Evolving dendritic morphology and parameters in biologically realistic model neurons for pattern recognition

G. De Sousa; R. Maex; R. Adams; N. Davey; Volker Steuber

doi:10.1007/978-3-642-33269-2_45

Evolving dendritic morphology and parameters in biologically realistic model neurons for pattern recognition

G. De Sousa, R. Maex, R. Adams, N. Davey, Volker Steuber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

This paper addresses the problem of how dendritic topology and other properties of a neuron can determine its pattern recognition performance. In this study, dendritic trees were evolved using an evolutionary algorithm, which varied both morphologies and other parameters. Based on these trees, we constructed multi-compartmental conductance-based models of neurons. We found that dendritic morphology did have a considerable effect on pattern recognition performance. The results also revealed that the evolutionary algorithm could find effective morphologies, with a performance that was five times better than that of hand-tuned models.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer Nature Link
Pages	355-362
Number of pages	8
Volume	7552 LNCS
ISBN (Electronic)	978-3-642-33269-2
ISBN (Print)	978-3-642-33268-5
DOIs	https://doi.org/10.1007/978-3-642-33269-2_45
Publication status	Published - 2012
Event	ICANN 2012 - Lausanne, Switzerland Duration: 11 Sept 2012 → …

Conference

Conference	ICANN 2012
Country/Territory	Switzerland
City	Lausanne
Period	11/09/12 → …

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10.1007/978-3-642-33269-2_45

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De Sousa, G., Maex, R., Adams, R., Davey, N., & Steuber, V. (2012). Evolving dendritic morphology and parameters in biologically realistic model neurons for pattern recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7552 LNCS, pp. 355-362). Springer Nature Link. https://doi.org/10.1007/978-3-642-33269-2_45

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De Sousa, G, Maex, R , Adams, R, Davey, N & Steuber, V 2012, Evolving dendritic morphology and parameters in biologically realistic model neurons for pattern recognition. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7552 LNCS, Springer Nature Link, pp. 355-362, ICANN 2012, Lausanne, Switzerland, 11/09/12. https://doi.org/10.1007/978-3-642-33269-2_45

Evolving dendritic morphology and parameters in biologically realistic model neurons for pattern recognition. / De Sousa, G.; Maex, R.; Adams, R. et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7552 LNCS Springer Nature Link, 2012. p. 355-362.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - This paper addresses the problem of how dendritic topology and other properties of a neuron can determine its pattern recognition performance. In this study, dendritic trees were evolved using an evolutionary algorithm, which varied both morphologies and other parameters. Based on these trees, we constructed multi-compartmental conductance-based models of neurons. We found that dendritic morphology did have a considerable effect on pattern recognition performance. The results also revealed that the evolutionary algorithm could find effective morphologies, with a performance that was five times better than that of hand-tuned models.

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De Sousa G, Maex R , Adams R, Davey N, Steuber V. Evolving dendritic morphology and parameters in biologically realistic model neurons for pattern recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7552 LNCS. Springer Nature Link. 2012. p. 355-362 doi: 10.1007/978-3-642-33269-2_45

Evolving dendritic morphology and parameters in biologically realistic model neurons for pattern recognition

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