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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

基本信息

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
项目类别：
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

项目摘要

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.

摘要