Unbiased measures of transmitted information and channel capacity from multivariate neuronal data

Two measures from information theory, transmitted information and channel capacity, can quantify the ability of neurons to convey stimulus-dependent information. These measures are calculated using probability functions estimated from stimulus-response data. However, these estimates are biased by response quantization, noise, and small sample sizes. Improved estimators are developed in this paper that depend on both an estimate of the sample-size bias and the noise in the data.

[1]  P. R. Bevington,et al.  Data Reduction and Error Analysis for the Physical Sciences , 1969 .

[2]  A. Carlton On the bias of information estimates. , 1969 .

[3]  Richard E. Blahut,et al.  Computation of channel capacity and rate-distortion functions , 1972, IEEE Trans. Inf. Theory.

[4]  B J Richmond,et al.  Temporal encoding of two-dimensional patterns by single units in primate inferior temporal cortex. II. Quantification of response waveform. , 1987, Journal of neurophysiology.

[5]  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.

[6]  Richard E. Blahut,et al.  Principles and practice of information theory , 1987 .

[7]  R. Fagen Information measures: statistical confidence limits and inference. , 1978, Journal of theoretical biology.

[8]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[9]  B. Efron The jackknife, the bootstrap, and other resampling plans , 1987 .

[10]  Alan Crowe,et al.  Information transmission in non-visual fingertip matching along a horizontal track in the median plane , 2004, Biological Cybernetics.

[11]  Ga Miller,et al.  Note on the bias of information estimates , 1955 .

[12]  Irwin Guttman,et al.  Introductory Engineering Statistics , 1965 .

[13]  R. Eckhorn,et al.  Efficiency of different neuronal codes: Information transfer calculations for three different neuronal systems , 1976, Biological Cybernetics.

[14]  H. Spitzer,et al.  Temporal encoding of two-dimensional patterns by single units in primate primary visual cortex. I. Stimulus-response relations. , 1990, Journal of neurophysiology.

[15]  Marc S. Fuller,et al.  An Information-Theoretic Analysis of Cutaneous Receptor Responses , 1984, IEEE Transactions on Biomedical Engineering.

[16]  B. Efron,et al.  The Jackknife: The Bootstrap and Other Resampling Plans. , 1983 .

[17]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[18]  Thomas A. Zeffiro,et al.  The information transmitted at final position in visually triggered forearm movements , 2004, Biological Cybernetics.

[19]  L Maffei,et al.  Patterns in the discharge of simple and complex visual cortical cells , 1981, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[20]  Barbara Sakitt Visual-motor efficiency (VME) and the information transmitted in visual-motor tasks , 1980 .

[21]  Reinhard Eckhorn,et al.  Rigorous and extended application of information theory to the afferent visual system of the cat , 2004, Biological Cybernetics.

[22]  Reinhard Eckhorn,et al.  Rigorous and extended application of information theory to the afferent visual system of the cat. I. Basic concepts , 2004, Kybernetik.

[23]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[24]  J G Daugman,et al.  Information Theory and Coding , 1998 .

[25]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[26]  B J Richmond,et al.  Temporal encoding of two-dimensional patterns by single units in primate primary visual cortex. II. Information transmission. , 1990, Journal of neurophysiology.

[27]  B. Silverman Density estimation for statistics and data analysis , 1986 .

[28]  A. W. Macrae,et al.  On calculating unbiased information measures. , 1971 .