Inferring computational dynamics from neural measurements using deep recurrent neural networks

使用深度循环神经网络从神经测量中推断计算动力学

• 批准号: 406070939
• 负责人: Professor Dr. Daniel Durstewitz
• 金额: --
• 依托单位: Klinik für Psychiatrie und Psychotherapie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/406070939?language=en
• 关键词: Inferring; computational; dynamics; neural; measurements

In theoretical neuroscience, computational processes in the brain are often thought to be implemented in terms of the underlying stochastic neural system dynamics. Cognitive processes like working memory, decision making, interval timing, or thought sequences, have been described in terms of attractor states, probabilistic transitions between these, slow transients, or chains of saddle nodes, for instance. Consequently, from this point of view, for understanding the neural basis of cognition one should unravel the neural system dynamics that underlies behavioral performance and neural activity. However, neural dynamics are not directly observable but have to be inferred from a limited and noisy set of physiological measurements that usually probe only a few of the system’s degrees of freedom. It would therefore be of great value if one had methodological tools for (automatically) recovering the underlying neural dynamics from such ‘sparse’ physiological measurements. The central goal of the present proposal is to develop and validate such methods based on deep recurrent neural networks (RNN), and probe them on neurophysiological data.We have previously used a combination of delay embeddings and nonlinear basis expansions to extract from multiple single-unit (MSU) recordings essential dynamical properties and aspects of the flow, like convergence to putative semi-attracting states. More recently, we have developed a framework for statistical estimation of RNN models from experimental data. RNNs are computationally and dynamically universal in the sense that they can emulate and approximate any dynamical system, thus, in theory, are powerful enough to represent whatever neural dynamics and computational processes underlie the observed neural activity and behavior.Based on this previous and preliminary work, here we will tackle a number of important open issues: 1) Existing methods for statistical inference of (deep) RNN models do not scale very well with system size, yet this is very important for achieving good approximations to the dynamics and dealing with larger physiological data sets. Here we suggest several lines of methodological improvement. 2) More importantly, a systematic validation and comparison of various model architectures and training/inference algorithms on ground truth systems of differing biophysical complexity is still lacking, especially for experimentally realistic scenarios with comparatively sparse data from high-dimensional systems, which were only partially observed, and with high levels of both system-intrinsic and measurement noise. 3) As a case study for the usefulness of such methods, we will re-analyze MSU recordings from rat prefrontal cortex and hippocampus obtained during two different working memory tasks, to address specific issues about the (coupled) dynamics of these areas that were beyond the realm of previous analysis tools.

在理论上的神经科学中，通常认为大脑中的计算过程是根据潜在的随机神经元系统动力学来实现的。例如，已经用吸引子状态，这些概率过渡，缓慢的过渡或马鞍节点的链条来描述认知过程，诸如工作记忆，决策时间，间隔时间或思想序列。因此，从这个角度来看，为了理解认知的神经元基础，应阐明行为性能和神经元活性的基础的神经元系统动力学。但是，神经动力学不是直接观察到的，而必须从有限的噪音集合中推断出，通常只探测系统的自由度的少数。因此，如果一个方法学工具（自动）从这种“稀疏”物理测量中恢复了基本的神经元动力学，那将具有很大的价值。 The central goal of the present proposal is to develop and validate such methods based on deep recurrent neuronal networks (RNN), and probe them on neurophysiological data.We have previously used a combination of delay embeddings and nonlinear basis expansions to extract from multiple single-unit (MSU) recordings essential dynamic properties and aspects of the flow, like convergence to putative semi-attracting states.最近，我们开发了一个从实验数据中对RNN模型进行统计估算的框架。 RNN在计算和动态上是普遍的，因为它们可以仿真并近似任何动态系统，因此，从理论上讲，它足以代表任何神经动态和计算过程，这是观察到的神经活动和行为的基础。基于此之前的这项和初步的工作，基于这一和初步的工作。实现良好的近似动力学并处理较大的物理数据集。在这里，我们建议几条方法学改进。 2）更重要的是，仍然缺乏对各种模型体系结构的系统验证和比较，以及在地面真实系统上具有区分生物物理复杂性的地面真实系统的训练/推理算法，尤其是对于实验性现实的情景，来自高维系统的相对稀疏的数据，仅观察到高维系统的相对稀疏的数据，这些数据仅是部分观察到的，并且具有高水平的系统intertrinsic and Insertrinsic and Inertrinsic sistinsic和sermesurement siseur nose。 3）作为此类方法有用性的案例研究，我们将重新分析来自在两个不同的工作记忆任务中获得的大鼠前额叶皮层和海马的MSU记录，以解决有关这些领域（耦合）动态的特定问题，这些问题超出了先前分析工具的领域。