Harkin Emerson F, Shen Peter R, Goel Anish, Richards Blake A, Naud Richard
uOttawa Brain and Mind Institute, Centre for Neural Dynamics, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada.
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada.
Neuroscience. 2022 May 1;489:200-215. doi: 10.1016/j.neuroscience.2021.07.026. Epub 2021 Aug 3.
Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysical models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option for capturing the complexities of real neural computation is to use cascade models, which treat individual neurons as a cascade of linear and non-linear operations, akin to a multi-layer artificial neural network. Recent research has shown that cascade models can capture single-cell computation well, but there are still a number of sub-cellular, regenerative dendritic phenomena that they cannot capture, such as the interaction between sodium, calcium, and NMDA spikes in different compartments. Here, we propose that it is possible to capture these additional phenomena using parallel, recurrent cascade models, wherein an individual neuron is modelled as a cascade of parallel linear and non-linear operations that can be connected recurrently, akin to a multi-layer, recurrent, artificial neural network. Given their tractable mathematical structure, we show that neuron models expressed in terms of parallel recurrent cascades can themselves be integrated into multi-layered artificial neural networks and trained to perform complex tasks. We go on to discuss potential implications and uses of these models for artificial intelligence. Overall, we argue that parallel, recurrent cascade models provide an important, unifying tool for capturing single-cell computation and exploring the algorithmic implications of physiological phenomena.
神经元是非常复杂的计算装置，包含众多非线性过程，尤其是在其树突中。生物物理模型通过明确模拟生理变量，如离子通道、电流流动、膜电容等，直接捕捉这些过程。然而，捕捉真实神经计算复杂性的另一种选择是使用级联模型，该模型将单个神经元视为线性和非线性操作的级联，类似于多层人工神经网络。最近的研究表明，级联模型能够很好地捕捉单细胞计算，但仍有一些亚细胞的、再生性的树突现象无法捕捉，比如不同隔室中钠、钙和NMDA尖峰之间的相互作用。在此，我们提出可以使用并行递归级联模型来捕捉这些额外的现象，其中单个神经元被建模为并行线性和非线性操作的级联，这些操作可以递归连接，类似于多层递归人工神经网络。鉴于其易于处理的数学结构，我们表明以并行递归级联形式表达的神经元模型本身可以集成到多层人工神经网络中，并经过训练以执行复杂任务。我们接着讨论这些模型对人工智能的潜在影响和用途。总体而言，我们认为并行递归级联模型为捕捉单细胞计算和探索生理现象的算法含义提供了一个重要的统一工具。
