nips nips2011 nips2011-86 nips2011-86-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jakob H. Macke, Lars Buesing, John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, Maneesh Sahani
Abstract: Neurons in the neocortex code and compute as part of a locally interconnected population. Large-scale multi-electrode recording makes it possible to access these population processes empirically by fitting statistical models to unaveraged data. What statistical structure best describes the concurrent spiking of cells within a local network? We argue that in the cortex, where firing exhibits extensive correlations in both time and space and where a typical sample of neurons still reflects only a very small fraction of the local population, the most appropriate model captures shared variability by a low-dimensional latent process evolving with smooth dynamics, rather than by putative direct coupling. We test this claim by comparing a latent dynamical model with realistic spiking observations to coupled generalised linear spike-response models (GLMs) using cortical recordings. We find that the latent dynamical approach outperforms the GLM in terms of goodness-offit, and reproduces the temporal correlations in the data more accurately. We also compare models whose observations models are either derived from a Gaussian or point-process models, finding that the non-Gaussian model provides slightly better goodness-of-fit and more realistic population spike counts. 1
[1] J. W. Pillow, J. Shlens, L. Paninski, A. Sher, A. M. Litke, E. J. Chichilnisky, and E. P. Simoncelli. Spatiotemporal correlations and visual signalling in a complete neuronal population. Nature, 454(7207):995– 999, 2008.
[2] G. Santhanam, B. M. Yu, V. Gilja, S. I. Ryu, A. Afshar, M. Sahani, and K. V. Shenoy. Factor-analysis methods for higher-performance neural prostheses. J Neurophysiol, 102(2):1315–1330, 2009.
[3] E.S. Chornoboy, L.P. Schramm, and A.F. Karr. Maximum likelihood identification of neural point process systems. Biological Cybernetics, 59(4):265–275, 1988.
[4] P. McCulloch and J. Nelder. Generalized linear models. Chapman and Hall, London, 1989.
[5] L. Paninski. Maximum likelihood estimation of cascade point-process neural encoding models. Network, 15(4):243–262, 2004.
[6] S.P. Boyd and L. Vandenberghe. Convex optimization. Cambridge Univ Press, 2004.
[7] W. Truccolo, L. R. Hochberg, and J. P. Donoghue. Collective dynamics in human and monkey sensorimotor cortex: predicting single neuron spikes. Nat Neurosci, 13(1):105–111, 2010.
[8] B. M. Yu, J. P. Cunningham, G. Santhanam, S. I. Ryu, K. V. Shenoy, and M. Sahani. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. J Neurophysiol, 102(1):614–635, 2009.
[9] S. Roweis and Z. Ghahramani. A unifying review of linear gaussian models. Neural Comput, 11(2):305– 345, 1999 Feb 15.
[10] A. C. Smith and E. N. Brown. Estimating a state-space model from point process observations. Neural Comput, 15(5):965–91, 2003.
[11] V. Lawhern, W. Wu, N. Hatsopoulos, and L. Paninski. Population decoding of motor cortical activity using a generalized linear model with hidden states. J Neurosci Methods, 189(2):267–280, 2010.
[12] J.E. Kulkarni and L. Paninski. Common-input models for multiple neural spike-train data. Network: Computation in Neural Systems, 18(4):375–407, 2007.
[13] M. Vidne, Y. Ahmadian, J. Shlens, J.W. Pillow, J Kulkarni, E. J. Chichilnisky, E. P. Simoncelli, and L Paninski. A common-input model of a complete network of ganglion cells in the primate retina. In Computational and Systems Neuroscience, 2010.
[14] M. M. Churchland, B. M. Yu, M. Sahani, and K. V. Shenoy. Techniques for extracting single-trial activity patterns from large-scale neural recordings. Current Opinion in Neurobiology, 17(5):609–618, 2007.
[15] D. Y. Tso, C. D. Gilbert, and T. N. Wiesel. Relationships between horizontal interactions and functional architecture in cat striate cortex revealed by cross-correlation analysis. J Neurosci, 6(4):1160–1170, 1986.
[16] A. Jackson, V. J. Gee, S. N. Baker, and R. N. Lemon. Synchrony between neurons with similar muscle fields in monkey motor cortex. Neuron, 38(1):115–125, 2003.
[17] W. Wu, Y. Gao, E. Bienenstock, J.P. Donoghue, and M.J. Black. Bayesian population decoding of motor cortical activity using a kalman filter. Neural Comput, 18(1):80–118, 2006.
[18] B. Yu, A. Afshar, G. Santhanam, S.I. Ryu, K. Shenoy, and M. Sahani. Extracting dynamical structure embedded in neural activity. In Advances in Neural Information Processing Systems, volume 18, pages 1545–1552. MIT Press, Cambridge, 2006.
[19] J.P. Cunningham, B.M. Yu, K.V. Shenoy, and M. Sahani. Inferring neural firing rates from spike trains using gaussian processes. Advances in neural information processing systems, 20:329–336, 2008.
[20] U. T. Eden, L. M. Frank, R. Barbieri, V. Solo, and E. N. Brown. Dynamic analysis of neural encoding by point process adaptive filtering. Neural Comput, 16(5):971–98, 2004.
[21] B. Yu, J. Cunningham, K. Shenoy, and M. Sahani. Neural decoding of movements: From linear to nonlinear trajectory models. In Neural Information Processing, pages 586–595. Springer, 2008.
[22] L. Paninski, Y. Ahmadian, D. G. Ferreira, S. Koyama, K. Rahnama Rad, M. Vidne, J. Vogelstein, and W. Wu. A new look at state-space models for neural data. J Comput Neurosci, 29(1-2):107–126, 2010.
[23] Y. Ahmadian, J. W. Pillow, and L. Paninski. Efficient markov chain monte carlo methods for decoding neural spike trains. Neural Comput, 23(1):46–96, 2011.
[24] G. Andrew and J. Gao. Scalable training of l 1-regularized log-linear models. In Proceedings of the 24th international conference on Machine learning, pages 33–40. ACM, 2007.
[25] E. Schneidman, M. J. 2nd Berry, R. Segev, and W. Bialek. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 440(7087):1007–12, 2006.
[26] T.D. Wickens. Elementary Signal Detection Theory. Oxford University Press, 2002. 9