Network and dendritic mechanisms of context-dependent cortical computation

包括精神分裂症、帕金森氏病和阿尔茨海默病在内的许多疾病正在细胞和分子水平上进行深入研究，并在它们如何改变突触传递和单个神经元功能方面取得了相当大的进展。然而，细胞水平的神经元功能如何转化为更大规模网络功能和认知的中断还不是很清楚。细胞功能和行为之间的一个关键环节是神经元树突中突触输入的整合，这可以为每个单独的神经元提供自己的计算机制，进而塑造神经元网络执行的计算。由于两个主要障碍，研究亚细胞树突状细胞功能如何对大脑功能和行为做出贡献，特别是通过参与更高水平认知的关联皮质，一直是具有挑战性的。首先，在啮齿动物中，参与联合皮质的行为与头部固定不相容，限制了我们在复杂行为中测量树突信号的能力。其次，从自然行为中解释数据在计算和概念上都很困难：感觉编码可以通过平均数百次重复试验来理解，但以这种方式研究决策会让动物记住简单而稳定的刺激-反应关联，这不需要以同样的方式关联大脑皮层。这项建议将使用新的方法来克服这些问题，以检查树状和网络计算在复杂的关联行为中的贡献。这个项目利用了一种在自由行为中进行细胞水平成像的新方法，以及一种需要动物进行联想计算但很容易训练的任务。应聘者拥有系统神经科学、电生理学、钙成像和工程学方面的专业知识，为该项目提供了坚实的基础。K99将提供树突生理学方面的培训，以及将复杂任务与单个神经元和神经元网络的活动联系起来所需的计算方法，尽管行为存在差异。主要导师Mark Harnett博士将提供树突状细胞功能和研究亚细胞计算方法的指导。共同导师Ila Fiete博士将提供与该任务相关的计算方法方面的培训。Mehrda Jazayeri博士和Cengiz Pehlevan博士关于人口动态建模的其他建议将确保所有相关科学方法的高水平培训。这一培训将使应聘者能够完成这些目标，并开发一项独立的研究计划，专注于复杂自然行为背后的神经计算。这项研究的结果将为细胞计算如何影响复杂的行为提供新的见解，这将对理解具有非琐碎行为表型的疾病非常重要，这些疾病在典型的啮齿动物行为中不容易研究，如痴呆症和精神分裂症。

Many disorders including schizophrenia, Parkinson's disease, and Alzheimer's disease are being studied in-depth on the cellular and molecular level, and considerable progress has been made on how they change synaptic transmission and single neuron function. However, how cellular-level neuronal function translates to disruptions in larger-scale network function and cognition is not well understood. A key link between cellular function and behavior is the integration of synaptic inputs in the dendrites of neurons, which can give each individual neuron its own computational mechanisms that in turn shape the computations performed by networks of neurons. Efforts to study how sub-cellular dendritic function contributes to brain function and behavior, especially via the associative cortices involved in higher-level cognition, have been challenging, due to two major barriers. First, in rodents, the behaviors that engage associative cortices are incompatible with head fixation, limiting our ability to measure dendritic signals during complex behavior. Second, interpreting data from natural behaviors is computationally and conceptually hard: sensory encoding can be understood by averaging hundreds of trial repetitions, but studying decision-making in this manner makes animals memorize simple and stable stimulus-response associations, which do not require associative cortices in the same manner. This proposal will use new approaches that overcome these issues to examine the contributions of dendritic and network computation in a complex associative behavior. This project exploits a novel approach for cellular-level imaging in free behavior, and a task that requires animals to perform associative computations but is easy to train. The candidate has expertise in systems neuroscience, electrophysiology, calcium imaging, and engineering, providing a strong foundation for this program. The K99 will provide training in dendritic physiology, and the computational methods required for linking complex tasks to the activity of individual neurons and networks of neurons despite variability in behavior. The primary mentor Dr. Mark Harnett will provide mentorship in dendritic function and methods for studying subcellular computation. The co- mentor Dr. Ila Fiete will provide training in computational methods relevant to the task. Additional advice from Dr. Mehrdad Jazayeri on modeling population dynamics and Dr. Cengiz Pehlevan on the mathematical properties of the underlying computations will ensure high-level training in all relevant scientific approaches. This training will enable the candidate to complete these aims and to develop an independent research program focused on neural computations underlying complex natural behaviors. The results of this research will provide new insight into how cellular computations contribute to complex behaviors, which will be important for understanding disorders with non-trivial behavioral phenotypes that are not easily studied in classical rodent behavior, such as dementia and schizophrenia.