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Parallel Functional Architectures Within a Single Dendritic Tree

27 Pages Posted: 1 Mar 2022 Publication Status: Published

See all articles by Young Joon Kim

Young Joon Kim

University of Cambridge - Computational and Biological Learning Laboratory

Balázs Ujfalussy

Institute of Experimental Medicine - Laboratory of Biological Computation

Máté Lengyel

University of Cambridge - Computational and Biological Learning Laboratory

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Abstract

The input-output transformation of individual neurons is a key building block of neural circuit dynamics. While previous models of this transformation vary widely in their complexity, they all describe the underlying functional architecture as unitary, performing a single type of elementary computation, either once or cascaded multiple times. Here, we show that the input-output transformation of CA1 pyramidal cells is instead best captured by two distinct functional architectures operating in parallel. We used statistically principled methods to fit flexible, yet interpretable models of the transformation of spatiotemporal patterns of input spikes into the somatic "output'' voltage, and to automatically select among alternative functional architectures. The contribution of NMDA-nonlinearities was accurately captured by a single static and global nonlinearity. In contrast, dendritic Na+ spikes required a functional architecture with multiple dynamic nonlinearities and clustered connectivity. These two architectures incorporated distinct morphological and biophysical properties of the neuron and its synaptic organization.

Keywords: neuron, dendrite, dendritic spike, dendritic nonlinearity, neuron model

Suggested Citation

Kim, Young Joon and Ujfalussy, Balázs and Lengyel, Máté, Parallel Functional Architectures Within a Single Dendritic Tree. Available at SSRN: https://ssrn.com/abstract=4047251 or http://dx.doi.org/10.2139/ssrn.4047251
This version of the paper has not been formally peer reviewed.

Young Joon Kim (Contact Author)

University of Cambridge - Computational and Biological Learning Laboratory ( email )

Cambridge
United Kingdom

Balázs Ujfalussy

Institute of Experimental Medicine - Laboratory of Biological Computation ( email )

Orszaghaz utca 30.
Budapest, H-1502
Hungary

Máté Lengyel

University of Cambridge - Computational and Biological Learning Laboratory ( email )

Trinity Ln
Cambridge, CB2 1TN
United Kingdom

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