  This paper presents a Bayesian approach to learning the connectivity
structure of a group of neurons from data on configuration frequencies. A major
objective of the research is to provide statistical tools for detecting changes
in firing patterns with changing stimuli. Our framework is not restricted to
the well-understood case of pair interactions, but generalizes the Boltzmann
machine model to allow for higher order interactions. The paper applies a
Markov Chain Monte Carlo Model Composition (MC3) algorithm to search over
connectivity structures and uses Laplace's method to approximate posterior
probabilities of structures. Performance of the methods was tested on synthetic
data. The models were also applied to data obtained by Vaadia on multi-unit
recordings of several neurons in the visual cortex of a rhesus monkey in two
different attentional states. Results confirmed the experimenters' conjecture
that different attentional states were associated with different interaction
structures.
