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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.

时间维度对于理解大脑至关重要，因为大脑的主要功能之一本质上是时间的：大脑利用过去的信息（记忆）来预测未来。由于大脑功能固有的时间性质，大脑已经进化出了识别时间、编码时间和执行时间相关计算的机制。这些计算赋予动物快速学习预测外部事件的能力（例如，当红灯应该改变时），并识别和生成复杂的时间模式（例如，语音或莫尔斯电码的基础）。大脑计算能力的一个特征被称为“时间缩放”，例如，以不同的速度说话、播放音乐或敲击摩尔斯电码的能力。时间和时间尺度背后的神经机制仍然知之甚少。此外，尽管机器学习领域已经取得了巨大的进步，但目前的机器学习方法并没有捕捉到大脑如何执行时间计算或实现时间缩放。新出现的实验数据表明，大脑可能通过许多不同的动态机制来编码时间并实现时间尺度，包括斜坡（随着时间增加放电率）或神经序列（神经元的短暂顺序激活）。该项目旨在了解如何在循环神经网络中执行与时间相关的计算，并提出神经序列为时间尺度问题提供了最佳解决方案。这个项目将有助于提高人工系统捕捉大脑计算能力的能力。相关的教育和推广工作与研究密切相关。将采用两种主要方法。首先，基于机器学习的监督递归神经网络将在许多不同的定时任务上进行训练——包括需要以不同速度产生复杂时间模式的莫尔斯电码任务——以确定神经序列是否代表编码时间和时间尺度问题的一般解决方案。其次，当前机器学习方法中大部分缺失的神经元和突触特性将用于开发神经序列如何以生物学上合理的方式出现和经历时间缩放的模型。具体来说，皮质突触表现出短期突触可塑性，其中突触的强度在数百毫秒的过程中以一种依赖于使用的方式变化，这些动态反过来可以被调节——加速或减缓短期突触可塑性。据推测，这种短期突触可塑性的调节是大脑实现时间尺度的一种方式。总的来说，这个项目将导致新的生物学原理应用于机器学习，并进一步提高模拟大脑计算策略的能力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。