Abstract

The inference of the couplings of an Ising model with given means and correlations is called the inverse Ising problem. This approach has received a lot of attention as a tool to analyze neural data. We show that autoregressive methods may be used to learn the couplings of an Ising model, also in the case of asymmetric connections and for multispin interactions. We find that, for each link, the linear Granger causality is two times the corresponding transfer entropy (i.e., the information flow on that link) in the weak coupling limit. For sparse connections and a low number of samples, the `1 regularized least squares method is used to detect the interacting pairs of spins. Nonlinear Granger causality is related to multispin interactions.

Autore Pugliese

• Stramaglia Sebastiano

Tutti gli autori

• STRAMAGLIA S.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2010

ISSN

0378-4371

ISBN

Non Disponibile

Numero di citazioni Wos

3

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

4

Ultimo Aggiornamento Citazioni

Non Disponibile

Settori ERC

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Codici ASJC

Non Disponibile