Memristive circuit-based model of central pattern generator to reproduce spinal neuronal activity in walking pattern

Abstract

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.

Principles of analog neuromorphic computing: from components to systems and algorithms

“…Обсуждаются также подходы к реализации локальных правил дофаминоподобного обучения с подкреплением в СНС, которые необходимы для формирования аналога системы «потребностей» интеллектуального агента в процессе его автономного функционирования [12][13][14]. Рассмотрены первые результаты по созданию прототипа мемристивного имплантируемого устройства, нейропротезирующего двигательную активность животного [15,16]. Наконец, демонстрируются возможные аппаратные решения как для нейрональных элементов, так и для синаптических связей на базе перспективных мемристивных устройств, подходящих для указанных типов локального обучения, представлены концепция и первые результаты по созданию аналогового нейроморфного процессора на базе вышеуказанных компонент.…”

A reservoir computing system with volatile and non-volatile organic memristors as a promising hardware architecture

“…Для реализации этого слоя были выбраны волатильные мемристоры на основе полианилина (ПАНИ). Они могут работать с биологически правдоподобными временными диапазонами, что важно, поскольку мы стремимся имитировать биологические системы [4]. Напротив, считывающий слой должен состоять из мемристоров с долговременной памятью, т.е.…”

“…The reservoir layer ought to comprise memristors with short-term memory, specifically, volatile memristors, to process each input sample independently. The implementation of polyanilinebased volatile memristors was chosen for this layer, operating within a biologically plausible time frame, reflecting our aim of mimicking biological systems [4]. Instead, the reservoir layer should be made up of memristors with long-term memory, specifically non-volatile memristors, as the readout layer needs to maintain the trained synaptic weights.…”

Modulation of polyaniline memristive device switching voltage by nucleotide-free analogue of vitamin B12

“…Among other applications, memristive devices are used in formal neuromorphic networks, such as fully connected perceptron [4], reinforcement learning networks [5], convolutional kernels [6], astrocytic networks [7], reservoir computing [8], and macros [9]. Another suitable application for such devices is complementation of biological systems for therapy and improvement of life quality, including neuromorphic vision [10,11], sensing systems [12], and neuroprosthetics [13].…”

“…Moreover, PANI is an electrochromic material with unique spectra in various redox states, enabling optical control for in-depth investigation of the switching process [32]. When it comes to integrating memristive devices into existing systems, PANI-based devices have proved to be compatible with widespread CMOS architecture [33], and provide switching times compatible with those of biological systems making it possible coupling with biological neurons [13,34]. In addition, when fabricated with solid state electrolyte layer, the devices demonstrate stability for at least 3•10 4 cycles, accompanied by reliable device-to-device and cycle-to-cycle variation [27,31].…”