Masaev Dinar N, Suleimanova Alina A, Prudnikov Nikita V, Serenko Mariia V, Emelyanov Andrey V, Demin Vyacheslav A, Lavrov Igor A, Talanov Max O, Erokhin Victor V
ITIS, IFMB, Kazan Federal University, Kazan, Russia.
B-Rain Labs LLC, Kazan, Russia.
Front Neurosci. 2023 Feb 28;17:1124950. doi: 10.3389/fnins.2023.1124950. eCollection 2023.
Existing methods of neurorehabilitation include invasive or non-invasive stimulators that are usually simple digital generators with manually set parameters like pulse width, period, burst duration, and frequency of stimulation series. An obvious lack of adaptation capability of stimulators, as well as poor biocompatibility and high power consumption of prosthetic devices, highlights the need for medical usage of neuromorphic systems including memristive devices. The latter are electrical devices providing a wide range of complex synaptic functionality within a single element. In this study, we propose the memristive schematic capable of self-learning according to bio-plausible spike-timing-dependant plasticity to organize the electrical activity of the walking pattern generated by the central pattern generator.
现有的神经康复方法包括侵入性或非侵入性刺激器，这些刺激器通常是简单的数字发生器，具有手动设置的参数，如脉冲宽度、周期、爆发持续时间和刺激序列频率。刺激器明显缺乏适应能力，以及假体装置的生物相容性差和功耗高，凸显了对包括忆阻器件在内的神经形态系统在医学应用方面的需求。后者是在单个元件内提供广泛复杂突触功能的电气设备。在本研究中，我们提出了一种忆阻原理图，该原理图能够根据生物合理的脉冲时间依赖可塑性进行自学习，以组织由中枢模式发生器产生的行走模式的电活动。
