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

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：大脑使用关于过去(记忆)的信息来预测未来。由于大脑功能固有的时间性，大脑进化出了告诉时间、编码时间和执行与时间相关的计算的机制。这些计算赋予动物快速学习预测外部事件(例如红灯何时改变)的能力，并识别和生成复杂的时间模式(例如语音或莫尔斯电码背后的模式)。大脑计算能力的一个特征被称为“时间尺度”，例如，以不同的速度说话、播放音乐或轻敲摩尔斯电码信息的能力。时序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和执行时间尺度，包括斜坡(随时间增加的放电率)或神经序列(神经元的瞬时顺序激活)。这个项目试图了解递归神经网络中依赖时间的计算是如何执行的，并提出神经序列为时间缩放问题提供了一个最佳解决方案。该项目将有助于提高人工系统捕捉大脑计算能力的能力。相关的教育和外展工作与研究密切相关。将采用两种主要方法。首先，基于机器学习的有监督的递归神经网络将在许多不同的计时任务上进行训练--包括需要以不同速度产生复杂的时间模式的莫尔斯电码任务--以便确定神经序列是否代表编码时间和时间缩放问题的一般解决方案。其次，目前机器学习方法大多缺乏的神经元和突触特性将被用来开发一个模型，说明神经序列是如何以生物学上可信的方式出现和经历时间缩放的。具体地说，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依赖于使用的方式变化，这些动态反过来可以被调制--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制是大脑实现时间尺度的一种方式。总体而言，该项目将导致将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NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.