Collective Dynamics and Computational Properties of Small Neural Networks

神经网络研究的一个主要挑战是建立模型 简单到足以分析，但描述了 网络的行为，并可以通过实验进行测试。 最近 小型神经网络的实验和理论研究 使这种模式的建立成为可能。 拟议 我们将建立一个详细的幽门生物模型， 中央模式发生器的龙虾。 该模型将是一个 Hodgkin-Huxley模型的扩展版本，其中包括 重要的电流和突触连接。 使用来自 动力系统理论和物理近似，我们将获得 一个更简单的模型，它仍然描述了实验结果。 将根据完整模型开发阶段模型。 的影响 突触修饰和内在特性的变化 神经元将被测试，神经递质的作用 将被调查。 理论研究将联合收割机 神经网络的分析和数值方法， 动力系统理论，数值模拟，并将 与神经生物学实验相比。 这项研究将 有助于弥合常规神经网络之间的差距 模型和生物现实。

A major challenge in neural network research is to build models that are simple enough for analysis but describe the biological behavior of the network and can be tested experimentally. Recent experimental and theoretical studies of small neural networks make the building of such models possible. In the proposed research we will build a detailed biological model of the pyloric Central Pattern Generator of the lobster. This model will be an extended version of the Hodgkin-Huxley model which includes the important currents and synaptic connections. Using methods from dynamical system theory and physical approximation we will obtain a simpler model which still describes the experimental results. A phase model will be developed from the full model. The effects of synaptic modification and changes of intrinsic properties of the neurons will be tested, and the effect of neurotransmitters will be investigated. The theoretical study will combine analytical and numerical methods from neural network and dynamical system theory, numerical simulations and will be compared to neurobiological experiments. The research will contribute to bridging the gap between regular neural network models and the biological reality.