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

在不利的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。 目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的数量级。来自神经科学的证据表明，听觉信息编码在皮层下神经元的稀疏和精确定时的尖峰中。然而，在何种程度上的代码的基础上尖峰时间可能是人类听觉对象识别的鲁棒性尚未得到充分的研究。本项目通过设计一个受生物学启发的皮层水平听觉处理计算模型，并提取模型实现鲁棒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，并使用具有活性树突的皮层神经元的状态依赖动力学在空间和时间上整合这些尖峰。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脉冲，这些神经元已经被训练用于检测有用的特征。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，皮层网络的状态演变，导致时间整合。当皮层神经元达到特定的网络状态时，听觉对象的识别被发出信号。该计算模型受到皮层神经元性质、皮层网络组织原理和网络结构活动相关可塑性规则的实验结果的约束。该项目旨在设计能够用时空尖峰序列鲁棒地表示听觉对象的特征检测器，并建立一个皮质网络模型，该模型可以使用由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被评估和改进的听觉任务，旨在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.