Preprints on neural coding

[Simon Schultz]

Dear Connectionists,

The following two preprints are available for downloading:

1. ...................................................................

"A UNIFIED APPROACH TO THE STUDY OF TEMPORAL, CORRELATIONAL AND
RATE CODING", Stefano Panzeri* and Simon R. Schultz+. 

In press, Neural Computation.

* Neural Systems Group, Department of Psychology, University of
Newcastle upon Tyne.
+ Howard Hughes Medical Institute & Center for Neural Science, New
York University.

------ Abstract: ------

We demonstrate that the information contained in the spike occurrence
times of a population of neurons can be broken up into a series of
terms, each of which reflect something about potential coding
mechanisms. This is possible in the coding r{\'e}gime in which few
spikes are emitted in the relevant time window. 

This approach allows us to study the additional information
contributed by spike timing beyond that present in the spike counts;
to examine the contributions to the whole information of different
statistical properties of spike trains, such as firing rates and
correlation functions; and forms the basis for a new quantitative
procedure for the analysis of simultaneous multiple neuron
recordings. It also provides theoretical constraints upon neural
coding strategies. We find a transition between two coding
r{\'e}gimes, depending upon the size of the relevant observation
timescale. For time windows shorter than the timescale of the
stimulus-induced response fluctuations, there exists a spike count
coding phase, where the purely temporal information is of third order
in time. For time windows much longer than the characteristic
timescale, there can be additional timing information of first order,
leading to a temporal coding phase in which timing information may
affect the instantaneous information rate.

In this new framework we study the relative contributions of the
dynamic firing rate and correlation variables to the full temporal
information; the interaction of signal and noise correlations in
temporal coding; synergy between spikes and between cells; and the
effect of refractoriness. We illustrate the utility of the technique
by analysis of a few cells from the rat barrel cortex.

Download alternatives:

http://www.cns.nyu.edu/~schultz/tcode.ps.gz
http://www.cns.nyu.edu/~schultz/tcode.pdf

2. ...................................................................

"SYNCHRONISATION, BINDING AND THE ROLE OF CORRELATED FIRING IN FAST
INFORMATION TRANSMISSION", Simon R. Schultz+, Huw D. R. Golledge* and
Stefano Panzeri*.

To be published in S. Wermter, J. Austin and
D. Willshaw (Eds.), Emergent Neural Computational Architectures based on
Neuroscience, Springer-Verlag, Heidelberg.

+ Howard Hughes Medical Institute & Center for Neural Science, New
York University.
* Neural Systems Group, Department of Psychology, University of
Newcastle upon Tyne.

------ Abstract: ------

Does synchronisation between action potentials from different neurons
in the visual system play a substantial role in solving the binding
problem? The binding problem can be studied quantitatively in the
broader framework of the information contained in neural spike trains
about some external correlate, which in this case is object
configurations in the visual field. We approach this problem by using
a mathematical formalism that quantifies the impact of correlated
firing in short time scales. Using a power series expansion, the
mutual information an ensemble of neurons conveys about external
stimuli is broken down into firing rate and correlation
components. This leads to a new quantification procedure directly
applicable to simultaneous multiple neuron recordings. It
theoretically constrains the neural code, showing that correlations
contribute less significantly than firing rates to rapid information
processing. By using this approach to study the limits upon the amount
of information that an ideal observer is able to extract from a
synchrony code, it may be possible to determine whether the available
amount of information is sufficient to support computational processes
such as feature binding.

Download alternatives:

http://www.cns.nyu.edu/~schultz/emernet.ps.gz
http://www.cns.nyu.edu/~schultz/emernet.pdf


-- 
Dr. Simon R. Schultz                         Phone: +1-212 998 3775
Howard Hughes Medical Institute &            Fax: +1-212 995 4011
Center for Neural Science,                   Email:schultz at cns.nyu.edu
New York University, 4 Washington Place, New York NY 10003, U.S.A.    
http://www.cns.nyu.edu/~schultz/