Next Article in Journal
Objective Bayesianism and the Maximum Entropy Principle
Next Article in Special Issue
Estimating Functions of Distributions Defined over Spaces of Unknown Size
Previous Article in Journal
Land-Use Planning for Urban Sprawl Based on the CLUE-S Model: A Case Study of Guangzhou, China
Previous Article in Special Issue
Estimation Bias in Maximum Entropy Models
Entropy 2013, 15(9), 3507-3527; doi:10.3390/e15093507

The Measurement of Information Transmitted by a Neural Population: Promises and Challenges

1 The Fishberg Department of Neuroscience and The Friedman Brain Institute, The Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 2 Laboratory of Biophysics, The Rockefeller University, New York, NY 10065, USA
* Author to whom correspondence should be addressed.
Received: 10 May 2013 / Revised: 19 August 2013 / Accepted: 27 August 2013 / Published: 3 September 2013
(This article belongs to the Special Issue Estimating Information-Theoretic Quantities from Data)
Download PDF [1625 KB, uploaded 24 February 2015]


All brain functions require the coordinated activity of many neurons, and therefore there is considerable interest in estimating the amount of information that the discharge of a neural population transmits to its targets. In the past, such estimates had presented a significant challenge for populations of more than a few neurons, but we have recently described a novel method for providing such estimates for populations of essentially arbitrary size. Here, we explore the influence of some important aspects of the neuronal population discharge on such estimates. In particular, we investigate the roles of mean firing rate and of the degree and nature of correlations among neurons. The results provide constraints on the applicability of our new method and should help neuroscientists determine whether such an application is appropriate for their data.
Keywords: information; neural population; spike trains; dynamics information; neural population; spike trains; dynamics
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
MDPI and ACS Style

Crumiller, M.; Knight, B.; Kaplan, E. The Measurement of Information Transmitted by a Neural Population: Promises and Challenges. Entropy 2013, 15, 3507-3527.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


Cited By

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert