RI: Small: Neural Sequences as a Robust Dynamic Regime for Spatiotemporal Time Invariant Computations.

RI：小：神经序列作为时空时不变计算的鲁棒动态机制。

基本信息

批准号：
2008741
负责人：
Dean Buonomano
金额：
$ 49.96万
依托单位：
University of California-Los Angeles
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008741&HistoricalAwards=false
关键词：
RI Small Neural Sequences Robust

项目摘要

The temporal dimension is of fundamental importance to understanding the brain because one of the brain's primary function is temporal in nature: the brain uses information about the past (memories) to predict the future. As a result of the inherently temporal nature of brain function the brain has evolved mechanisms to tell time, encode time, and perform time-dependent computations. These computations endow animals with the ability to quickly learn to anticipate external events (for example when a red light should change), and to recognize and generate complex temporal patterns (such as those that underlie speech or Morse code). A feature of the brain's computational abilities is referred to as "temporal scaling," for example, the ability to talk, play music, or tap a Morse code message at different speeds. The neural mechanisms underlying timing and temporal scaling remain poorly understood. Furthermore, although dramatic advances have taken place in the field of machine learning, current machine learning approaches do not capture how the brain performs temporal computations or achieves temporal scaling. Emerging experimental data suggest that the brain may encode time and implement temporal scaling through a number of different dynamic regimes including ramping (increasing firing rates with time) or neural sequences (transient sequential activation of neurons). This project seeks to understand how time-dependent computations are performed in recurrent neural networks, and proposes that neural sequences provide an optimal solution to the problem of temporal scaling. This project will contribute to advances in the ability of artificial systems to capture the computational power of the brain. Associated education and outreach efforts are closely related to the research.Two main approaches will be used. First, machine-learning based supervised recurrent neural networks will be trained on a number of different timing tasks--including a Morse code task that requires producing a complex temporal pattern at different speeds--in order to determine if neural sequences represent a general solution to the problems of encoding time and temporal scaling. Second, neuronal and synaptic properties that are mostly absent from current machine learning approaches will be used to develop a model of how neural sequences emerge and undergo temporal scaling in a biologically plausible fashion. Specifically, cortical synapses exhibit short-term synaptic plasticity, in which the strength of synapses change in a use-dependent manner over the course of hundreds of milliseconds, these dynamics can in turn be modulated--accelerating or slowing short-term synaptic plasticity. It is hypothesized that this modulation of short-term synaptic plasticity is one way the brain implements temporal scaling. Overall, this project will lead to novel biological principles being applied towards machine learning, and further advance the ability to emulate the brain’s computational strategies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

暂时的维度对于理解大脑至关重要，因为大脑的主要功能在本质上是暂时的：大脑使用有关过去（记忆）的信息来预测未来。由于大脑功能的固有暂时性，大脑具有进化的机制来分解时间，编码时间和执行时间依赖性计算。这些计算赋予动物能够快速学会预测外部事件的能力（例如，当红灯应发生变化），并识别和生成复杂的临时模式（例如那些是基于语音或莫尔斯代码的临时模式）。大脑计算能力的一个功能称为“时间缩放”，例如，以不同速度说话，播放音乐或点击摩尔斯的代码消息的能力。定时和临时缩放的基础神经元机制仍然很少理解。此外，尽管在机器学习领域已经取得了巨大进步，但当前的机器学习方法并未捕获大脑如何执行临时计算或实现临时缩放。新兴的实验数据表明，大脑可以通过多种不同的动态状态编码时间并实施临时缩放，包括渐变（随时间提高点火速率）或神经元（神经元的短暂顺序激活）。该项目旨在了解如何在复发性神经网络中执行时间依赖性计算，并提出神经序列为临时缩放问题提供了最佳解决方案。该项目将有助于人造系统捕获大脑计算能力的能力的进步。相关的教育和外展工作与研究密切相关。将使用两种主要方法。首先，将基于机器学习的重复神经元网络对许多不同的计时任务进行培训 - 包括需要以不同速度生产复杂的临时模式的摩尔斯代码任务 - 以确定神经元序列是否代表了编码时间和临时缩放问题的一般解决方案。其次，当前机器学习方法中大多不存在的神经元和突触特性将用于开发一个模型，以了解神经元序列如何以生物学上合理的方式出现并进行临时缩放。具体而言，皮质突触暴露了短期突触可塑性，其中突触的强度在数百毫秒的过程中以使用依赖性方式变化，这些动态又可以调节 - 加速或减慢短期突触可变性。假设这种短期突触可塑性的调节是大脑实现临时缩放的一种方式。总体而言，该项目将导致新颖的生物学原理用于机器学习，并进一步促进模仿大脑的计算策略的能力。该奖项反映了NSF的法定任务，并通过评估基金会的智力优点和更广泛的影响来评估NSF的法定任务。