Modeling and implementation of biological neurons are key to the fundamental understanding of neural network architectures in the brain and its cognitive behavior. Synchronization of neuronal models play a significant role in neural signal processing as it is very difficult to identify the actual interaction between neurons in living brain. Therefore, the synchronization study of these neuronal architectures has received extensive attention from researchers. Higher biological accuracy of these neuronal units demands more computational overhead and requires more hardware resources for implementation. This paper presents a two coupled hardware implementation of Hindmarsh Rose neuron model which is mathematically simpler model and yet mimics several behaviors of a real biological neuron. These neurons are synchronized using an exponential function. The coupled system shows several behaviors depending upon the parameters of HR model and coupling function. An approximation of coupling function is also provided to reduce the hardware cost. Both simulations and a low cost hardware implementations of exponential synaptic coupling function and its approximation are carried out for comparison. Hardware implementation on field programmable gate array (FPGA) of approximated coupling function shows that the coupled network produces different dynamical behaviors with acceptable error. Hardware implementation shows that the approximated coupling function has significantly lower implementation cost. A spiking neural network based on HR neuron is also shown as a practical application of this coupled HR neural networks. The spiking network successfully encodes and decodes a time varying input.

中文翻译：

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Hindmarsh玫瑰神经元的同步。

生物神经元的建模和实现是对大脑中神经网络架构及其认知行为的基本理解的关键。神经元模型的同步在神经信号处理中起着重要作用，因为很难确定活体大脑中神经元之间的实际相互作用。因此，这些神经元结构的同步研究受到了研究人员的广泛关注。这些神经元单元的更高生物学准确性需要更多的计算开销，并且需要更多的硬件资源来实施。本文介绍了Hindmarsh Rose神经元模型的两个耦合的硬件实现，它在数学上是更简单的模型，但是可以模拟实际生物神经元的几种行为。这些神经元使用指数函数同步。耦合系统根据HR模型的参数和耦合函数显示出几种行为。还提供了耦合函数的近似值，以降低硬件成本。为了进行比较，对指数突触耦合函数及其近似进行了仿真和低成本硬件实现。近似耦合功能在现场可编程门阵列（FPGA）上的硬件实现表明，耦合网络产生具有可接受误差的不同动态行为。硬件实现表明，近似耦合函数的实现成本大大降低。基于HR神经元的尖峰神经网络也显示为这种耦合HR神经网络的实际应用。尖峰网络成功地对时变输入进行编码和解码。