| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
Letter |
Neural Systems Group, University of Newcastle upon Tyne Department of Psychology, Newcastle upon Tyne, U.K.
Howard Hughes Medical Institute and Center for Neural Science, New York University, New York, NY 10003, U.S.A.
We demonstrate that the information contained in the spike occurrence times of a population of neurons can be broken up into a series of terms, each reflecting something about potential coding mechanisms. This is possible in the coding regime in which few spikes are emitted in the relevant time window. This approach allows us to study the additional information contributed by spike timing beyond that present in the spike counts and to examine the contributions to the whole information of different statistical properties of spike trains, such as firing rates and correlation functions. It thus forms the basis for a new quantitative procedure for analyzing simultaneous multiple neuron recordings and provides theoretical constraints on neural coding strategies. We find a transition between two coding regimes, depending on the size of the relevant observation timescale. For time windows shorter than the timescale of the stimulus-induced response fluctuations, there exists a spike count coding phase, in which the purely temporal information is of third order in time. For time windows much longer than the characteristic timescale, there can be additional timing information of first order, leading to a temporal coding phase in which timing information may affect the instantaneous information rate.
In this new framework, we study the relative contributions of the dynamic firing rate and correlation variables to the full temporal information, the interaction of signal and noise correlations in temporal coding, synergy between spikes and between cells, and the effect of refractoriness. We illustrate the utility of the technique by analyzing a few cells from the rat barrel cortex.
This article has been cited by other articles:
|
|
 |
|
  M. A. Montemurro, R. Senatore, and S. Panzeri Tight Data-Robust Bounds to Mutual Information Combining Shuffling and Model Selection Techniques Neural Comput., November 1, 2007; 19(11): 2913 - 2957. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Nevet, G. Morris, G. Saban, D. Arkadir, and H. Bergman Lack of Spike-Count and Spike-Time Correlations in the Substantia Nigra Reticulata Despite Overlap of Neural Responses J Neurophysiol, October 1, 2007; 98(4): 2232 - 2243. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F. Montani, A. Kohn, M. A. Smith, and S. R. Schultz The Role of Correlations in Direction and Contrast Coding in the Primary Visual Cortex J. Neurosci., February 28, 2007; 27(9): 2338 - 2348. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. L. Fairhall, C. A. Burlingame, R. Narasimhan, R. A. Harris, J. L. Puchalla, and M. J. Berry II Selectivity for Multiple Stimulus Features in Retinal Ganglion Cells J Neurophysiol, November 1, 2006; 96(5): 2724 - 2738. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. B. Averbeck and D. Lee Effects of Noise Correlations on Information Encoding and Decoding J Neurophysiol, June 1, 2006; 95(6): 3633 - 3644. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Szwed, K. Bagdasarian, B. Blumenfeld, O. Barak, D. Derdikman, and E. Ahissar Responses of Trigeminal Ganglion Neurons to the Radial Distance of Contact During Active Vibrissal Touch J Neurophysiol, February 1, 2006; 95(2): 791 - 802. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. Pola, R. S. Petersen, A. Thiele, M. P. Young, and S. Panzeri Data-Robust Tight Lower Bounds to the Information Carried by Spike Times of a Neuronal Population Neural Comput., September 1, 2005; 17(9): 1962 - 2005. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. Arabzadeh, S. Panzeri, and M. E. Diamond Whisker Vibration Information Carried by Rat Barrel Cortex Neurons J. Neurosci., June 30, 2004; 24(26): 6011 - 6020. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Tatsuno and M. Okada Investigation of Possible Neural Architectures Underlying Information-Geometric Measures Neural Comput., April 1, 2004; 16(4): 737 - 765. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. Schneidman, W. Bialek, and M. J. Berry II Synergy, Redundancy, and Independence in Population Codes J. Neurosci., December 17, 2003; 23(37): 11539 - 11553. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
E. Arabzadeh, R. S. Petersen, and M. E. Diamond Encoding of Whisker Vibration by Rat Barrel Cortex Neurons: Implications for Texture Discrimination J. Neurosci., October 8, 2003; 23(27): 9146 - 9154. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Nirenberg and P. E. Latham Decoding neuronal spike trains: How important are correlations? PNAS, June 10, 2003; 100(12): 7348 - 7353. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Panzeri, G. Pola, and R. S. Petersen Coding of Sensory Signals by Neuronal Populations: The Role of Correlated Activity Neuroscientist, June 1, 2003; 9(3): 175 - 180. [Abstract] [PDF]
|
|
|
|
 |
|
 S. Panzeri, F. Petroni, R. S. Petersen, and M. E. Diamond Decoding Neuronal Population Activity in Rat Somatosensory Cortex: Role of Columnar Organization Cereb Cortex, January 1, 2003; 13(1): 45 - 52. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. Nakahara and S.-i. Amari Information-Geometric Measure for Neural Spikes Neural Comput., October 1, 2002; 14(10): 2269 - 2316. [Abstract] [Full Text]
|
|
|
|
 |
|
 D. S. Reich, F. Mechler, and J. D. Victor Independent and Redundant Information in Nearby Cortical Neurons Science, December 21, 2001; 294(5551): 2566 - 2568. [Abstract] [Full Text] [PDF]
|
|
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| J COGNITIVE NEUROSCIENCE | NEURAL COMPUTATION | MIT PRESS JOURNALS |
