Neuronal population dynamics within and across cortical areas

项目概要：大脑皮层必须同时跟踪和处理动态变化的环境 作为存储和联合收割机不同的输入产生复杂的行为。此外，神经回路， 要做到这一点，就必须能够适应不断变化的环境，比如在与注意力相关的任务中。带电 对于这些任务，皮质神经元群体的反应动力学可能并不令人惊讶， 复杂得令人生畏目前，我们缺乏对电路力学的深刻理解， 构成了神经系统中丰富的动力学的基础。这一疏忽尤其严重， 越来越多的数据表明，神经元动力学及其变异性是背景- 在大脑的大部分区域中相互依赖和共享。我们的建议旨在解决几个 当前网络模型面临的基本问题。也就是说，尖峰网络模型， 兴奋和抑制目前不能产生现实的瞬时活动，稳态 活动和神经变异性。为了解决这些问题，我们将开发一个 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，通过比较人口活动产生的 该网络模型与猕猴视觉区V4和前额叶皮层中记录的网络模型在 辨别和工作记忆任务。要进行这种比较， 尝试将每个记录的神经元对应于网络模型中的神经元。取而代之的是一把钥匙 我们建议的创新之处在于，我们将比较低维表示的 网络模型中的人口活动和真实的数据。网络模型与优化 我们建立的方法将与研究界广泛分享。如果成功，工作 这里提出的将导致更深入地了解神经回路如何引起 瞬时活动，稳态活动和神经变异性，并为研究界提供 在这个方向上做出进一步发现的工具。

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.