Neuromorphic memristive circuits to simulate inhibitory and excitatory dynamics of neuron networks: from physiological similarities to deep learning

点击翻译按钮获取中文摘要

Why does the human brain not operate as a computer? Both use logic and numbers when operating. Nevertheless, the human memory is much more distributed (pattern-like) in contrast to localised computer bit-memory, has time decay (clogging), changes "wiring" when trained and is designed to process information signals (excitation waves) rather than just store bits in different memory locations as a computer does. Although modern computers significantly numerically outperform the human brain, they still cannot handle tasks requiring guessing and fuzzy logic.This explains a booming interest in AI systems trained to perform certain tasks infeasible for numerical simulations. Currently AI technology is driven by two distinct goals: (i) technological demand (autonomous vehicles, online trading, AI for medical imaging analysis etc.) and (ii) an attempt to create an electronic brain with the ability to think, feel and interact with humans. Using different learning algorithms, AI has demonstrated abilities comparable to, or even outperforming, the human brain in several strategic and decision-making tasks (e.g., the game of Go). However, it is unclear how/if AI algorithms relate to information processing and the corresponding psycho-physiological processes in the brain. Answering this big question would help in not only making machines with human abilities but also elucidating whether a brain can be reduced to a biologically-wired electric circuit only or it has something beyond simple electric/chemical functionalities.To truly emulate information processing in the brain (neuromorphic computing), a new generation of computer architecture should be developed. One of the most promising technologies for neuromorphic computing and signal processing is based on memristors, where resistance is tuned (e.g., switching between two states) by total charge passed through the system (e.g., resistance depends on applied electric pulse sequence/history which can encode an information signal propagating through the brain). Using Loughborough's expertise in solid state physics, functional materials, thin films, modelling, and AI, in synergy with a world-leading centre of neuromorphic research (the University of Massachusetts, Amherst) and neuroscience/physiological expertise (Salk Institute for Biological studies), and driven by the demand of UK industrial partners, we intend to develop a prototype of a memristive neuromorphic chipset able to analyse image-streams and to make decisions and choices by mimicking neural process in a brain cortex.Inspired by biological neuron operation, and the deep learning AI paradigm, we propose to develop an electric circuit operating via two competing processes:(1) Intermixing or interfering electric signals generated by visual stimuli at different time moments (e.g., subsequent video frames) and;(2) Transmitting signals from one circuit layer to another in order to extract the main visual features/concepts.A combination of these processes for image-stream analysis has never been considered before for neuromorphic systems and is the main novelty of the proposed research. This allows us to compare image frames in a video and to reduce the complexity of the information towards a binary decision (choice). This neuromorphic two-process concept has a clear brain-functioning analogy: sensory stimuli excite a myriad of receptors generating superimposing signals, an end effect of which can be expressed by a short statement of recognition ("It's my Mom") or discrimination ("It was a car not a bike"). Cycles of interfering and convolving information followed by binary choice seems particularly well fit to memristor layered architecture where initial complex voltage-pattern encoding image stream reduces to switching or not of a certain memristor (signalling which decision/choice is made) in a deeper layer of the structure. The developed prototype will be the first memristive realisation of a visual cortex.