When are microcircuits well-modeled by maximum entropy methods?

When are microcircuits well-modeled by pairwise maximum entropy methods?

When are feedforward microcircuits well-modeled by maximum entropy methods?

Describing the collective activity of neural populations is a daunting task: the number of possible patterns grows exponentially with the number of cells, resulting in practically unlimited complexity. Recent empirical studies, however, find that the activity patterns of some, but not all, circuits are well described by maximum entropy models that incorporate only pairwise interactions. Why are...

Empirical Maximum Entropy Methods

A method, which we suggest to call the Empirical Maximum Entropy method, is implicitly present at Maximum Entropy Empirical Likelihood method, as its special, non-parametric case. From this vantage point the entropy-based empirical approach to estimation is surveyed.

The Effect of Observation Data Sampling Methods on Infiltration Areas by Maximum Entropy Model

Statistical modeling methods are based on multivariate regression methods and require the presence and absence location of data for the construction of the model. In most cases, there is no trustworthy absence data. Therefore, other methods that are based only on the presence of the phenomenon are used. Considering the importance of modeling - saving time and cost and the probable prediction of...

Smoothing methods in maximum entropy language modeling

This paper discusses various aspects of smoothing techniques in maximum entropy language modeling, a topic not sufficiently covered by previous publications. We show (1) that straightforward maximum entropy models with nested features, e.g. tri–, bi–, and unigrams, result in unsmoothed relative frequencies models; (2) that maximum entropy models with nested features and discounted feature count...