Electronic circuit implementation of the chaotic Rulkov neuron model
Electronic circuit implementation of the chaotic Rulkov neuron model
复制标题
混沌 Rulkov 神经元模型的电子电路实现
DOI：
10.1016/j.jfranklin.2013.01.026
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
M. Sanjuán
中科院分区：
文献类型：
--
作者：
Alexandre Wagemakers;M. Sanjuán
数值积分是计算神经科学中研究基于常微分方程组的生物神经元模型的最常用和最直接的方法。对于某些目的，由于计算机体系结构中的多个瓶颈，数值模拟是不够的。然而，当使用电子电路来实时模拟大型耦合神经元阵列时，模拟速度比计算机模拟快得多。我们提出了一种基于映射的神经元模型--混沌Rulkov神经元模型的电子实现，该模型可以很容易地在大规模集成电路上传输，从而为大规模神经元网络的模拟提供了一个框架。Rulkov模型是一种基于映射的神经元模型，具有令人惊讶的丰富特征，如周期性和混沌的尖峰和爆裂。离散时间动态允许根据特定应用的需要调整电路的时间刻度。由于这里描述的电路只使用18个MOS晶体管，它为在单个设备中构建大型神经元网络提供了新的视角。这对于分析大型耦合神经元网络以研究其在网络上的动力学及其同步特性是非常重要的。
Numerical integration is the most common and straightforward approach in computational neuroscience for the study of biological neuron models based on ordinary differential equations. For some purposes, numerical simulations are not enough due to the multiple bottlenecks in computer architectures. However, when electronic circuits are used to simulate in real time large arrays of coupled neurons, the simulations are much faster than the computer simulations. We present here an electronic implementation of a map-based neuron model, a chaotic Rulkov neuron model, that can be easily transferred on a large scale integration circuit and thus provide a framework for the simulation of large networks of neurons. The Rulkov model is a map-based neuron model that has a surprising abundance of features, such as periodic and chaotic spiking and bursting. The discrete time dynamics allows to tune the time scale of the circuit to the needs of the specific application. Since the circuit described here only uses 18 MOS transistors, it offers new perspectives for building large networks of neurons in a single device. This is very relevant for the analysis of large networks of coupled neurons in order to investigate its dynamics over the network and its synchronization properties.