Biologically Plausible Computational Models of Perirhinal Cortex

项目总结 人类和其他动物能够无缝地整合感觉和助记信息。作为一名医生 颞叶结构位于高水平感觉皮质的顶端，周围皮质(PRC)是理想的位置 支持知觉-助记整合。事实上，中国已被证明在不同的 行为，包括基于熟悉度的识别、视觉对象感知和恐惧条件反射。然而这件事 事实证明，职能的汇合很难正规化，有关机制也存在相当大的争议 使中国能够在这些不同的行为中发挥作用。通过整合传统的神经科学方法 在一个新的深度学习计算框架内，该提议旨在形式化和评估 PRC函数的计算理论。我有三个具体目标： 目标1.我的研究生工作已经为中国职能的形式化奠定了基础。通过整合病变， 在深度学习框架内的电生理和行为数据，这项工作解决了数十年的 关于PRC参与视觉物体知觉的实验结果似乎不一致。更多 具体地说，我发现灵长类腹侧视流(VVS)的一个生物学上可信的计算代理 直接与患有PRC的(人类和非人类)灵长类动物的视觉辨别行为相似 从实验刺激中。相反，PRC完好的参与者能够表现得比PRC受损的行为更好， 以及这些VVS的计算代理--这一发现表明PRC与这些行为有关。 目标2.在F99阶段提出的工作将建立在我以前工作的基础上，包括计算 对PRC依赖的视觉歧视行为的解释。首先，我将开发出生物学上可信的 PRC完好视觉行为的计算模型能够达到PRC完好人类的表现 受试者同时进行视觉辨别任务。然后，我将确定这些PRC-Model最适合- 活体(FMRI)测量PRC功能。这将在构建深度学习模型方面提供广泛的培训 视觉行为，以及神经成像专业知识，利用我研究生院现有的资源。 目标3.在K00阶段提出的工作将建立在这些感知模型的基础上，以包括PRC的已知 助记功能。我打算在记忆法的强化学习(RL)模型方面获得丰富的经验 行为。通过将深度学习和强化学习集成到生物学上可信的计算中 框架，我将构建一个依赖于PRC的知觉-助记行为的集成模型。 总的来说，这一建议为描述典型和非典型知觉助记符提供了一个新的框架 行为学，有望对感知和记忆的神经生物学有新的见解，同时概述了培训 需要成为计算认知神经科学前沿的独立研究人员。这是我的希望 这一框架可能为未来的临床应用提供基础，以解决记忆相关的障碍。

PROJECT SUMMARY Humans and other animals are able to seamlessly integrate sensory and mnemonic information. As a medial temporal lobe structure at the apex of high-level sensory cortices, perirhinal cortex (PRC) is ideally situated to support perceptual-mnemonic integration. Indeed, PRC has been shown to play a causal role in diverse behaviors, including familiarity-based recognition, visual object perception, and fear conditioning. Yet this confluence of functions has proven difficult to formalize, and there is considerable debate over the mechanisms that enable PRC to play a role in these diverse behaviors. By integrating traditional neuroscientific methods within a novel deep learning computational framework, this proposal aims to formalize and evaluate computational theories of PRC function. I have three specific aims: Aim 1. My graduate work has already laid the foundation to formalize PRC function. By integrating lesion, electrophysiological, and behavioral data within a deep learning framework, this work resolves decades of seemingly inconsistent experimental findings surrounding PRC involvement in visual object perception. More specifically, I find that a biologically plausible computational proxy for the primate ventral visual stream (VVS) approximates the visual discrimination behaviors of PRC-lesioned (human and non-human) primates, directly from experimental stimuli. Conversely, PRC-intact participants are able to outperform PRC-lesioned behaviors, as well as these computational proxies for the VVS—a finding that implicates PRC in these behaviors. Aim 2. The work proposed during the F99 phase will build upon my previous work to include a computational account of PRC-dependent visual discrimination behaviors. First, I will develop biologically plausible computational models of PRC-intact visual behaviors able to achieve the performance of PRC-intact human participants on concurrent visual discrimination tasks. Then, I will identify which of these PRC-models best fit in- vivo (fMRI) measurements of PRC function. This will provide extensive training in building deep learning models of visual behaviors, alongside neuroimaging expertise, harnessing resources available at my graduate institution. Aim 3. Work proposed in the K00 phase will build upon these perceptual models to include PRC's known mnemonic functions. I intend to gain extensive experience with reinforcement learning (RL) models of mnemonic behaviors. By integrating deep learning and reinforcement learning within a biologically plausible computational framework, I will build towards an integrated model of PRC-dependent perceptual-mnemonic behaviors. Collectively, this proposal offers a novel framework for characterizing typical and atypical perceptual-mnemonic behaviors, promising new insights into the neurobiology of perception and memory, while outlining the training I need to be an independent researcher at the forefront of computational cognitive neuroscience. It is my hope that this framework may provide a foundation for future clinical applications to address memory-related disorders.