Models for accumulation of evidence through sequences in a navigation-based, decision-making task

决策是一个基本的认知过程，很多决策都是在逐步积累的基础上做出的 证据。因此，了解这种积累过程背后的机制基础是至关重要的。 传统的证据积累模型是基于低维吸引子的，其中单个神经元 在整个试验过程中显示斜坡活动。然而，越来越多的研究观察到 神经记录中的选择选择序列，在这些序列中，神经元以瞬时和顺序的方式放电 被激活的神经元的子集表明动物的选择。类似的序列已经在 其他记忆和决策任务，表明序列是神经动力学的基本形式 这与正则模型预测的持续动态本质上是不同的。为了弥补差距， 在经典模型和新兴数据之间，我将首先开发两个积累起来的新型神经电路模型 通过序列提供证据。第一个将是位置门控的凹凸吸引器，其中活动神经元的集合 (人口中活动的位置)编码沿一个轴的位置和沿 另一种是证据以非单调方式编码。与这种基于位置的模式不同，第二种模式将 由两个相互抑制的链组成，其中活动神经元的放电率(幅度)编码 证据，因此证据是单调编码的。因此，这两个新模型提出了两种替代方案 通过序列积累证据的机制，这些序列以其预测为特色 关于证据是如何编码的。单神经元编码和几何编码的模型预测 将通过与之前收集的广泛的一组代码进行比较来测试全人口代码的结构 在以导航为基础的证据积累任务中的神经活动，这表明了选择的选择性 整个大脑的序列，包括在海马体，视觉皮质，前扣带皮质，和 纹状体。因此，这个项目将解决我们理解决策过程中的两个关键问题，即如何 证据是通过序列积累的，当这些机制存在于大脑中时，通过 提出新的电路机制并分析整个大脑的神经数据。团契培训 PLAN使我有能力探索这些问题，因为我选择了一位实验赞助商，他的专业知识 存在于奖励学习和决策的系统神经科学和一个计算赞助商 神经集成器模型方面的专业知识。总部设在一家处于实验和 计算神经科学，我将有机会获得资源来扩展我的神经科学领域 在获得计算神经科学方面的知识的同时，还获得了专门用于建模神经整合和 神经编码。总体而言，该项目将开发两种新的证据积累范例，基于 关于顺序神经活动，这解释了不同大脑区域潜在机制的差异， 表明决策可能是如何在整个大脑中协调的。

Decision making is a fundamental cognitive process, and many decisions are based on gradually accumulated evidence. Thus, it is critical to understand the mechanistic basis underlying this accumulation process. Traditional models of evidence accumulation are based on low-dimensional attractors where individual neurons show ramping activity throughout a trial. However, an increasing number of studies have observed choice-selective sequences in their neural recordings, in which neurons fire transiently and sequentially with the subset of neurons that fires indicative of the animal’s choice. Similar sequences have been observed in other memory and decision-making tasks, suggesting sequences are a fundamental form of neural dynamics that are inherently different from the persistent dynamics predicted by canonical models. To address the gap between classic models and emerging data, I will first develop two novel neural circuit models that accumulate evidence through sequences. The first will be a position-gated, bump attractor, where the set of active neurons (“location” of activity in the population) encodes position along one axis and accumulated evidence along the other so that evidence is encoded non-monotonically. In contrast to this location-based model, the second will consist of two mutually inhibitory chains, where the firing rate (“amplitude”) of the active neurons encodes evidence, so that evidence is encoded monotonically. Thus, these two novel models propose two alternative mechanisms for the accumulation of evidence through sequences, which are distinguished by their predictions about how evidence is encoded. Model predictions for the encoding of single neurons and the geometric structure of the whole-population code will be tested by comparison to an extensive set of previously collected neural activity during a navigation-based, accumulation of evidence task, which demonstrate choice-selective sequences across the brain, including in the hippocampus, visual cortex, anterior cingulate cortex, and striatum. Thus, this project will address two key questions in our understanding of decision-making, how evidence is accumulated through sequences and where these mechanisms are present in the brain, by proposing novel circuit mechanisms and analyzing neural data throughout the brain. The fellowship training plan equips me to explore these questions because I have chosen one experimental sponsor whose expertise lies in the systems neuroscience of reward learning and decision making and one computational sponsor with expertise in neural integrator models. Based at an institution at the leading edge of both experimental and computational neuroscience, I will have access to the resources to expand my neuroscience domain knowledge while gaining skills in computational neuroscience specific to modeling neural integration and neural encoding. Overall, this project will develop two novel paradigms for the accumulation of evidence, based on sequential neural activity, that elucidate differences in the underlying mechanisms across brain regions, suggesting how decision making may be coordinated across the brain.