## Neural Coding

*02 Jun 2018 19:02*

And the statistics of neural spike trains more generally.

Since I've written about the neural coding problem, at great length, in my review of Spikes (see below), I won't repeat myself here.

*Things to try to understand:* Distributed and population codes.
How much can be understood about coding without also understanding computation?

*Things to do:* Causal-state reconstruction on real neural spike
data. (Done; see below.) Transducer state reconstruction; states of the
inferred transducer = classes of stimuli (+ internal histories) which make a
difference to the cell. The information coherence measure should indicate the
quantity of distributed information in spike-trains. Calculate for actual
neuronal circuits; does this interpretation make sense?

See also: Information Theory; Neural Modeling and Data Analysis; Stochastic Processes; Synchronization; Synchronization in Neural Systems; Time Series

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- Larry Abbott and Terry Sejnowski (eds.), Neural Codes and Distributed Representations
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[Review: Cells That Go
*ping,*or, the Value of the Three-Bit Spike]

- Recommended, close-ups:
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**16**(2004): 717--736 [Normally, I have strong views on using Lempel-Ziv to measure entropy rates, but here they are using the 1976 Lempel-Ziv definitions, not the 1978 ones. The difference is subtle, but important; 1978 leads to gzip and practical compression algorithms, but very bad entropy estimates; 1976 leads, as they show numerically, to quite good entropy rate estimates, at least for some processes. Thanks to Dr. Szczepanski for correspondence about this paper.] - Riccardo Barbieri, Loren M. Frank, David P. Nguyen, Michael
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