RI: Small: Robust Auditory Object Recognition with Spike Sequence Coding and the State-Dependent Dynamics of Cortical Networks

RI：小：具有尖峰序列编码和状态相关的皮质网络动力学的鲁棒听觉对象识别

• 批准号: 1116530
• 负责人: Dezhe Jin
• 金额: $ 29.65万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116530&HistoricalAwards=false
• 关键词: RI; Small; Robust; Auditory; Object

Recognizing speech or other auditory objects in adverse environments -- e.g. with noise, reverberation, and multiple speakers -- is essential for human and animal communication. Current speech recognition technologies work well in high signal-to-noise conditions, but perform orders of magnitude below human performance in adverse conditions. Converging evidence from neuroscience suggests that auditory information is encoded in sparse and precisely timed spikes of sub-cortical neurons. However, the extent to which codes based on spike timing might underlie the robustness of human auditory object recognition has not yet been fully investigated. This project bridges this gap by devising a biologically inspired computational model of auditory processing at the cortical level and extracting computational principles that are essential for the model to achieve robust auditory object recognition.The approach is to transform sounds into the spike sequences generated by feature-detecting thalamic auditory neurons, and to integrate these spikes spatially and temporally using the state-dependent dynamics of cortical neurons with active dendrites. In the proposed model, an auditory object first evokes sequential spiking of thalamic neurons that have been trained to detect useful features. Then, through feed-forward excitation and inhibition from the thalamus, and lateral excitation and inhibition from the cortical neurons, the state of the cortical network evolves, leading to temporal integration. Recognition of the auditory object is signaled when the cortical neurons reach a specific network state. The computational model is constrained by experimental results on the properties of cortical neurons, the organization principles of cortical networks, and the activity-dependent plasticity rules of the network structures. The project aims both to design feature detectors that can robustly represent auditory objects with spatiotemporal spike sequences, and to build a cortical network model that can recognize specific auditory objects using state transitions driven by the thalamic inputs, with neuron dynamics that can be compared with those observed in the auditory cortex. The recognition performance of the computational model will be evaluated and improved with auditory tasks designed to compare different approaches to speech recognition.

在不利环境中识别语音或其他听觉对象 - 例如噪音、混响和多个扬声器——对于人类和动物的交流至关重要。 当前的语音识别技术在高信噪比条件下工作良好，但在不利条件下的表现低于人类表现。来自神经科学的证据表明，听觉信息被编码在稀疏且精确定时的皮层下神经元尖峰中。然而，基于脉冲定时的代码在多大程度上可能成为人类听觉对象识别的鲁棒性基础，尚未得到充分研究。该项目通过设计一种受生物学启发的皮层听觉处理计算模型并提取该模型实现鲁棒听觉对象识别所必需的计算原理来弥补这一差距。该方法是将声音转换为由特征检测丘脑听觉神经元生成的尖峰序列，并利用皮质神经元的状态依赖动力学在空间和时间上整合这些尖峰与主动 树突。在所提出的模型中，听觉对象首先引起丘脑神经元的连续尖峰，这些神经元已被训练来检测有用的特征。然后，通过丘脑的前馈兴奋和抑制，以及皮质神经元的横向兴奋和抑制，皮质网络的状态发生演变，导致时间整合。当皮层神经元达到特定的网络状态时，就会发出对听觉对象的识别信号。计算模型受到皮层神经元特性、皮层网络的组织原理以及网络结构的活动依赖性可塑性规则的实验结果的约束。该项目的目标是设计能够通过时空尖峰序列稳健地表示听觉对象的特征检测器，并构建一个皮层网络模型，该模型可以使用丘脑输入驱动的状态转换来识别特定的听觉对象，并且神经元动力学可以与听觉皮层中观察到的神经元动力学进行比较。计算模型的识别性能将通过旨在比较不同语音识别方法的听觉任务进行评估和改进。