Neural populations can induce reliable postsynaptic currents without observable spike rate changes or precise spike timing.

Fine temporal patterns of firing in much of the brain are highly irregular. In some circuits, the precise pattern of irregularity contains information beyond that contained in mean firing rates. However, the capacity of neural circuits to use this additional information for computational purposes is not well understood. Here we employ computational methods to show that an ensemble of neurons firing at a constant mean rate can induce arbitrarily chosen temporal current patterns in postsynaptic cells. If the presynaptic neurons fire with nearly uniform interspike intervals, then current patterns are sensitive to variations in spike timing. But irregular, Poisson-like firing can drive current patterns robustly, even if spike timing varies by tens of milliseconds from trial to trial. Notably, irregular firing patterns can drive useful patterns of current even if they are so variable that several hundred repeated experimental trials would be needed to distinguish them from random firing. Together, these results describe an unrestrictive set of conditions in which postsynaptic cells might exploit virtually any information contained in spike timing. We speculate as to how this capability may underlie an extension of population coding to the temporal domain.

[1]  R. Reid,et al.  Precise Firing Events Are Conserved across Neurons , 2002, The Journal of Neuroscience.

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

[3]  Adam Kepecs,et al.  Information encoding and computation with spikes and bursts , 2003, Network.

[4]  J. Eggermont,et al.  Cross-correlation and joint spectro-temporal receptive field properties in auditory cortex. , 2005, Journal of neurophysiology.

[5]  Javier F. Medina,et al.  Timing Mechanisms in the Cerebellum: Testing Predictions of a Large-Scale Computer Simulation , 2000, The Journal of Neuroscience.

[6]  Idan Segev,et al.  Methods in Neuronal Modeling , 1988 .

[7]  Frank C. Hoppensteadt,et al.  Bursts as a unit of neural information: selective communication via resonance , 2003, Trends in Neurosciences.

[8]  Ehud Ahissar,et al.  Temporal-Code to Rate-Code Conversion by Neuronal Phase-Locked Loops , 1998, Neural Computation.

[9]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[10]  Michael Rudolph,et al.  A Fast-Conducting, Stochastic Integrative Mode for Neocortical Neurons InVivo , 2003, The Journal of Neuroscience.

[11]  Paul F. M. J. Verschure,et al.  Decoding a Temporal Population Code , 2004, Neural Computation.

[12]  勇一 作村,et al.  Biophysics of Computation , 2001 .

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

[14]  Y. Dan,et al.  Spike Timing-Dependent Plasticity of Neural Circuits , 2004, Neuron.

[15]  P. Brown,et al.  Event-related beta desynchronization in human subthalamic nucleus correlates with motor performance. , 2004, Brain : a journal of neurology.

[16]  Yuji Ikegaya,et al.  Synfire Chains and Cortical Songs: Temporal Modules of Cortical Activity , 2004, Science.

[17]  Peter Dayan,et al.  The Effect of Correlated Variability on the Accuracy of a Population Code , 1999, Neural Computation.

[18]  A. Graybiel,et al.  Synchronous, Focally Modulated β-Band Oscillations Characterize Local Field Potential Activity in the Striatum of Awake Behaving Monkeys , 2003, The Journal of Neuroscience.

[19]  Arnaud Delorme,et al.  Spike-based strategies for rapid processing , 2001, Neural Networks.

[20]  J. Dostrovsky,et al.  Dependence of subthalamic nucleus oscillations on movement and dopamine in Parkinson's disease. , 2002, Brain : a journal of neurology.

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

[22]  A. Destexhe Kinetic Models of Synaptic Transmission , 1997 .

[23]  E. Vaadia,et al.  Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. , 1993, Journal of neurophysiology.

[24]  G. Horwitz,et al.  The Local Field Potential , 2022 .

[25]  Michael N. Shadlen,et al.  Synchrony Unbound A Critical Evaluation of the Temporal Binding Hypothesis , 1999, Neuron.

[26]  F. Crick Function of the thalamic reticular complex: the searchlight hypothesis. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[27]  Michael J. Berry,et al.  Synergy, Redundancy, and Independence in Population Codes , 2003, The Journal of Neuroscience.

[28]  R. Johansson,et al.  First spikes in ensembles of human tactile afferents code complex spatial fingertip events , 2004, Nature Neuroscience.

