Paper Info
Title
Discovering Patterns in Multi-neuronal Spike Trains using the Frequent Episode Method
Abstract
Discovering the 'Neural Code' from multi-neuronal spike trains is an important task in neuroscience. For such an analysis, it is important to unearth interesting regularities in the spiking patterns. In this report, we present an efficient method for automatically discovering synchrony, synfire chains, and more general sequences of neuronal firings. We use the Frequent Episode Discovery framework of Laxman, Sastry, and Unnikrishnan (2005), in which the episodes are represented and recognized using finite-state automata. Many aspects of functional connectivity between neuronal populations can be inferred from the episodes. We demonstrate these using simulated multi-neuronal data from a Poisson model. We also present a method to assess the statistical significance of the discovered episodes. Since the Temporal Data Mining (TDM) methods used in this report can analyze data from hundreds and potentially thousands of neurons, we argue that this framework is appropriate for discovering the `Neural Code'.
Year
Venue
Keywords
2007
Clinical Orthopaedics and Related Research
finite state automata,statistical significance,poisson model
Field
DocType
Volume
Data mining,Computer science,Neural coding,Automaton,Artificial intelligence,Train,Temporal data mining,Machine learning
Journal
abs/0709.0
Citations 
PageRank 
References 
5
0.71
0
Authors
3
Name
Order
Citations
PageRank
K. P. Unnikrishnan129923.21
Debprakash Patnaik219114.89
P. S. Sastry374157.27