CAREER: Biologically inspired neural network models for robust speech processing

最近在深度神经网络模型和神经成像技术方面取得的并行突破极大地推进了人工和生物计算的现状。然而，这两个学科之间几乎没有相互作用，导致神经系统模型过于简单，预测，学习和泛化能力有限。该项目的目标是创建一个连贯的理论和数学框架，以了解生物神经网络的独特功能的计算作用，它们对形成强大的信号表示的贡献，并将其建模和集成到当前的人工神经网络中。这些新的生物启发模型和算法将具有自适应和认知能力，将更好地预测实验观察，并将推进大脑如何处理语音的知识。此外，这些模型的性能应该接近人类在模拟认知功能的任务中的能力，并将激励新的实验，这些实验可以进一步对模型施加现实的约束。这个跨学科项目位于神经语言学，语音工程和机器学习的交叉点，将神经科学和工程的历史上分离的学科结合起来。所提出的创新方法整合了各个学科的方法和专业知识，包括系统识别，信号处理，神经生理学和系统神经科学。该提案的目的是通过三个具体目标分析和转换人工神经网络模型，以准确反映生物系统的计算和组织原理：I）创建分析方法，通过检查人工神经网络模型的代表性属性和特征编码，可以深入了解人工神经网络模型中发生的转换，II）建模和实现本地的，自下而上的，适应性神经机制，普遍存在于生物系统中，和III）建模自上而下，知识驱动的认知系统的能力，以实现新的计算，以响应任务的要求。神经转换的精确计算模型将对包括人工智能、神经语言学和系统神经科学在内的许多学科产生重大影响。更现实的神经网络模型不仅会产生类似人类的模式识别技术，更好地理解大脑如何解决语音感知，而且还可以帮助解释这些过程是如何在言语和语言障碍患者中受损的。因此，拟议的项目将推进多个学科的最先进水平。

The recent parallel breakthroughs in deep neural network models and neuroimaging techniques have significantly advanced the current state of artificial and biological computing. However, there has been little interaction between these two disciplines, resulting in simplistic models of neural systems with limited prediction, learning and generalization abilities. The goal of this project is to create a coherent theoretical and mathematical framework to understand the computational role of distinctive features of biological neural networks, their contribution to the formation of robust signal representations, and to model and integrate them into the current artificial neural networks. These new bio-inspired models and algorithms will have adaptive and cognitive abilities, will better predict experimental observations, and will advance the knowledge of how the brain processes speech. In addition, the performance of these models should approach human abilities in tasks mimicking cognitive functions, and will motivate new experiments that can further impose realistic constraints on the models. This interdisciplinary project lies at the intersection of neurolinguistics, speech engineering, and machine learning, uniting the historically separated disciplines of neuroscience and engineering. The proposed innovative approach integrates methods and expertise across various disciplines, including system identification, signal processing, neurophysiology, and systems neuroscience. The aim of this proposal is to analyze and transform the artificial neural network models to accurately reflect the computational and organizational principles of biological systems through three specific objectives: I) to create analytic methods that can provide insights into the transformations that occur in artificial neural network models by examining their representational properties and feature encoding, II) to model and implement the local, bottom-up, adaptive neural mechanisms that appear ubiquitously in biological systems, and III) to model the top-down, knowledge driven abilities of cognitive systems to implement new computations in response to the task requirements. Accurate computational models of the neural transformations will have an overarching impact in many disciplines including artificial intelligence, neurolinguistics, and systems neuroscience. More realistic neural network models will not only result in human-like pattern recognition technologies and better understanding of how the brain solves speech perception, but can also help explain how these processes are impaired in people with speech and language disorders. Therefore, the proposed project will advance the state-of-the-art in multiple disciplines.