Spike Count Reliability and the Poisson Hypothesis

The variability of cortical activity in response to repeated presentations of a stimulus has been an area of controversy in the ongoing debate regarding the evidence for fine temporal structure in nervous system activity. We present a new statistical technique for assessing the significance of observed variability in the neural spike counts with respect to a minimal Poisson hypothesis, which avoids the conventional but troubling assumption that the spiking process is identically distributed across trials. We apply the method to recordings of inferotemporal cortical neurons of primates presented with complex visual stimuli. On this data, the minimal Poisson hypothesis is rejected: the neuronal responses are too reliable to be fit by a typical firing-rate model, even allowing for sudden, time-varying, and trial-dependent rate changes after stimulus onset. The statistical evidence favors a tightly regulated stimulus response in these neurons, close to stimulus onset, although not further away.

[1]  D. Cox.  Some Statistical Methods Connected with Series of Events , 1955 .

[2]  J. Movshon,et al.  The statistical reliability of signals in single neurons in cat and monkey visual cortex , 1983, Vision Research.

[3]  Idan Segev,et al.  Methods in neuronal modeling: From synapses to networks , 1989 .

[4]  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.

[5]  Fabrizio Gabbiani,et al.  Principles of spike train analysis , 1996 .

[6]  David L. Sheinberg,et al.  Visual object recognition. , 1996, Annual review of neuroscience.

[7]  D. Snodderly,et al.  Response Variability of Neurons in Primary Visual Cortex (V1) of Alert Monkeys , 1997, The Journal of Neuroscience.

[8]  David L. Sheinberg,et al.  The role of temporal cortical areas in perceptual organization. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[9]  M. Teich,et al.  Fractal character of the neural spike train in the visual system of the cat. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[10]  Michael J. Berry,et al.  The structure and precision of retinal spike trains. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[11]  R. Muller,et al.  Place cell discharge is extremely variable during individual passes of the rat through the firing field. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

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

[13]  Michael J. Berry,et al.  Refractoriness and Neural Precision , 1997, The Journal of Neuroscience.

[14]  T. Albright,et al.  Efficient Discrimination of Temporal Patterns by Motion-Sensitive Neurons in Primate Visual Cortex , 1998, Neuron.

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

[16]  B L McNaughton,et al.  Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. , 1998, Journal of neurophysiology.

[17]  B. Richmond,et al.  Coding strategies in monkey V1 and inferior temporal cortices. , 1998, Journal of neurophysiology.

[18]  E N Brown,et al.  A Statistical Paradigm for Neural Spike Train Decoding Applied to Position Prediction from Ensemble Firing Patterns of Rat Hippocampal Place Cells , 1998, The Journal of Neuroscience.

[19]  Carrie J. McAdams,et al.  Effects of Attention on the Reliability of Individual Neurons in Monkey Visual Cortex , 1999, Neuron.

[20]  R. Vogels.  Categorization of complex visual images by rhesus monkeys. Part 1: behavioural study , 1999, The European journal of neuroscience.

[21]  R. Vogels.  Categorization of complex visual images by rhesus monkeys. Part 2: single‐cell study , 1999, The European journal of neuroscience.

[22]  B J Richmond,et al.  Stochastic nature of precisely timed spike patterns in visual system neuronal responses. , 1999, Journal of neurophysiology.

[23]  Vivien A. Casagrande,et al.  Biophysics of Computation: Information Processing in Single Neurons , 1999 .

[24]  J. Movshon.  Reliability of Neuronal Responses , 2000, Neuron.

[25]  R. Reid,et al.  Low Response Variability in Simultaneously Recorded Retinal, Thalamic, and Cortical Neurons , 2000, Neuron.

[26]  R. Desimone,et al.  Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention , 2001, Science.

[27]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[28]  P. Lennie,et al.  Information Conveyed by Onset Transients in Responses of Striate Cortical Neurons , 2001, The Journal of Neuroscience.

[29]  Robert E. Kass,et al.  A Spike-Train Probability Model , 2001, Neural Computation.

[30]  David L. Sheinberg,et al.  Noticing Familiar Objects in Real World Scenes: The Role of Temporal Cortical Neurons in Natural Vision , 2001, The Journal of Neuroscience.

[31]  Emery N. Brown,et al.  The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis , 2002, Neural Computation.

[32]  Michael J. Black,et al.  Modeling and decoding motor cortical activity using a switching Kalman filter , 2004, IEEE Transactions on Biomedical Engineering.

[33]  R E Kass,et al.  Recursive bayesian decoding of motor cortical signals by particle filtering. , 2004, Journal of neurophysiology.

[34]  D. Perrett,et al.  Visual neurones responsive to faces in the monkey temporal cortex , 2004, Experimental Brain Research.

[35]  J. Reynolds,et al.  Attentional modulation of visual processing. , 2004, Annual review of neuroscience.

[36]  Matthew Fellows,et al.  Statistical encoding model for a primary motor cortical brain-machine interface , 2005, IEEE Transactions on Biomedical Engineering.

[37]  Robert E Kass,et al.  Trial-to-trial variability and its effect on time-varying dependency between two neurons. , 2005, Journal of neurophysiology.

[38]  Robert E Kass,et al.  Statistical issues in the analysis of neuronal data. , 2005, Journal of neurophysiology.