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SP
2008
IEEE
159views Security Privacy» more  SP 2008»
13 years 2 months ago
Inferring neuronal network connectivity from spike data: A temporal data mining approach
Abstract. Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent developments in electrophysiology an...
Debprakash Patnaik, P. S. Sastry, K. P. Unnikrishn...
ICDM
2009
IEEE
141views Data Mining» more  ICDM 2009»
13 years 9 months ago
Discovering Excitatory Networks from Discrete Event Streams with Applications to Neuronal Spike Train Analysis
—Mining temporal network models from discrete event streams is an important problem with applications in computational neuroscience, physical plant diagnostics, and human-compute...
Debprakash Patnaik, Srivatsan Laxman, Naren Ramakr...
BC
2007
107views more  BC 2007»
13 years 2 months ago
Decoding spike train ensembles: tracking a moving stimulus
We consider the issue of how to read out the information from nonstationary spike train ensembles. Based on the theory of censored data in statistics, we propose a ‘censored’ m...
Enrico Rossoni, Jianfeng Feng
NECO
2000
133views more  NECO 2000»
13 years 2 months ago
Neural Coding: Higher-Order Temporal Patterns in the Neurostatistics of Cell Assemblies
Recent advances in the technology of multi-unit recordings make it possible to test Hebb's hypothesis that neurons do not function in isolation but are organized in assemblie...
Laura Martignon, Gustavo Deco, Kathryn B. Laskey, ...
BMCBI
2010
143views more  BMCBI 2010»
13 years 2 months ago
Learning gene regulatory networks from only positive and unlabeled data
Background: Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled...
Luigi Cerulo, Charles Elkan, Michele Ceccarelli