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

[30]  William A. MacKay,et al.  Synchronized neuronal oscillations and their role in motor processes , 1997, Trends in Cognitive Sciences.

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

[32]  A. B. Bonds,et al.  Stimulus-dependent modulation of spike burst length in cat striate cortical cells. , 1997, Journal of neurophysiology.

[33]  William R. Softky,et al.  The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[34]  Steven C Dakin,et al.  Role of synchrony in contour binding: some transient doubts sustained. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[35]  Wolf Singer,et al.  Time as coding space? , 1999, Current Opinion in Neurobiology.

[36]  J. J. Hopfield,et al.  Pattern recognition computation using action potential timing for stimulus representation , 1995, Nature.

[37]  Christof Koch,et al.  Temporal Precision of Spike Trains in Extrastriate Cortex of the Behaving Macaque Monkey , 1999, Neural Computation.

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

[39]  D. Winter,et al.  Predictions of knee and ankle moments of force in walking from EMG and kinematic data. , 1985, Journal of biomechanics.

[40]  K. D. Punta,et al.  An ultra-sparse code underlies the generation of neural sequences in a songbird , 2002 .

[41]  Y. Lamarre,et al.  Local field potential oscillations in primate cerebellar cortex during voluntary movement. , 1997, Journal of neurophysiology.

[42]  Eugene M. Izhikevich,et al.  Polychronization: Computation with Spikes , 2006, Neural Computation.

[43]  Bartlett W. Mel,et al.  Arithmetic of Subthreshold Synaptic Summation in a Model CA1 Pyramidal Cell , 2003, Neuron.

[44]  T. Yin,et al.  Interaural time sensitivity in medial superior olive of cat. , 1990, Journal of neurophysiology.

[45]  Wolfgang Maass,et al.  Computing the Optimally Fitted Spike Train for a Synapse , 2001, Neural Computation.

[46]  Carlos D. Brody,et al.  Simple Networks for Spike-Timing-Based Computation, with Application to Olfactory Processing , 2003, Neuron.

[47]  Robert A. Legenstein,et al.  What Can a Neuron Learn with Spike-Timing-Dependent Plasticity? , 2005, Neural Computation.

[48]  L. Optican,et al.  Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. III. Information theoretic analysis. , 1987, Journal of neurophysiology.

[49]  Jesper Tegnér,et al.  Spike-timing-dependent plasticity: common themes and divergent vistas , 2002, Biological Cybernetics.

[50]  Paul F. M. J. Verschure,et al.  Two-State Membrane Potential Fluctuations Driven by Weak Pairwise Correlations , 2004, Neural Computation.

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

[52]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[53]  D V Buonomano,et al.  Decoding Temporal Information: A Model Based on Short-Term Synaptic Plasticity , 2000, The Journal of Neuroscience.

[54]  H. Sompolinsky,et al.  The tempotron: a neuron that learns spike timing–based decisions , 2006, Nature Neuroscience.

[55]  Chris Eliasmith,et al.  Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems , 2004, IEEE Transactions on Neural Networks.

[56]  B. Grothe,et al.  Precise inhibition is essential for microsecond interaural time difference coding , 2002, Nature.

[57]  Jeffrey C. Magee,et al.  Dendritic Ih normalizes temporal summation in hippocampal CA1 neurons , 1999, Nature Neuroscience.

[58]  T Natschläger,et al.  Spatial and temporal pattern analysis via spiking neurons. , 1998, Network.

[59]  A. Oliviero,et al.  Movement-related changes in synchronization in the human basal ganglia. , 2002, Brain : a journal of neurology.

[60]  M. Abeles Role of the cortical neuron: integrator or coincidence detector? , 1982, Israel journal of medical sciences.

[61]  William Bialek,et al.  Spike timing and the coding of naturalistic sounds in a central auditory area of songbirds , 2001, NIPS.

[62]  G. P. Moore,et al.  SENSITIVITY OF NEURONES IN APLYSIA TO TEMPORAL PATTERN OF ARRIVING IMPULSES. , 1963, The Journal of experimental biology.

[63]  G. Stuart,et al.  Site independence of EPSP time course is mediated by dendritic I(h) in neocortical pyramidal neurons. , 2000, Journal of neurophysiology.

[64]  T. Sejnowski,et al.  Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models , 2000, The Journal of Neuroscience.

[65]  G. Laurent,et al.  Who reads temporal information contained across synchronized and oscillatory spike trains? , 1998, Nature.