Uncertainty in Artificial Intelligence
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Bayesian Learning of Loglinear Models for Neural Connectivity
Kathryn Laskey, Laura Martignon
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.
Keywords: Nonhierarchical loglinear models, Markov chain Monte Carlo model composition, Laplac
Pages: 373-380
PS Link: FTP://site.gmu.edu/people/klaskey/papers/loglin.ps
PDF Link: /papers/96/p373-laskey.pdf
AUTHOR = "Kathryn Laskey and Laura Martignon",
TITLE = "Bayesian Learning of Loglinear Models for Neural Connectivity",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "373--380"

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