The fundamental problem in modeling complex phenomena such as human perception
using probabilistic methods is that of deducing a stochastic model of interactions between …

With our ability to record more neurons simultaneously, making sense of these data is a
challenge. Functional connectivity is one popular way to study the relationship of multiple …

A network of neurons in the central nervous system collectively represents information by its
spiking activity states. Typically observed states, ie, code words, occupy only a limited …

Unsupervised learning in a generalized Hopfield associative-memory network is
investigated in this work. First, we prove that the (generalized) Hopfield model is equivalent …

To understand the collective spiking activity in neuronal populations, it is essential to reveal
basic circuit variables responsible for these emergent functional states. Here, I develop a …

I propose a variational approach to maximum pseudolikelihood inference of the Ising model.
The variational algorithm is more computationally efficient, and does a better job predicting …