A Simple Model of Nonspiking Neurons

Naudin, Loïs; Laredo, Juan Luís Jiménez; Corson, Nathalie

doi:10.1162/neco_a_01531

Cited by 7 publications

(11 citation statements)

References 70 publications

(119 reference statements)

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“…We then model 3 experimentally recorded non-spiking neurons of C. elegans - RIM, AFD, and AIY, and compare the performance of EP-GAN with NSGA2, DEMO, GDE3, and NSDE. Recorded data for these neurons is publicly available and their modeling descriptions were elaborated by previous works, making them common benchmarks [16, 22, 42]. For all methods, the total training data size of 32,000 (32k HH simulated neurons) is used.…”

Section: Resultsmentioning

confidence: 99%

ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiological Responses

Kim,

Liu,

Shlizerman

2023

Preprint

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Recent advances in connectomics, biophysics, and neuronal electrophysiology warrant modeling of neurons with further details in both network interaction and cellular dynamics. Such models may be referred to as ElectroPhysiome, as they incorporate the connectome and individual neuron electrophysiology to simulate neuronal activities. The nervous system ofC. elegansis considered a viable framework for such ElectroPhysiome studies due to advances in connectomics of its somatic nervous system and electrophysiological recordings of neuron responses. In order to achieve a simulated ElectroPhysiome, the set of parameters involved in modeling individual neurons need to be estimated from electrophysiological recordings. Here, we address this challenge by developing a novel deep generative method called ElectroPhysiomeGAN (EP-GAN), which once trained, can instantly generate parameters associated with the Hodgkin-Huxley neuron model (HH-model) for neurons with graded potential response. The method combines Generative Adversarial Network (GAN) architecture with Recurrent Neural Network (RNN) Encoder and can generate an extensive number of parameters (>170) given the neuron’s membrane potential responses and steady-state current profiles. We validate our method by estimating HH-model parameters for 200 synthetic neurons with graded membrane potential followed by 9 experimentally recorded neurons (where 6 of them newly recorded) in the nervous system ofC. elegans. Compared to other methods, EP-GAN is advantageous in both accuracy of generated parameters and inference speed. In addition, EP-GAN preserves performance when provided with incomplete membrane potential responses up to 25% and steady-state current profiles up to 75%. EP-GAN is designed to leverage the generative capability of GAN to align with the dynamical structure of HH-model, and thus able to achieve such performance.

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Section: Resultsmentioning

confidence: 99%

ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiological Responses

Kim,

Liu,

Shlizerman

2023

Preprint

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“…While AIY is more sensitive to hyperpolarizing than depolarizing inputs with a transition point around −30mV, RIM is depolarized or hyperpolarized in a smooth manner due to the lack of large sustained currents. The steady-state current of these neurons characterizes and reveals its differences (Naudin et al, 2022b), so that the addition of subclasses to differentiate qualitative features between neurons of the same class could still be done based on the steady-state current shape.…”

Section: Discussionmentioning

confidence: 99%

A general pattern of non-spiking neuron dynamics under the effect of potassium and calcium channel modifications

2022

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Electrical activity of excitable cells results from ion exchanges through cell membranes, so that genetic or epigenetic changes in genes encoding ion channels are likely to affect neuronal electrical signaling throughout the brain. There is a large literature on the effect of variations in ion channels on the dynamics of spiking neurons that represent the main type of neurons found in the vertebrate nervous systems.Nevertheless, non-spiking neurons are also ubiquitous in many nervous tissues and play a critical role in the processing of some sensory systems. To our knowledge, however, how conductance variations affect the dynamics of non-spiking neurons has never been assessed. Based on experimental observations in the biological literature and on mathematical considerations, we first propose a phenotypic classification of non-spiking neurons. Then, we determine a general pattern of the phenotypic evolution of non-spiking neurons as a function of changes in calcium and potassium conductances. Furthermore, we study the homeostatic compensatory mechanisms of ion channels in a well-posed non-spiking retinal cone model. We show that there is a restricted range of ion conductance values for which the behavior and phenotype of the neuron are maintained.

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“…A simple phenomenological model of non-spiking neurons was recently developed in Naudin et al (2022b). This type of neuron is found in a wide variety of nervous tissues (Davis and Stretton, 1989b;Goodman et al, 1998;Field and Chichilnisky, 2007), encodes neuronal information in an analog manner through graded responses (Lockery et al, 2009), and plays a crucial role in the functioning of many nervous systems (Roberts and Bush, 1981;Burrows et al, 1988;Laurent and Burrows, 1989;Davis and Stretton, 1989a;Bidaye et al, 2018).…”

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confidence: 99%

“…The aim of this paper is the development of a numerical procedure to determine a relationship between the dimensionless parameters of the phenomenological non-spiking model (Naudin et al, 2022b) and the calcium conductance of a non-spiking cell, in order to reproduce its phenotypic transitions as g Ca decreases (Figure 1). In other words, the dimensionless parameters of the simple model are expressed as a function of g Ca .…”

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confidence: 99%

“…This section aims at proposing a methodology to build a simple and lightweight model that reproduces the qualitative evolution of non-spiking neuron dynamics as g Ca decreases, as described in the previous section. The simple model, described in Materials and methods, has the advantage of having a very low computational cost so that one can simulate largescale neuronal networks in real time (Naudin et al, 2022b), which is more difficult with CBMs due to their complexity (Izhikevich, 2004). Moreover, the simplicity of this model allows a theoretical mathematical analysis to gain insight into neuronal dynamics (Naudin, 2022).…”

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confidence: 99%

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Conductance-Based Phenomenological Nonspiking Model: A Dimensionless and Simple Model That Reliably Predicts the Effects of Conductance Variations on Nonspiking Neuronal Dynamics

2023

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The modeling of single neurons has proven to be an indispensable tool in deciphering the mechanisms underlying neural dynamics and signal processing. In that sense, two types of single-neuron models are extensively used: the conductance-based models (CBMs) and the so-called phenomenological models, which are often opposed in their objectives and their use. Indeed, the first type aims to describe the biophysical properties of the neuron cell membrane that underlie the evolution of its potential, while the second one describes the macroscopic behavior of the neuron without taking into account all of its underlying physiological processes. Therefore, CBMs are often used to study “low-level” functions of neural systems, while phenomenological models are limited to the description of “high-level” functions. In this letter, we develop a numerical procedure to endow a dimensionless and simple phenomenological nonspiking model with the capability to describe the effect of conductance variations on nonspiking neuronal dynamics with high accuracy. The procedure allows determining a relationship between the dimensionless parameters of the phenomenological model and the maximal conductances of CBMs. In this way, the simple model combines the biological plausibility of CBMs with the high computational efficiency of phenomenological models, and thus may serve as a building block for studying both high-level and low-level functions of nonspiking neural networks. We also demonstrate this capability in an abstract neural network inspired by the retina and C. elegans networks, two important nonspiking nervous tissues.

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A Simple Model of Nonspiking Neurons

Cited by 7 publications

References 70 publications

ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiological Responses

ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiological Responses

A general pattern of non-spiking neuron dynamics under the effect of potassium and calcium channel modifications

Conductance-Based Phenomenological Nonspiking Model: A Dimensionless and Simple Model That Reliably Predicts the Effects of Conductance Variations on Nonspiking Neuronal Dynamics

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