CAREER: Biologically inspired neural network models for robust speech processing

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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.