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

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——对人类和动物的交流至关重要。目前的语音识别技术在高信噪比条件下工作良好，但在恶劣条件下的表现比人类的表现低几个数量级。来自神经科学的越来越多的证据表明，听觉信息被编码在皮层下神经元的稀疏和精确定时的尖峰中。然而，基于脉冲时序的编码在多大程度上可能是人类听觉对象识别鲁棒性的基础，目前还没有得到充分的研究。本项目通过在皮层水平设计一个受生物学启发的听觉处理计算模型，并提取对该模型实现鲁棒听觉对象识别至关重要的计算原理，弥合了这一差距。该方法是将声音转换为由特征检测的丘脑听觉神经元产生的脉冲序列，并利用具有活跃树突的皮层神经元的状态依赖动力学将这些脉冲在空间和时间上整合。在提出的模型中，一个听觉对象首先唤起丘脑神经元的连续尖峰，这些神经元已经被训练来检测有用的特征。然后，通过来自丘脑的前馈兴奋和抑制，以及来自皮层神经元的侧向兴奋和抑制，皮层网络的状态进化，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。该计算模型受皮层神经元特性、皮层网络组织原理、网络结构活动依赖的可塑性规律等实验结果的约束。该项目旨在设计能够通过时空尖峰序列鲁棒地表示听觉对象的特征检测器，并建立一个皮层网络模型，该模型可以通过丘脑输入驱动的状态转换来识别特定的听觉对象，并将神经元动态与听觉皮层中观察到的动态进行比较。计算模型的识别性能将通过听觉任务进行评估和改进，以比较不同的语音识别方法。

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.