paper available

[Matthias Bethge]

Dear Connectionists,

the following preprint is available for downloading:

http://www-neuro.physik.uni-bremen.de/~mbethge/publications.html

Matthias Bethge, David Rotermund and Klaus Pawelzik.
Optimal short-term population coding: when Fisher information fails.
Neural Computation, in press.

Abstract:

Efficient coding has been proposed as a first principle
explaining neuronal response properties in the central nervous system.
The shape of optimal codes, however, strongly depends on the natural
limitations of the particular physical system. Here we investigate how
optimal neuronal encoding strategies are influenced by the finite
number of neurons $N$ (place constraint), the limited decoding time
window length $T$ (time constraint), the maximum neuronal firing rate
$f_{max}$ (power constraint) and the maximal average rate $\langle f
\rangle_{max}$ (energy constraint).  While Fisher information provides
a general lower bound for the mean squared error of unbiased signal
reconstruction, its use to characterize the coding precision is
limited. Analyzing simple examples, we illustrate some typical
pitfalls and thereby show that Fisher information provides a valid
measure for the precision of a code only if the dynamic range
$(f_{min}T, f_{max}T)$ is sufficiently large. In particular, we
demonstrate that the optimal width of Gaussian tuning curves depends
on the available decoding time $T$. Within the broader class of
unimodal tuning functions it turns out that the shape of a
Fisher-optimal coding scheme is not unique. We solve this
ambiguity by taking the minimum mean square error into account, which
leads to flat tuning curves. The tuning width, however, remains to be
determined rather by energy constraints than by the principle of
efficient coding.



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  Matthias Bethge  @ Institute of Theoretical Physics
  www: http://www-neuro.physik.uni-bremen.de/~mbethge

  Tel. : (+49)421-218-4460             /\_______________
  Fax  : (+49)421-218-9104          /\/
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