A multilayer-multiplexer network processing scheme based on the dendritic integration in a single neuron

Jhunlyn Lorenzo; Stéphane Binczak; Sabir Jacquir; Jhunlyn Lorenzo; Stéphane Binczak; Sabir Jacquir

doi:10.3934/Neuroscience.2022006

AIMS Neuroscience

2022, Volume 9, Issue 1: 76-113. doi: 10.3934/Neuroscience.2022006

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A multilayer-multiplexer network processing scheme based on the dendritic integration in a single neuron

1.
Laboratoire ImViA EA7535, Université de Bourgogne, 9 Avenue Alain Savary, 21078 Dijon, France
2.
College of Engineering and Information Technology, Cavite State University, Indang, 4122, Philippines
3.
Université Paris-Saclay, CNRS, Institut des Neurosciences Paris-Saclay, 91400, Saclay, France

Received: 29 November 2021 Revised: 08 February 2022 Accepted: 15 February 2022 Published: 28 February 2022

Advances in neuronal studies suggest that a single neuron can perform integration functions previously associated only with neuronal networks. Here, we proposed a dendritic abstraction employing a dynamic thresholding function that models the spatiotemporal dendritic integration process of a CA3 pyramidal neuron. First, we developed an input-output quantification process that considers the natural neuronal response and the full range of dendritic dynamics. We analyzed the IO curves and demonstrated that dendritic integration is branch-specific and dynamic rather than the commonly employed static nonlinearity. Second, we completed the integration model by creating a dendritic abstraction incorporating the spatiotemporal characteristics of the dendrites. Furthermore, we predicted the dendritic activity in each dendritic layer and the corresponding somatic firing activity by employing the dendritic abstraction in a multilayer-multiplexer information processing scheme comparable to a neuronal network. The subthreshold activity influences the suprathreshold regions via its dynamic threshold, a parameter that is dependent not only on the driving force but also on the number of activated synapses along the dendritic branch. An individual dendritic branch performs multiple integration modes by shifting from supralinear to linear then to sublinear. The abstraction includes synaptic input location-dependent voltage delay and decay, time-dependent linear summation, and dynamic thresholding function. The proposed dendritic abstraction can be used to create multilayer-multiplexer neurons that consider the spatiotemporal properties of the dendrites and with greater computational capacity than the conventional schemes.
- CA3 neuron,
- computational model,
- dendritic integration,
- dendritic nonlinearity,
- dynamic threshold,
- input-output transformation,
- neuron model
Citation: Jhunlyn Lorenzo, Stéphane Binczak, Sabir Jacquir. A multilayer-multiplexer network processing scheme based on the dendritic integration in a single neuron[J]. AIMS Neuroscience, 2022, 9(1): 76-113. doi: 10.3934/Neuroscience.2022006

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Abstract

Advances in neuronal studies suggest that a single neuron can perform integration functions previously associated only with neuronal networks. Here, we proposed a dendritic abstraction employing a dynamic thresholding function that models the spatiotemporal dendritic integration process of a CA3 pyramidal neuron. First, we developed an input-output quantification process that considers the natural neuronal response and the full range of dendritic dynamics. We analyzed the IO curves and demonstrated that dendritic integration is branch-specific and dynamic rather than the commonly employed static nonlinearity. Second, we completed the integration model by creating a dendritic abstraction incorporating the spatiotemporal characteristics of the dendrites. Furthermore, we predicted the dendritic activity in each dendritic layer and the corresponding somatic firing activity by employing the dendritic abstraction in a multilayer-multiplexer information processing scheme comparable to a neuronal network. The subthreshold activity influences the suprathreshold regions via its dynamic threshold, a parameter that is dependent not only on the driving force but also on the number of activated synapses along the dendritic branch. An individual dendritic branch performs multiple integration modes by shifting from supralinear to linear then to sublinear. The abstraction includes synaptic input location-dependent voltage delay and decay, time-dependent linear summation, and dynamic thresholding function. The proposed dendritic abstraction can be used to create multilayer-multiplexer neurons that consider the spatiotemporal properties of the dendrites and with greater computational capacity than the conventional schemes.

Abbreviations

AMPA

α-amino-3-hydroxy-5-methyl-4isoxazole-propionic acid

AMPAR

AMPA Receptor

Action Potential

GABA

γ-Aminobutyric Acid

GLM

Generalized Linear Model

Input-Output

LIF

Leaky-Integrate and Fire

LNP

Linear-Nonlinear Poisson

NMDA

N-methyl-D-aspartate

NMDAR

NMDA Receptor

STDP

Spike-timing dependent plasticity

Conflict of interest

The author declares no conflicts of interest in this paper.

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