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

在恶劣环境中识别语音或其他听觉物体--例如有噪音、混响和多个扬声器--对于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，但在恶劣条件下的性能比人类性能低几个数量级。来自神经科学的聚合证据表明，听觉信息编码在皮质下神经元稀疏和精确计时的棘波中。然而，基于棘波时序的编码在多大程度上可能支撑人类听觉物体识别的稳健性还没有得到充分的研究。该项目通过设计一个受生物启发的大脑皮层听觉处理的计算模型并提取该模型实现稳健的听觉对象识别所必需的计算原理来弥合这一差距。该方法是将声音转换为特征检测丘脑听觉神经元产生的尖峰序列，并利用皮层神经元与活跃的树突的状态依赖的动力学在空间和时间上整合这些尖峰。在提出的模型中，听觉对象首先唤起丘脑神经元的顺序放电，这些神经元已经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侧向兴奋和抑制，皮质网络的状态发生进化，导致时间整合。当皮质神经元达到特定的网络状态时，发出识别听觉对象的信号。计算模型受到大脑皮层神经元的性质、大脑皮层网络的组织原理和网络结构的活动相关塑性规则的实验结果的约束。该项目的目标既是设计能够稳健地表示具有时空棘波序列的听觉对象的特征检测器，又旨在建立一个皮质网络模型，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，其神经元动力学可以与在听觉皮质中观察到的神经动力学相比较。计算模型的识别性能将通过设计用于比较不同语音识别方法的听觉任务进行评估和改进。

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.