More about neural coding


William Bialek and collaborators


Papers are in chronological order, most recent papers at the top.  Numbers refer to a full list of publications for WB.



[103.] Synergy from silence in a combinatorial neural code.  E Schneidman, JL Puchalla, RA Harris, W Bialek & MJ Berry II, submitted.



[101.] Entropy and information in neural spike trains:  Progress on the sampling problem.  I Nemenman, W Bialek & R de Ruyter van Steveninck, Phys Rev E 69, 056111 (2004); physics/0306063.



[98.] Time course of information about  motion direction  in visual area  MT of macaque monkeys.  LC Osborne, W Bialek & SG Lisberger, J Neurosci  24, 3210-3222 (2004).



[93.] Analyzing neural responses to natural signals: Maximally informative dimensions.  T Sharpee, NC Rust & W Bialek, Neural  Comp  16, 223-250 (2004).



[92.] Spike sorting in the frequency domain with overlap detection. D Rinberg, W Bialek, H Davidowitz  & N Tishby, physics/0306056.



[91.] Synergy, redundancy, and independence in population codes. E Schneidman, W Bialek & MJ Berry II, J Neurosci  23, 11539-11553 (2003).



[90.] Network information and connected correlations. E Schneidman, S Still, MJ Berry II & W Bialek, Phys Rev Lett  91, 238701 (2003).



[89.] The information content of receptive fields. TL Adelman, W Bialek & RM Olberg, Neuron  40, 822-833 (2003).



[88.] Computation in single neurons:  Hodgkin and Huxley revisited. B Agüera y Arcas, AL Fairhall, & W Bialek, Neural Comp 15, 1715-1749 (2003).



[87.] An information theoretic approach to the functional classification of neurons. E Schneidman, W Bialek, & MJ Berry II, in Advances in Neural Information Processing 15, S Becker, S Thrun & K Obermayer, eds, pp 197-204 (MIT Press, Cambridge, 2003).



[82.] Spike timing and the coding of naturalistic sounds in a central auditory area of songbirds. BD Wright, K Sen, W Bialek, & AJ Doupe, in Advances in Neural Information Processing 14,  TG Dietterich, S Becker & Z Ghahramani, eds, pp 309-316 (MIT Press, Cambridge, 2002).



[58.] Naturalistic stimuli increase the rate and efficiency of information transmission by primary auditory neurons.  F Rieke, DA Bodnar & W Bialek, Proc R Soc Lond Ser B  262, 259-265 (1995).



[55.] Information flow in sensory neurons.  M DeWeese & W Bialek, Il  Nuovo Cimento  17D, 733-741 (1995).


This was a first step in the still incomplete project of constructing a theory for optimal coding by spiking neurons.  Along the way we introduced some interesting technical tools, such as a perturbative expansion of the information transmission rate.  In addition we took the opportunity to debunk some misconceptions that surrounded the idea of “stochastic resonance.”  This might also be the first place here we stated explicitly that the prediction of an optimal coding theory will necessarily be a code that adapts to the statistics of the sensory inputs.



[49.] Non-phase-locked auditory cells and envelope detection.  F Rieke, W Yamada, E Lewis & W Bialek, in  Analysis and Modeling of Neural Systems 2, F Eeckman, ed, pp 255-263 (Kluwer Academic, 1993).



[45.] Coding efficiency and information rates in sensory neurons.  F Rieke, D Warland &  W Bialek,  Europhys Lett  22, 151-156, (1993).



[40.] Real-time coding of complex sounds in the auditory nerve.  F Rieke, W Yamada, K Moortgat, ER Lewis & W Bialek, in Auditory Physiology and Perception: Proceedings of the 8th International Conference on Hearing, Y Cazals, L Demany, & K Horner, eds, pp 315-322 (Pergamon, 1992).



[33.]  Reading between the spikes in the cricket cercal afferent system.  D Warland, M Landolfa, JP Miller & W Bialek, in  Analysis and Modeling of Neural Systems, F Eeckman, ed, pp 327-333 (Kluwer Academic, 1991).



[25.] Coding and computation with neural spike trains. W Bialek & A Zee, J. Stat. Phys. 59, 103-115 (1990).


Inspired in part by the results in the fly, we set out to study the problem of coding and decoding in simple models of spiking neurons. Probably the most important result was that there is a large regime in which signals can be decoded by linear (perturbative) methods even though the encoding is strongly nonlinear. The small parameter that makes this work is the mean number of spikes per correlation time of the signal, suggesting that spike trains can be decoded linearly if they are sparse in the time domain.  In Spikes we discuss the evidence that many different neural systems make use of such a sparse representation, but of course one really wants a direct experimental answer: Can we decode the spike trains of real neurons using these theoretical ideas? As an aside, it is worth noting that identifying a regime where linear decoding can work is really much more general than the details of the model that we chose to investigate; this is important, since none of the models we write down are likely to be accurate in detail.


Rereading the original paper, it is perhaps not so clear that sparseness is the key idea. A somewhat more explicit discussion is given in later summer school lectures [29, 84].