Uncovering biophysical and functional mechanisms of information processing in neural systems

揭示神经系统信息处理的生物物理和功能机制

• 批准号: RGPIN-2020-05868
• 负责人: Lankarany, Milad
• 金额: $ 2.19万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740882
• 关键词: Uncovering; biophysical; functional; mechanisms; information

The prodigious capacity of our brain to process information relies on efficient information-processing mechanisms. Understanding these mechanisms enables the development of bio-inspired intelligent systems and facilitates the development of implantable devices that can help people with neurological disorders to experience a healthy life again. Inferring these mechanisms from recorded brain signals is extremely challenging because the neural systems' activities are complex, nonlinear, and multi-scale. Furthermore, the recorded brain signals are noisy, sparse, and incomplete. We refer to these obstacles toward understanding the mechanisms of neuronal information processing as the lack of observability in neural systems. Relying solely on experimental recordings of the brain is not sufficient to overcome these obstacles - mathematical models and engineering methods are required. My research program is devoted to creating mathematical models and developing engineering algorithms that can link the models with experimental recordings. In this way, we aim to conquer this lack of observability in neural systems, understand neuronal information processing, and enhance the controllability of the brain's functions. Nearly all efforts to uncover neuronal information processing have focused on understanding how neurons respond to external stimuli. However, it is well-known from experimental studies that the communication between neurons is NOT static. Neurons communicate with each other through synapses (the structure that permits a neuron to transmit information to another neuron). The strength of connectivity between neurons varies with the ever-changing rate of activities in the pre- and post-synaptic neurons. The dynamic interactions between neurons are referred to as synaptic plasticity. Inferring mechanisms of neuronal information processing becomes more challenging if an additional set of parameters reflecting synaptic plasticity is to be taken into account. However, neurostimulation devices - by delivering electrical pulses to sets of neurons and affecting synaptic plasticity - presents a way to examine the modulated neural activities in a controlled fashion. My general approach is to benefit from neurostimulation to stimulate a neural system, create computational models to predict the underlying neural activities, and use experimental recordings provided by my collaborators to observe those activities. Moreover, I will develop engineering algorithms to infer the model parameters by minimizing the error between the observed and the predicted neural activities. My long-term goal is to uncover the mechanisms of information processing in neural systems by examining how neurostimulation impacts synaptic plasticity and modulates neuronal responses. This research program will provide a framework to gain a mechanistic understanding of neural systems' information processing, which in turn will advance our ability to control the brain's functions.

我们大脑处理信息的巨大能力依赖于有效的信息处理机制。了解这些机制可以促进仿生智能系统的开发，并促进可帮助神经系统疾病患者再次体验健康生活的植入式设备的开发。从记录的大脑信号推断这些机制极具挑战性，因为神经系统的活动是复杂的、非线性的和多尺度的。此外，记录的大脑信号嘈杂、稀疏且不完整。我们将理解神经元信息处理机制的这些障碍称为神经系统缺乏可观察性。仅仅依靠大脑的实验记录不足以克服这些障碍——需要数学模型和工程方法。我的研究项目致力于创建数学模型和开发可以将模型与实验记录联系起来的工程算法。通过这种方式，我们的目标是克服神经系统可观察性的缺乏，了解神经元信息处理，并增强大脑功能的可控性。几乎所有揭示神经元信息处理的努力都集中在理解神经元如何响应外部刺激。然而，从实验研究中众所周知，神经元之间的通信并不是静态的。神经元通过突触（允许一个神经元向另一个神经元传输信息的结构）相互通信。神经元之间的连接强度随着突触前和突触后神经元活动速率的不断变化而变化。神经元之间的动态相互作用被称为突触可塑性。 如果要考虑一组反映突触可塑性的附加参数，推断神经元信息处理的机制将变得更具挑战性。然而，神经刺激装置——通过向神经元组传递电脉冲并影响突触可塑性——提供了一种以受控方式检查调节神经活动的方法。 我的一般方法是受益于神经刺激来刺激神经系统，创建计算模型来预测潜在的神经活动，并使用我的合作者提供的实验记录来观察这些活动。此外，我将开发工程算法，通过最小化观察到的神经活动和预测的神经活动之间的误差来推断模型参数。我的长期目标是通过研究神经刺激如何影响突触可塑性和调节神经元反应来揭示神经系统中信息处理的机制。该研究计划将提供一个框架来获得对神经系统信息处理的机械理解，从而提高我们控制大脑功能的能力。