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Digital realization of the proposed linear model of the Hodgkin-Huxley neuron

机译:霍奇金-赫克斯利神经元线性模型的数字实现

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It seems that computing systems that imitate the brain can be achieved by integrating of electronics and neuroscience. In recent years, neuromorphic systems have been developed by the fusion of electronics and neuroscience. Since neurons are the basis of neural systems, constructing the optimized digital neuron plays a critical role in neuromorphic applications. Furthermore, the dynamic of ionic channels, which are the causative agents of synaptic plasticity is the main characteristic of biological neurons. The Hodgkin-Huxley neuron model is a mathematical description of biological neuron that is widely used in neuroscience to explore the relation of action potential propagation and information transmission. This model consists of nonlinear differential equations, which approximates the electrical characteristics of excitable cells such as neurons and cardiac muscle. In this paper, a simplified version of the Hodgkin-Huxley neuron model was proposed by substituting its complex nonlinear dynamics with linear ones. Next, a digital circuit for the proposed linear model was designed, which can be implemented on a low-cost hardware platform, such as field-programmable gate array (FPGA). Comparison of MATLAB simulation of original model and Vivado simulation of the proposed digital circuit confirm that obtained results are in good agreement. The proposed digital circuit compared with the earlier circuits is more successful in terms of replicate essential characteristics of spiking responses and ionic currents in the biologically plausible model of neural activities. This new digital design will be applicable in developing neuro-inspired chips and exploring the brain's functionality in information processing.
机译:似乎可以通过集成电子学和神经科学来实现模仿大脑的计算系统。近年来,通过电子学和神经科学的融合发展了神经形态系统。由于神经元是神经系统的基础,因此构建优化的数字神经元在神经形态应用中起着至关重要的作用。此外,作为突触可塑性的致病因子的离子通道的动态是生物神经元的主要特征。霍奇金-赫克斯利(Hodgkin-Huxley)神经元模型是生物学神经元的数学描述,在神经科学中被广泛使用,以探讨动作电位传播与信息传递之间的关系。该模型由非线性微分方程组成,该方程近似于可兴奋细胞(如神经元和心肌)的电特性。在本文中,通过用线性动力学模型代替其复杂的非线性动力学,提出了霍奇金-赫克斯利神经元模型的简化版本。接下来,针对提出的线性模型设计了一种数字电路,该电路可以在低成本硬件平台上实现,例如现场可编程门阵列(FPGA)。将原始模型的MATLAB仿真与拟议的数字电路的Vivado仿真进行比较,可以证明所获得的结果吻合良好。在复制神经活动的生物学上合理的模型中的尖峰响应和离子电流的基本特征方面,与早期电路相比,拟议的数字电路更加成功。这种新的数字设计将适用于开发神经启发性芯片,并探索大脑在信息处理中的功能。

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