论文标题
生物物理神经网络的在线分布式估计
Distributed online estimation of biophysical neural networks
论文作者
论文摘要
在这项工作中，我们为一类受生物物理神经网络模型启发的非线性网络系统提出了一个分布式自适应观察者。神经系统通过以分布式方式调整内在和突触权重来学习，神经元膜电压带有网络中相邻神经元的信息。我们表明，该学习原理可用于根据分散的学习规则来设计自适应观察者，该规则大大减少了参数估计指数收敛所需的观察者状态的数量。这种新颖的设计与生物，生物医学和神经形态应用有关。
In this work, we propose a distributed adaptive observer for a class of nonlinear networked systems inspired by biophysical neural network models. Neural systems learn by adjusting intrinsic and synaptic weights in a distributed fashion, with neuronal membrane voltages carrying information from neighbouring neurons in the network. We show that this learning principle can be used to design an adaptive observer based on a decentralized learning rule that greatly reduces the number of observer states required for exponential convergence of parameter estimates. This novel design is relevant for biological, biomedical and neuromorphic applications.
