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？该架构对于确定神经系统如何处理信息非常重要。这适用于不同长度的尺度，例如，突触、神经元和神经元网络。这个项目旨在通过以下主题揭示在不同水平上有效的原理：-突触中的信息处理：海马区兴奋性突触中受体激活的真实模型。突触可塑性、长时程增强和长时程抑制在学习记忆中起着重要作用。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海马区突触，并建立突触传递的模型。我们想要研究突触裂隙的大小、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-神经胶质细胞的相互作用。-神经元中的信息处理：模拟丘脑皮质细胞的尖峰和反向传播。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挡的地方。丘脑和皮质中的神经元显示出丰富多样的尖峰模式。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质神经元的树突树。我们想要研究影响刺激性行为的因素：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：视觉感知中的双目竞争。感知在我们所做的每一件事中都起着至关重要的作用。最近的实验允许在介观时空尺度上分析潜在感知的神经网络动力学。这一规模涉及到智能脑机接口的发展。我们的目标是在双目竞争中模拟感知开关，研究感知开关的起始位置(成核)、成核区的生长及其几何形状。通过与实验的对比，我们希望了解神经元群体的连通性结构及其生物物理参数。

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.