individual neurons. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Wulfram Gerstner,et al.  Extracting Oscillations: Neuronal Coincidence Detection with Noisy Periodic Spike Input , 1998, Neural Computation.

[8]  Wulfram Gerstner Populations of spiking neurons , 1999 .

[9]  Stefan Rotter,et al.  Higher-Order Statistics of Input Ensembles and the Response of Simple Model Neurons , 2003, Neural Computation.

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

[11]  W. Singer,et al.  In search of common foundations for cortical computation , 1997, Behavioral and Brain Sciences.

[12]  Vreeswijk,et al.  Partial synchronization in populations of pulse-coupled oscillators. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[13]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[14]  Reinhard Eckhorn,et al.  Parallel processing by a homogeneous group of coupled model neurons can enhance, reduce and generate signal correlations , 1997, Biological Cybernetics.

[15]  W. Newsome,et al.  The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding , 1998, The Journal of Neuroscience.

[16]  Jianfeng Feng,et al.  Coefficient of variation of interspike intervals greater than 0.5. How and when? , 1999, Biological Cybernetics.

[17]  K. Stratford,et al.  Synaptic transmission between individual pyramidal neurons of the rat visual cortex in vitro , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[19]  Moshe Abeles,et al.  Synfire chain in a balanced network , 2002, Neurocomputing.

[20]  G B Ermentrout,et al.  Fine structure of neural spiking and synchronization in the presence of conduction delays. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Christof Koch,et al.  Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series) , 1998 .

[22]  T. Geisel,et al.  Delay-induced multistable synchronization of biological oscillators , 1998 .

[23]  Ad Aertsen,et al.  Stable propagation of synchronous spiking in cortical neural networks , 1999, Nature.

[24]  Kenneth D. Miller,et al.  Physiological Gain Leads to High ISI Variability in a Simple Model of a Cortical Regular Spiking Cell , 1997, Neural Computation.

[25]  Jianfeng Feng,et al.  Impact of Correlated Inputs on the Output of the Integrate-and-Fire Model , 2000, Neural Computation.

[26]  J Feng,et al.  Synchronization due to common pulsed input in Stein's model. , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.