nips nips2009 nips2009-163 nips2009-163-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Philipp Berens, Sebastian Gerwinn, Alexander Ecker, Matthias Bethge
Abstract: The relative merits of different population coding schemes have mostly been analyzed in the framework of stimulus reconstruction using Fisher Information. Here, we consider the case of stimulus discrimination in a two alternative forced choice paradigm and compute neurometric functions in terms of the minimal discrimination error and the Jensen-Shannon information to study neural population codes. We first explore the relationship between minimum discrimination error, JensenShannon Information and Fisher Information and show that the discrimination framework is more informative about the coding accuracy than Fisher Information as it defines an error for any pair of possible stimuli. In particular, it includes Fisher Information as a special case. Second, we use the framework to study population codes of angular variables. Specifically, we assess the impact of different noise correlations structures on coding accuracy in long versus short decoding time windows. That is, for long time window we use the common Gaussian noise approximation. To address the case of short time windows we analyze the Ising model with identical noise correlation structure. In this way, we provide a new rigorous framework for assessing the functional consequences of noise correlation structures for the representational accuracy of neural population codes that is in particular applicable to short-time population coding. 1
[1] L. F. Abbott and Peter Dayan. The effect of correlated variability on the accuracy of a population code. Neural Comp., 11(1):91–101, 1999.
[2] B. B. Averbeck, P. E. Latham, and A. Pouget. Neural correlations, population coding and computation. Nat Rev Neurosci, 7(5):358–366, 2006.
[3] B. B. Averbeck and D. Lee. Effects of noise correlations on information encoding and decoding. J Neurophysiol, 95(6):3633–3644, 2006.
[4] M. Bethge, D. Rotermund, and K. Pawelzik. Optimal Short-Term population coding: When fisher information fails. Neural Comp., 14(10):2317–2351, 2002.
[5] A. Bradley, B. C. Skottun, I. Ohzawa, G. Sclar, and R. D. Freeman. Visual orientation and spatial frequency discrimination: a comparison of single neurons and behavior. J Neurophysiol, 57(3):755–772, 1987.
[6] N. Brunel and J. P. Nadal. Mutual information, fisher information, and population coding. Neural Computation, 10(7):1731–1757, 1998.
[7] M. Casas, P. W. Lamberti, A. Plastino, and A. R. Plastino. Jensen-Shannon divergence, fisher information, and wootters’ hypothesis. Arxiv preprint quant-ph/0407147, 2004.
[8] T. M. Cover and J. A. Thomas. Elements of Information Theory. Wiley-Interscience, 2006.
[9] P. Dayan and L. F. Abbott. Theoretical neuroscience: Computational and mathematical modeling of neural systems. MIT Press, 2001.
[10] J.R. Hershey and P.A. Olsen. Approximating the kullback leibler divergence between gaussian mixture models. In Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, volume 4, pages IV–317–IV–320, 2007.
[11] C. P. Hung, G. Kreiman, T. Poggio, and J. J. DiCarlo. Fast readout of object identity from macaque inferior temporal cortex. Science, 310(5749):863–866, 2005.
[12] K. Josic, E. Shea-Brown, B. Doiron, and J. de la Rocha. Stimulus-dependent correlations and population codes. Neural Computation, 21(10):2774–2804, 2009.
[13] J. Lin. Divergence measures based on the shannon entropy. Information Theory, IEEE Transactions on, 37(1):145–151, 1991.
[14] S. Panzeri, A. Treves, S. Schultz, and E. T. Rolls. On decoding the responses of a population of neurons from short time windows. Neural Computation, 11(7):1553–1577, 1999.
[15] Y. Roudi, J. Tyrcha, and J. Hertz. The ising model for neural data: Model quality and approximate methods for extracting functional connectivity. Phys. Rev. E, 79:051915, February 2009.
[16] E. Schneidman, M. J. Berry, R. Segev, and W. Bialek. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 440(7087):1007–1012, 2006.
[17] J. Shlens, G. D. Field, J. L. Gauthier, M. Greschner, A. Sher, A. M. Litke, and E. J. Chichilnisky. The structure of Large-Scale synchronized firing in primate retina. Journal of Neuroscience, 29(15):5022, 2009.
[18] H. Snippe and J. Koenderink. Information in channel-coded systems: correlated receivers. Biological Cybernetics, 67(2):183–190, June 1992.
[19] S. Thorpe, D. Fize, and C. Marlot. Speed of processing in the human visual system. Nature, 381(6582):520–522, 1996.
[20] O. Tudusciuc and A. Nieder. Neuronal population coding of continuous and discrete quantity in the primate posterior parietal cortex. Proceedings of the National Academy of Sciences of the United States of America, 104(36):14513–8, 2007.
[21] P. Vazquez, M. Cano, and C. Acuna. Discrimination of line orientation in humans and monkeys. J Neurophysiol, 83(5):2639–2648, 2000. 9