How does the architecture of neural systems determine information processing?

神经系统的架构如何决定信息处理？

• 批准号: 8687-2010
• 负责人: Kroger, Helmut
• 金额: $ 2.48万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=450340
• 关键词: How; does; architecture; neural; systems

How does the architecture of neural systems determine information processing? The architecture is important to determine how neural systems process information. This holds at different length scales, e.g., at the synapse, the neuron, and the network of neurons. This project aims to reveal principles valid at different levels, by addressing the following topics: - Information processing in a synapse: Realistic modeling of receptor acti - vation in hippocampal excitatory synapses. Synaptic plasticity, long term potentiation and long term depression play an important role in learning and memory. Such adaptation of a synapse can reinforce or weaken the link between the pre- and post-synaptic neurons. We aim to consider a reconstructed real hippocampal synapse, and model synaptic transmission. We want to study the role of synaptic cleft size, number of neurotransmitters, density and location of receptors, neuro-transmitter uptake and the synapse-glia interaction. - Information processing in a neuron: Modeling spiking and back propagation of thalamo-cortical cell. The thalamus is the major gateway for the flow of information toward the crebral cortex and is a station where incoming signals can be blocked during sleep. The neurons in thalamus and in cortex display a rich variety of spiking patterns. We aim to model the generation of the action potential and its back propagation into the dendritic tree of a thalamo-cortical neuron. We want to study the factors influencing the spiking behaviour: the shape of the dendritic arbor, the distribution of synapses, the distribution of Calcium ion channels. - Information processing in a neural network: Binocular rivalry in visual perception. Perception plays a cardinal role in everything we do. Recent experiments allow to analyze the neural network dynamics underlying perception on mesoscopic spatio-temporal scales. This scale is involved in the development of smart brain-computer interfacing. We aim to model percept switches in binocular rivalry, study the location of onset of percept switches (nucleation), the growth of the nucleation zone, and its geometrical shape. By comparison with experiments we hope to learn about the connectivity structure of the neuron populations and its biophysical parameters.

神经系统的结构如何决定信息处理？该架构对于确定神经系统如何处理信息非常重要。这适用于不同的长度尺度，例如，在突触，神经元，和神经元网络上。该项目旨在通过解决以下主题来揭示在不同水平有效的原理：-突触中的信息处理：海马兴奋性突触中受体激活的现实建模。突触可塑性、长时程增强和长时程抑制在学习记忆中起重要作用。突触的这种适应可以加强或削弱突触前和突触后神经元之间的联系。我们的目标是考虑一个重建的真实的海马突触，和模型突触传递。我们希望研究突触间隙的大小、神经递质的数量、受体的密度和位置、神经递质的摄取以及突触-胶质细胞相互作用的作用。- 神经元中的信息处理：丘脑-皮层细胞的尖峰脉冲和反向传播建模。丘脑是信息流向大脑皮层的主要通道，也是睡眠期间传入信号被阻断的地方。丘脑和皮层神经元呈现出丰富多样的放电模式。我们的目标是模拟动作电位的产生及其反向传播到丘脑-皮层神经元的树突树。我们想研究影响放电行为的因素：树突的形状，突触的分布，钙离子通道的分布。- 神经网络中的信息处理：视觉感知中的双眼竞争。感知在我们所做的每件事中起着至关重要的作用。最近的实验允许分析神经网络动态的中观时空尺度上的感知。这种规模涉及智能脑机接口的发展。我们的目标是在双目竞争中模拟神经感受器开关，研究神经感受器开关（成核）的起始位置，成核区的生长及其几何形状。通过与实验的比较，我们希望了解神经元群体的连接结构及其生物物理参数。