Computational Models to Study Auditory Processing and Learning Disorders in Children

本研究建立了学习障碍儿童的听觉加工模型及其相应的治疗方法，建立、验证并实验了人脑听觉神经系统对音频信号的处理的计算模型。本工作将完成以下任务：。从听神经纤维一直到听觉皮层的“单一神经元通道”的建模和验证；将SNC扩展到可能包括听觉通路上的所有神经元，并在高性能计算机上验证由此产生的高度并行模型，并在通路上提供反馈；． 将听觉通路的疾病（障碍）建模为计算模型中神经元回路的故障，并研究其对通路信号处理的影响；和。通过对学习障碍儿童的实验观察，确定“计算病变”模型的神经元活动与人类脑干反应的相关性。长期目标是将语音和听力科学家通过人类听觉系统实验获得的与声学处理流相关的知识捕获到计算模型中，该项目旨在实现以下短期目标：。理解听觉处理的哪些部分应该包括在模型中；与语言和听力科学研究人员共同建立神经网络模型；和。通过对实验观测数据进行计算机模拟来验证模型。这种模型被期望预测脑干对音频信号的反应，这些音频信号通过人耳进入耳蜗，并在耳蜗中分成更精细的频谱成分，脉冲通过听神经和脑干中几个专门的核到达听觉皮层。事实上，音频信号在听觉皮层分裂成两个不同的“什么”和“在哪里”流，或者在通往它的路上，这激发了这项研究。虽然存在许多听觉系统的计算模型，但提出的研究的新颖性在于。包括计算模型中的腹流和背流。在学习障碍儿童中观察到的听觉脑干反应验证模型。对不同听觉通路的理解的研究对儿童语言障碍等各种语言和交流障碍的治疗具有广泛的意义。更广泛的影响：经过验证的听觉皮层计算模型具有进行基于计算机的实验和试验的潜力，以了解对此类疾病提出的治疗方法的影响。这样的实验可以快速，相对便宜，并作为进行涉及人类和动物的实验的先驱。此外，学生还可以获得该领域的专业知识。该研究扩展了神经元计算建模的学科，并有助于理解儿童学习障碍的原因，从而促进相应治疗的发展。然而，目前尚不清楚这种计算模型需要什么样的训练来模拟实验观察到的行为，然后提出可以通过实验验证的行为。计算模型将用于回答“如果”的问题。

This work, modeling auditory processing in children with learning disabilities and its corresponding treatments, builds, validates, and experiments with computational models the processing of audio signals in the auditory nervous system of the human brain. The following tasks will be completed during this work:. Modeling and validation of a "single neuronal channel (SNC)" along the pathway from the auditory nerve fiber all the way to the auditory cortex;. Expansion of the SNC to include possibly all neurons along the auditory pathway and validation of the resulting highly parallel model, with feedback along the pathway, on a high performance computer; . Modeling of diseases (disorders) of the auditory pathway as faults in the neuronal circuitry in the computational model and studying their impact on signal processing along the pathway; and. Determining the correlation of the neuronal activity of the "computationally diseased" model with the human brainstem response observed through experimentation with learning-impaired children.With a long-term goal for capturing into a computational model the knowledge related to the acoustic processing streams acquired by speech and hearing scientists through experiments with the human auditory system, the project aims to accomplish the following short-term goals: . Understanding what portions of the auditory processing ought to be included in the model;. Building a neural network model in consultation with researchers in speech and hearing sciences; and. Validating the model by running computer simulations against experimentally observed data.Such a model was expected to predict brainstem responses to audio signals that come in via the human ear and partition into its finer spectral components in the cochlea from where the impulses travel via the auditory nerve and several specialized nuclei in the brainstem to the auditory cortex. The fact that the audio signals split into two distinct "what" and "where" streams in the auditory cortex or en-route to it motivates the research. Although many computational models of the auditory system exist, the novelty of the proposed research lies in. Including the ventral and dorsal streams in the computational model and. Validating the model against auditory brainstem responses observed in children with learning disorders.Research into the understanding of the different auditory pathways has wide ranging implication in the treatment of various speech and communications disorders such as language impairment in children.Broader Impacts: Validated computational models of the auditory cortex carry the potential for performing computer-based experiments and trials to understand the impact of proposed treatments for such disorders. Such experiments can be fast, relatively inexpensive, and serve as a precursor to conduct experiments involving humans and animals. Additionally, students also gain expertise in the area. The research expands the discipline of neuron-computational modeling and should contribute in understanding the causes of learning disabilities in children, thus enabling development of the corresponding treatment. However, it is unclear as to what type of training will be needed for such computational model to mimic the experimentally observed behaviors and then to suggest behaviors that can be verified experimentally. The computational model will be used to answer "what if" questions.