Synaptic-type-specific clustering optimizes the computational capabilities of 
balanced recurrent networks

Giannakakis, E; Levina, A; Buendia, V; Khajehabdollahi, S

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Synaptic-type-specific clustering optimizes the computational capabilities of balanced recurrent networks

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Giannakakis, E
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Levina, A
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Buendia, V
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Khajehabdollahi, S
Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.cosyne.org/poster-session-1
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Giannakakis, E., Levina, A., Buendia, V., & Khajehabdollahi, S. (2023). Synaptic-type-specific clustering optimizes the computational capabilities of balanced recurrent networks. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2023), Montreal, Quebec, Canada.

Cite as: https://hdl.handle.net/21.11116/0000-000C-9626-0

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