Paper Info
Title
Statistical Inference of Functional Connectivity in Neuronal Networks using Frequent Episodes
Abstract
Identifying the spatio-temporal network structure of brain activity from multi-neuronal data streams is one of the biggest challenges in neuroscience. Repeating patterns of precisely timed activity across a group of neurons is potentially indicative of a microcircuit in the underlying neural tissue. Frequent episode discovery, a temporal data mining framework, has recently been shown to be a computationally efficient method of counting the occurrences of such patterns. In this paper, we propose a framework to determine when the counts are statistically significant by modeling the counting process. Our model allows direct estimation of the strengths of functional connections between neurons with improved resolution over previously published methods. It can also be used to rank the patterns discovered in a network of neurons according to their strengths and begin to reconstruct the graph structure of the network that produced the spike data. We validate our methods on simulated data and present analysis of patterns discovered in data from cultures of cortical neurons.
Year
Venue
Keywords
2009
CoRR
event sequences,temporal data mining,microcircuits,spike trains,multi-electrode array,frequent episodes,non-overlapped occurrences,statistical inferences,quantitative method,neural network,neuronal network,statistical significance,statistical inference
Field
DocType
Volume
Graph,Data mining,Data stream mining,Pattern recognition,Counting process,Computer science,Brain activity and meditation,Statistical inference,Artificial intelligence,Temporal data mining,Machine learning,Network structure
Journal
abs/0902.3725
Citations 
PageRank 
References 
0
0.34
7
Authors
5
Name
Order
Citations
PageRank
Casey Diekman100.34
Vijay Nairz200.34
K. P. Unnikrishnan300.34
P. S. Sastry474157.27
K. P. Unnikrishnan529923.21