Abstract
Information Theory enables different candidate coding strategies to be quantified and compared, and hence is a natural framework for studying neural coding. The main difficulty is that estimates of information from experimental data are prone to systematic sampling error. In this chapter, we present a step-by-step guide to how this error can be addressed, and reliable information estimates obtained.
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Pola, G., Schultz, S.R., Petersen, R.S., Panzeri, S. (2003). A Practical Guide to Information Analysis of Spike Trains. In: Kötter, R. (eds) Neuroscience Databases. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1079-6_10
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DOI: https://doi.org/10.1007/978-1-4615-1079-6_10
Publisher Name: Springer, Boston, MA
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