paper available: "Bayesian Learning of loglinear models for neuron connectivity"

[Laura Martignon]

Kathryn Laskey and I have just finished the paper:

 "Bayesian Learning of loglinear models for neuron connectivity"

                                Kathryn Laskey
                          Department of Systems Engineering
                            George Mason University
                               Fairfax, VA   22030
                                klaskey at gmu.edu


                                  Laura Martignon
                      Max Planck Institute for Psychological Research
                               80802 München, Germany
                             laura at mpipf-muenchen.mpg.de

                                       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 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, Laplace's Method, Neural Networks



To obtain a copy of these papers, please send your email request to

Laura Martignon

e-mail: laura at mpipf-muenchen.mpg.de