Learning probabilistic models of connectivity from multiple spike train data
| dc.contributor.author | Patnaik, Debprakash | en |
| dc.contributor.author | Laxman, Srivatsan | en |
| dc.contributor.author | Ramakrishnan, Naren | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2012-08-24T11:19:09Z | en |
| dc.date.available | 2012-08-24T11:19:09Z | en |
| dc.date.issued | 2010-07-20 | en |
| dc.date.updated | 2012-08-24T11:19:09Z | en |
| dc.description.abstract | Neuronal circuits or cell assemblies carry out brain function through complex coordinated firing patterns [1]. Inferring topology of neuronal circuits from simultaneously recorded spike train data is a challenging problem in neuroscience. In this work we present a new class of dynamic Bayesian networks to infer polysynaptic excitatory connectivity between spiking cortical neurons [2]. The emphasis on excitatory networks allows us to learn connectivity models by exploiting fast data mining algorithms. Specifically, we show that frequent episodes help identify nodes with high mutual information relationships and can be summarized into a dynamic Bayesian network (DBN). | en |
| dc.description.version | Published version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | BMC Neuroscience. 2010 Jul 20;11(Suppl 1):P171 | en |
| dc.identifier.doi | https://doi.org/10.1186/1471-2202-11-S1-P171 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/18828 | en |
| dc.language.iso | en | en |
| dc.rights | Creative Commons Attribution 4.0 International | en |
| dc.rights.holder | Debprakash Patnaik et al.; licensee BioMed Central Ltd. | en |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en |
| dc.title | Learning probabilistic models of connectivity from multiple spike train data | en |
| dc.title.serial | BMC Neuroscience | en |
| dc.type | Poster | en |
| dc.type.dcmitype | Text | en |