RI: Small: Temporal and Spatiotemporal Processing in Recurrent Neural Networks with Unsupervised Learning

大脑执行复杂形式的模式识别（如语音识别）的能力远远超过最好的计算机程序。人类模式识别的优势之一是它对刺激的时间结构和时间特征的无缝处理。例如，短语“他给了她猫粮”可以传达两种不同的含义，这取决于说话者是强调“她”和“猫”之间的停顿，还是“猫”和“食物”之间的停顿。“试图利用人工神经网络来模仿大脑区分这种模式的能力，只取得了有限的成功。然而，这些模型传统上没有捕捉到大脑如何处理时间信息。事实上，这些模型中的大多数都将时间视为等同于空间维度，本质上假设相同的输入以不同的延迟进行缓冲和播放。类似地，更传统的模式识别方法通常依赖于离散的时间仓，也不能捕捉大脑如何处理时间信息。目前研究的目标是使用一个框架，称为状态依赖网络或水库计算，来模拟大脑处理刺激的空间和时间特征的能力。这个框架的一个关键组成部分是，时间信息自动编码的网络状态之间的相互作用的结果输入的刺激和内部状态的递归网络。本项目将开发一个通用的模型的时空模式识别侧重于语音歧视。该模型将纳入可塑性，这是大脑的一个关键特征，这是以前的状态依赖网络模型所无法实现的。可塑性是大脑计算能力的一个重要特征。例如，在语音识别的背景下，即使在6个月大的时候，婴儿的大脑也会被调整以识别他们母语的声音。这种能力是经验依赖性皮层可塑性的一个例子，它部分依赖于突触可塑性和皮层重组。由于递归网络固有的非线性和反馈动力学，将突触可塑性转化为递归网络已被证明是一个非常具有挑战性的问题。目前的项目将使用一种新的无监督形式的突触可塑性-基于经验观察到的可塑性形式，称为稳态突触可塑性-赋予状态依赖网络适应和自我调节网络所暴露的刺激集的能力。该项目将理论神经科学的最新进展与机器学习的新方法结合起来。这些结果将有助于开发人工神经网络，以捕捉大脑处理时间信息并根据经验进行重组的能力。

The brain's ability to perform complex forms of pattern recognition, such as speech discrimination, far exceeds that of the best computer programs. One of the strengths of human pattern recognition is its seamless processing of the temporal structure and temporal features of stimuli. For example, the phrase "he gave her cat food" can convey two different meanings depending on whether the speaker emphasizes the pause between "her" and "cat," or "cat" and "food." Attempts to emulate the brain's ability to discriminate such patterns using artificial neural networks have had only limited success. These models, however, have traditionally not captured how the brain processes temporal information. Indeed most of these models have treated time as equivalent to a spatial dimension, in essence assuming that the same input is buffered and played at different delays. Similarly, more traditional approaches to pattern recognition, which generally rely on discrete time bins, also do not capture how the brain processes temporal information. The goal of the current research is to use a framework, referred to as state-dependent networks or reservoir computing, to simulate the brain's ability to process both the spatial and temporal features of stimuli. A critical component of this framework is that temporal information is automatically encoded in the state of the network as a result of the interaction between incoming stimuli and internal states of recurrent networks.This project will develop a general model of spatiotemporal pattern recognition focusing on speech discrimination. The model will incorporate plasticity, a critical characteristic of the brain that has eluded previous state-dependent network models. Plasticity is a cardinal feature of the brain's computational power. For example, in the context of speech recognition, even at the age of 6 months, the brains of babies are tuned to recognize sounds of their native language. This ability is an example of experience-dependent cortical plasticity and it relies in part on synaptic plasticity and cortical reorganization. Incorporating synaptic plasticity into recurrent networks has proven to be a very challenging problem as a result of the inherent nonlinear and feedback dynamics of recurrent networks. The current project will use a novel unsupervised form of synaptic plasticity--based on empirically observed forms of plasticity referred to as homeostatic synaptic plasticity--to endow state-dependent networks with the ability to adapt and self-tune to the stimulus set the network is exposed to. This project interfaces recent advances in theoretical neuroscience and novel approaches in machine learning. The results will help develop artificial neural networks that capture the brain's ability to process temporal information and reorganize in response to experience.