English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Poster

How biased are maximum entropy models of neural population activity?

MPS-Authors
/persons/resource/persons84066

Macke,  J
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

Macke, J., Murray, I., & Latham, P. (2013). How biased are maximum entropy models of neural population activity?. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2013), Salt Lake City, UT, USA.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B4E2-A
Abstract
Maximum entropy models have become popular statistical models in neuroscience and other areas of biology, and can be useful for quantifying the coding properties of sensory systems. However, maximum entropy models fit to small data sets can be subject to sampling bias; i.e. the true entropy of the system can be severely under-estimated. Here we study the sampling properties of estimates of the entropy obtained from maximum entropy models. We focus on the pairwise binary model, which is used extensively to model neural population activity. We show that if the data is well described by a pairwise model, the bias is equal to the number of parameters divided by twice the number of observations. However, if the higher order correlations in the data deviate from those predicted by the model, the bias can be larger. Using a phenomenological model of neural population recordings, we find that the additional bias due to higher-order correlations is largest for small firing probabilities, high correlations, and large population sizes. However, numerical studies indicate that even in the worst case, it is only about a factor of four larger. We derive guidelines for how much recording time one needs to achieve a bias which is smaller than some specified level of accuracy. Finally, we show how a modified plug-in estimate of the entropy can be used for bias correction.