Paper Info
Title
Detecting and Measuring Higher Order Synchronization Among Neurons: A Bayesian Approach
Abstract
A Bayesian approach to modeling and inferring patterns of synchronous activation in a group of neurons. A major objective of the research is to provide statistical tools for detecting changes in synchronization patterns. Our framework is not restricted to the case of correlated pairs, but generalizes the Boltzmann machine model to allow for higher order interactions. A Markov Chain Monte Carlo Model Composition (MC3) algorithm is applied in order to search over connectivity structures and uses Laplace's method to approximate their posterior probabilities.Performance of the method was first tested on synthetic data. The method was then applied to data obtained on multi-unit recordings of six neurons in the visual cortex of a rhesus monkey in two different attentional states. The obtained results indicate that the interaction structure predicted by the data is richer than just a set of synchronous pairs. They also confirmed the experimenter's conjecture that different attentional states were associated with different interaction structures.
Year
DOI
Venue
1996
10.1007/3-540-61510-5_70
ICANN
Keywords
Field
DocType
measuring higher order synchronization,markov chain monte carlo model composition,bayesian approach,nonhierarchical loglinear models,laplace's method,neural networks,boltzmann machine,posterior probability,markov chain monte carlo,neural network,synthetic data,higher order
Boltzmann machine,Variable-order Bayesian network,Synchronization,Pattern recognition,Markov chain Monte Carlo,Computer science,Laplace's method,Synthetic data,Artificial intelligence,Artificial neural network,Machine learning,Bayesian probability
Conference
ISBN
Citations 
PageRank 
3-540-61510-5
0
0.34
References 
Authors
1
4
Name
Order
Citations
PageRank
Laura Martignon111317.73
Kathryn Blackmond-Laskey2851109.86
Arne Schwarz300.34
Vaadia, Eilon414115.90