CRCNS_:Neural Population Coding of Dynamic Natural Scenes

这个项目的目的是在我们对神经细胞群体如何处理和 代表视觉皮质内的信息。通过将开创性的记录技术与新的分析工具相结合 和理论框架，这项研究工作将提供第一次一瞥大量神经元如何相互作用 在处理动态自然场景的过程中，在大脑皮层内。硅多极将被用来记录 同时来自初级视皮层的LOO-I神经元群体。这些种群的活动将是 在响应精度、稀疏性、相关性和LFP一致性方面具有特征。为了弄清原因 大脑皮层刺激诱发反应的因素，联合活动和刺激将符合 试图捕捉大型神经元集合的刺激-反应关系的预测模型。最后，我们 我将尝试通过构建功能模型来解释这些关系，以实现理论上的动机 感知和认知的信息处理目标。该项目具有高度跨学科的性质， 结合神经物理学家、理论家和工程师的专业知识来回答超出 任何一门学科的范围。 相关性(请参阅说明)： 在这项工作中获得的数据和开发的模型将打开一个新的窗口，以了解皮质的操作 电路，提供了第一次看到大量神经元同时活动的反应 充满活力的自然风光。这些新的见解将为神经假体的发展铺平道路 设备(皮质植入物)和治疗视力障碍的新形式。

This project aims to achieve a fundamental advance in our understanding of how neural populations process and represent information within visual cortex. By combining pioneering recording technology with new analytical tools and theoretical frameworks, this research effort will provide the first glimpse at how large numbers of neurons interact within the cortex during the processing of dynamic natural scenes. Silicon polytrodes will be used to record simultaneously from populations of lOO-i- neurons in primary visual cortex. The activity of these populations will be characterized in terms of response precision, sparsity, correlation, and LFP coherence. In order to elucidate the causal factors that contribute to stimulus-evoked responses in the cortex, the joint activity and stimuli will be fit with predictive models that attempt to capture the stimulus-response relationships of large neuronal ensembles. Finally, we will attempt to account for these relationships by building functional models that achieve theoretically-motivated information processing objectives for perception and cognition. The project is highly interdisciplinary in nature, combining the expertise of neurophyslologists, theoreticians, and engineers to answer questions that are beyond the scope of any one discipline. RELEVANCE (See instructions): The data obtained and models developed in this work will open a new window into the operation of cortical circuits, providing a first glimpse of the simultaneous activity of large numbers of neurons responding to dynamic natural scenes. These new insights will pave the way for the development of neural prosthetic devices (cortical implants) and new forms of treatment for visual disorders.