Developing novel neural network tools for accurate and interpretable dynamical modeling of neural circuits

开发新型神经网络工具，用于准确且可解释的神经回路动态建模

• 批准号: 10752956
• 负责人: Christopher Versteeg
• 金额: $ 7.66万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10752956
• 关键词: Address; Affect; Architecture; Area; Behavior; Biological; Brain; Communication; Complex; Computer Models; Coupled; Coupling; Data; Data Set; Decision Making; Differential Equation; Dimensions; Dorsal; Electrodes; Generations; Geometry; Goals; Human; Individual; Intuition; Investigation; Learning; Measurement; Methodology; Methods; Modeling; Monkeys; Motor; Motor Cortex; Movement; Neural Network Simulation; Neurons; Neurosciences; Output; Pattern; Performance; Population; Publishing; Rewards; Speed; System; Systems Theory; Testing; Time; Training; Variant; artificial neural network; autoencoder; biological systems; cognitive process; dynamic system; frontal lobe; in silico; innovation; insight; invention; multidimensional data; neural; neural circuit; neural model; neural network; neural network architecture; neuromechanism; neuronal patterning; novel; reconstruction; recurrent neural network; terabyte; theories; time interval; tool

Abstract In recent years, the number of neurons that we can record simultaneously has seen an exponential increase, presenting a daunting challenge: how do we analyze these complex and high-dimensional datasets to gain insight into how neural circuits perform computation? Tools from dynamical systems theory have successfully unraveled the computational machinery of artificial recurrent neural networks (RNNs) trained to perform goal-directed tasks. If we could apply these tools to biological neural circuits, it would provide unparalleled access to the inner workings of the brain and potentially allow us to connect theories of neural computation to real biological data. However, for these tools to be useful, we need to create in silico replicas whose dynamics faithfully represent the dynamics of the underlying biological system. To date, the best in silico replicas of biological networks are RNNs trained to produce output that matches recorded patterns of neuronal firing. While this approach is rapidly growing in popularity, it has critical flaws. Current training methodologies are not constrained to produce accurate representations of the underlying dynamics; in fact, RNNs are actually rewarded for inventing superfluous dynamics, so long as those dynamics help to reproduce recorded neural data. Additionally, these models often assume that the relationship (“embedding”) between latent activity and neural firing rates is linear; when this assumption proves false, the dynamical accuracy suffers. The problems of superfluous dynamics and non-linear embedding are especially severe when attempting to model a system of interacting neural circuits. The objective of this proposal is to develop a novel artificial neural network architecture that addresses the above challenges and allows our in-silico models to capture accurate dynamics that are built both within and across-circuits. My approach combines two key components: 1) neural ordinary differential equations (NODEs), a computational architecture that we have demonstrated learns dynamics more accurately and compactly than RNNs and 2) invertible neural network (INN) readouts, which eliminate superfluous dynamics and allow the model to approximate nonlinear embeddings. I will validate the ability of this model, called an Ordinary Differential equation auto-encoder with Invertible readout (ODIN), to find accurate within- and across-circuit dynamics using synthetic neural data and previously-collected multi-electrode recordings from monkeys. This tool will help to build a bridge between neural data and both local and distributed neural computations.

抽象的 近年来，我们可以同时记录的神经元数量呈指数增长 增加，提出了一个艰巨的挑战：我们如何分析这些复杂和高维的 数据集以深入了解神经电路如何执行动力系统的计算？ 理论成功地揭示了人工循环神经网络的计算机制 （RNN）经过训练可以执行目标导向的任务，如果我们可以将这些工具应用于生物神经回路， 它将提供无与伦比的进入大脑内部运作的途径，并有可能让我们 将神经计算理论与真实的生物数据联系起来然而，为了使这些工具有用， 我们需要创建计算机复制品，其动态忠实地代表底层的动态 生物系统。 迄今为止，生物网络的最佳计算机复制品是 RNN，经过训练可产生以下输出： 匹配记录的神经放电模式虽然这种方法正在迅速流行，但它已经 当前的训练方法不限于产生准确的表示。 事实上，RNN 实际上是因为发明了多余的动力学而获得了奖励，所以 此外，这些模型通常有助于重现记录的神经数据。 假设潜在活动和神经放电率之间的关系（“嵌入”）是线性的； 这个假设被证明是错误的，动态精度会受到多余动态的问题。 当尝试对交互系统进行建模时，非线性嵌入尤其严重 神经回路。 该提案的目标是开发一种新颖的人工神经网络架构 解决了上述挑战，并允许我们的计算机模型捕获准确的动态 我的方法结合了两个关键组成部分：1）普通神经网络。 微分方程（NODE），我们已经证明可以学习的计算架构 动力学比 RNN 更准确、更紧凑，2) 可逆神经网络 (INN) 读数， 这消除了多余的动力学并允许模型近似非线性嵌入。 验证该模型的能力，称为具有可逆的常微分方程自动编码器 读出（ODIN），使用合成神经数据找到准确的电路内和电路间动态 之前从猴子身上收集的多电极录音将有助于建立一座桥梁。 神经数据与本地和分布式神经计算之间的关系。