Neuronal population dynamics within and across cortical areas

项目摘要：皮质还必须跟踪和处理动态变化的环境 因为存储和组合不同的输入以产生复杂的行为。此外，神经回路 要做到这一点，必须能够适应不断变化的环境，例如在与注意力相关的任务中。荷电 通过这些任务，也许并不令人惊讶的是，皮质神经元群体的反应动力学 令人望而生畏的复杂。目前，我们对电路力学缺乏深入的了解 这是神经系统表现出的丰富动力学的基础。这一遗漏尤其严重，因为 越来越多的数据表明，神经元动力学及其变异性是背景- 在大脑的大片区域中相互依赖和共享。我们的建议旨在解决以下几个问题 当前网络模型面临的基本问题。即均衡的尖峰网络模型 激发和抑制目前不能产生真实的瞬时活动、稳定状态 单个模型中的活动性和神经可变性。为了解决这些缺点，我们将制定一项 网络模型参数优化的自动化方法。然后，我们将验证 优化方法和由此产生的网络模型通过比较 用猕猴视觉V4区和前额叶皮质记录的网络模型 辨别和工作记忆任务。要进行这种比较，是徒劳无益的 尝试将每个记录的神经元与网络模型中的一个神经元相对应。相反，一把钥匙 我们建议的创新之处在于，我们将比较 人口活动在网络模型中的真实数据。网络模型及其优化 我们建立的方法将被广泛分享给研究社区。如果成功，这项工作 这将大大加深对神经回路如何引起 瞬时活动、稳态活动和神经变异性，并为研究社区配备 在这个方向上进行进一步发现的工具。

Project Summary: The cortex must both track and process dynamically changing environments as well as store and combine diverse inputs to generate complex behavior. Further, the neuronal circuits that accomplish this must be malleable to changing contexts, such as during attention related tasks. Charged with these tasks it is perhaps unsurprising that the response dynamics of populations of cortical neurons is then dauntingly complex. Currently, we lack a deep understanding of the circuit mechanics that underlie the rich dynamics exhibited in the nervous system. This omission is particularly serious given the ever increasing breadth of data showing that neuronal dynamics, and its variability, is context- dependent and shared across large regions of the brain. Our proposal seeks to address several fundamental issues facing current network models. Namely, spiking network models with balanced excitation and inhibition are not currently capable of generating realistic transient activity, steady state activity, and neural variability within a single model. To address these shortcomings, we will develop an automated method for optimizing the parameters of network models. We will then validate the optimization method and resulting network models by comparing the population activity generated by the network models with that recorded in macaque visual area V4 and prefrontal cortex during discrimination and working memory tasks. To perform this comparison, it is a fruitless exercise to attempt to correspond each recorded neuron to a neuron in the network model. Instead, a key innovation of our proposal is that we will compare the low-dimensional representations of the population activity in the network model and the real data. The network models and optimization method that we build will be will be widely shared with the research community. If successful, the work proposed here will lead to a vastly deeper understanding of how neural circuits give rise to transient activity, steady-state activity, and neural variability, and equip the research community with the tools to make further discoveries in this direction.