A neural model for V1 that incorporates dendritic nonlinearities and back-propagating action potentials.

IF 4 2区 医学 Q1 NEUROSCIENCES
Ilias Rentzeperis, Dario Prandi, Marcelo Bertalmío
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引用次数: 0

Abstract

The work of Hubel and Wiesel has been instrumental in shaping our understanding of V1, leading to modeling neural responses as cascades of linear and nonlinear processes in what is known as the "standard model" of vision. Under this formulation, however, some dendritic properties cannot be represented in a practical manner, while evidence from both experimental and theoretical work indicates that dendritic processes are an indispensable element of key neural behaviors. As a result, current V1 models fail to explain neural responses in a number of scenarios. In this work, we propose an implicit model for V1 that considers nonlinear dendritic integration and backpropagation of action potentials from the soma to the dendrites. Our model can be viewed as an extension of the standard model that minimizes an energy function, allows for a better conceptual understanding of neural processes, and explains several neurophysiological phenomena that have challenged classical approaches.Significance Statement Most current approaches for modeling neural activity in V1 are data driven; their main goal is to obtain better predictions and are formally equivalent to a deep neural network (DNN). Aside from behaving like a black-box these models ignore a key property of biological neurons, namely, that they integrate their input via their dendrites in a highly nonlinear fashion that includes backpropagating action potentials (bAPs). Here, we propose a model based on dendritic mechanisms, which facilitates conceptual analysis and can explain a number of physiological results that challenge standard approaches. Our results suggest that the proposed model may provide a better understanding of neural processes and be considered as a contribution in the search of a consensus model for V1.

一个包含树突非线性和反向传播动作电位的V1神经模型。
Hubel和Wiesel的工作有助于塑造我们对V1的理解,从而将神经反应建模为线性和非线性过程的级联,即所谓的视觉“标准模型”。然而,在这种表述下,一些树突特性无法以实际方式表示,而来自实验和理论工作的证据表明,树突过程是关键神经行为不可或缺的元素。因此,目前的V1模型无法解释许多情况下的神经反应。在这项工作中,我们提出了一个隐式的V1模型,该模型考虑了非线性树突积分和动作电位从体细胞到树突的反向传播。我们的模型可以看作是标准模型的扩展,它最小化了能量函数,允许对神经过程有更好的概念理解,并解释了一些挑战经典方法的神经生理现象。目前大多数对V1神经活动建模的方法都是数据驱动的;他们的主要目标是获得更好的预测,并且在形式上相当于深度神经网络(DNN)。除了表现得像一个黑箱之外,这些模型忽略了生物神经元的一个关键特性,即它们通过树突以高度非线性的方式整合输入,其中包括反向传播动作电位(bAPs)。在这里,我们提出了一个基于树突机制的模型,它有助于概念分析,并可以解释一些挑战标准方法的生理结果。我们的研究结果表明,所提出的模型可以更好地理解神经过程,并被认为是对寻找V1共识模型的贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Neuroscience
Journal of Neuroscience 医学-神经科学
CiteScore
9.30
自引率
3.80%
发文量
1164
审稿时长
12 months
期刊介绍: JNeurosci (ISSN 0270-6474) is an official journal of the Society for Neuroscience. It is published weekly by the Society, fifty weeks a year, one volume a year. JNeurosci publishes papers on a broad range of topics of general interest to those working on the nervous system. Authors now have an Open Choice option for their published articles
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