Bayesian learning of loglinear models for neural connectivity

Authors:
Kathryn Blackmond Laskey;Laura Martignon
Affiliations:
Department of Systems Engineering and C³I Center, George Mason University, Fairfax, VA;Max Hanck Institut für Psychologische Forschung, Center for Adaptive Behavior and Cognition, München, Germany
Venue:
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Year:
1996

Citing 3
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Abstract

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 multiunit 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.