Sparse convolutional coding for neuronal assembly detection

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PETER, Sven, Elke KIRSCHBAUM, Martin BOTH, Lee CAMPBELL, Brandon HARVEY, Conor HEINS, Daniel DURSTEWITZ, Ferran DIEGO, Fred A. HAMPRECHT, 2017. Sparse convolutional coding for neuronal assembly detection. 31st Conference on Neural Information Processing Systems (NIPS 2017). Long Beach, CA, USA, Dec 4, 2017 - Dec 9, 2017. In: GUYON, Isabelle, ed., Ulrike VON LUXBURG, ed., Samy BENGIO, ed. and others. Advances in Neural Information Processing Systems 30 (NIPS 2017)

@inproceedings{Peter2017Spars-52773, title={Sparse convolutional coding for neuronal assembly detection}, url={}, year={2017}, booktitle={Advances in Neural Information Processing Systems 30 (NIPS 2017)}, editor={Guyon, Isabelle and von Luxburg, Ulrike and Bengio, Samy}, author={Peter, Sven and Kirschbaum, Elke and Both, Martin and Campbell, Lee and Harvey, Brandon and Heins, Conor and Durstewitz, Daniel and Diego, Ferran and Hamprecht, Fred A.} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dcterms:issued>2017</dcterms:issued> <dc:contributor>Harvey, Brandon</dc:contributor> <dc:creator>Heins, Conor</dc:creator> <dc:contributor>Kirschbaum, Elke</dc:contributor> <dc:date rdf:datatype="">2021-02-09T09:17:03Z</dc:date> <dcterms:isPartOf rdf:resource=""/> <dc:creator>Both, Martin</dc:creator> <bibo:uri rdf:resource=""/> <dc:contributor>Heins, Conor</dc:contributor> <dcterms:title>Sparse convolutional coding for neuronal assembly detection</dcterms:title> <dc:contributor>Diego, Ferran</dc:contributor> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:creator>Diego, Ferran</dc:creator> <dc:contributor>Hamprecht, Fred A.</dc:contributor> <dc:creator>Durstewitz, Daniel</dc:creator> <dspace:isPartOfCollection rdf:resource=""/> <dc:language>eng</dc:language> <dc:contributor>Campbell, Lee</dc:contributor> <dc:creator>Peter, Sven</dc:creator> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:creator>Hamprecht, Fred A.</dc:creator> <dc:contributor>Peter, Sven</dc:contributor> <dcterms:available rdf:datatype="">2021-02-09T09:17:03Z</dcterms:available> <dc:creator>Harvey, Brandon</dc:creator> <dcterms:rights rdf:resource=""/> <dc:rights>terms-of-use</dc:rights> <dc:creator>Kirschbaum, Elke</dc:creator> <dc:contributor>Both, Martin</dc:contributor> <dc:creator>Campbell, Lee</dc:creator> <dcterms:abstract xml:lang="eng">Cell assemblies, originally proposed by Donald Hebb (1949), are subsets of neurons firing in a temporally coordinated way that gives rise to repeated motifs supposed to underly neural representations and information processing. Although Hebb's original proposal dates back many decades, the detection of assemblies and their role in coding is still an open and current research topic, partly because simultaneous recordings from large populations of neurons became feasible only relatively recently. Most current and easy-to-apply computational techniques focus on the identification of strictly synchronously spiking neurons. In this paper we propose a new algorithm, based on sparse convolutional coding, for detecting recurrent motifs of arbitrary structure up to a given length. Testing of our algorithm on synthetically generated datasets shows that it outperforms established methods and accurately identifies the temporal structure of embedded assemblies, even when these contain overlapping neurons or when strong background noise is present. Moreover, exploratory analysis of experimental datasets from hippocampal slices and cortical neuron cultures have provided promising results.</dcterms:abstract> <dc:contributor>Durstewitz, Daniel</dc:contributor> </rdf:Description> </rdf:RDF>

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