[Comp-neuro] New paper on information theoretically optimal tuning curves and population coding

Mark McDonnell Mark.McDonnell at unisa.edu.au
Sat Oct 24 04:20:56 CEST 2009

Dear Colleagues,

A new paper on information theoretically optimal tuning curves has been published in Physical Review Letters:

Nikitin, Stocks, Morse and McDonnell, "Neural Population Coding Is Optimized by Discrete Tuning Curves," Physical Review Letters 103, 138101 (2009)


The sigmoidal tuning curve that maximizes the mutual information for a Poisson neuron, or population of Poisson neurons, is obtained. The optimal tuning curve is found to have a discrete structure that results in a quantization of the input signal. The number of quantization levels undergoes a hierarchy of phase transitions as the length of the coding window is varied. We postulate, using the mammalian auditory system as an example, that the presence of a subpopulation structure within a neural population is consistent with an optimal neural code. 

DOI: http://dx.doi.org/10.1103/PhysRevLett.103.138101

Arxiv Preprint: http://arxiv.org/abs/0809.1549

Virtual Journal of Biological Physics Research:  http://www.vjbio.org/getabs/servlet/GetabsServlet?prog=normal&id=VIRT02000018000007000139000001&idtype=cvips&gifs=Yes


Dr Mark McDonnell
Research Fellow

Institute for Telecommunications Research
University of South Australia
SPRI Building Mawson Lakes Boulevard 
Mawson Lakes SA 5095 AUSTRALIA

Phone: +61 8 8302 3341
Fax: +61 8302 3817

URL:  http://people.unisa.edu.au/Mark.McDonnell
Email: mark.mcdonnell at unisa.edu.au

Now Available: "Stochastic Resonance: From Suprathreshold Stochastic Resonance to Stochastic Signal Quantization"
Mark D. McDonnell, Nigel G. Stocks, Charles E. M. Pearce, Derek Abbott

For more information see www.cambridge.org/9780521882620

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