Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications

An artificial visual neuron with multiplexed rate and time-to-first-spike coding

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware.

Neuromorphic one-shot learning utilizing a phase-transition material

Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO2), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO2 devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to fourfold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning in neuromorphic hardware.

A modular organic neuromorphic spiking circuit for retina-inspired sensory coding and neurotransmitter-mediated neural pathways

Signal communication mechanisms within the human body rely on the transmission and modulation of action potentials. Replicating the interdependent functions of receptors, neurons and synapses with organic artificial neurons and biohybrid synapses is an essential first step towards merging neuromorphic circuits and biological systems, crucial for computing at the biological interface. However, most organic neuromorphic systems are based on simple circuits which exhibit limited adaptability to both external and internal biological cues, and are restricted to emulate only specific the functions of an individual neuron/synapse. Here, we present a modular neuromorphic system which combines organic spiking neurons and biohybrid synapses to replicate a neural pathway. The spiking neuron mimics the sensory coding function of afferent neurons from light stimuli, while the neuromodulatory activity of interneurons is emulated by neurotransmitters-mediated biohybrid synapses. Combining these functions, we create a modular connection between multiple neurons to establish a pre-processing retinal pathway primitive.

Topological Quantum Switching Enabled Neuroelectronic Synaptic Modulators for Brain Computer Interface

Aging and genetic-related disorders in the human brain lead to impairment of daily cognitive functions. Due to their neural synaptic complexity and the current limits of knowledge, reversing these disorders remains a substantial challenge for brain-computer interfaces (BCI). In this work, a solution is provided to potentially override aging and neurological disorder-related cognitive function loss in the human brain through the application of the authors' quantum synaptic device. To illustrate this point, a quantum topological insulator (QTI) Bi2Se2Te-based synaptic neuroelectronic device, where the electric field-induced tunable topological surface edge states and quantum switching properties make them a premier option for establishing artificial synaptic neuromodulation approaches, is designed and developed. Leveraging these unique quantum synaptic properties, the developed synaptic device provides the capability to neuromodulate distorted neural signals, leading to the reversal of age-related disorders via BCI. With the synaptic neuroelectronic characteristics of this device, excellent efficacy in treating cognitive neural dysfunctions through modulated neuromorphic stimuli is demonstrated. As a proof of concept, real-time neuromodulation of electroencephalogram (EEG) deduced distorted event-related potentials (ERP) is demonstrated by modulation of the synaptic device array.

Memristor-Based Artificial Chips

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

Filament-free memristors for computing

Memristors have attracted increasing attention due to their tremendous potential to accelerate data-centric computing systems. The dynamic reconfiguration of memristive devices in response to external electrical stimuli can provide highly desirable novel functionalities for computing applications when compared with conventional complementary-metal-oxide-semiconductor (CMOS)-based devices. Those most intensively studied and extensively reviewed memristors in the literature so far have been filamentary type memristors, which typically exhibit a relatively large variability from device to device and from switching cycle to cycle. On the other hand, filament-free switching memristors have shown a better uniformity and attractive dynamical properties, which can enable a variety of new computing paradigms but have rarely been reviewed. In this article, a wide range of filament-free switching memristors and their corresponding computing applications are reviewed. Various junction structures, switching properties, and switching principles of filament-free memristors are surveyed and discussed. Furthermore, we introduce recent advances in different computing schemes and their demonstrations based on non-filamentary memristors. This Review aims to present valuable insights and guidelines regarding the key computational primitives and implementations enabled by these filament-free switching memristors.

Single Wavelength Operating Neuromorphic Device Based on a Graphene-Ferroelectric Transistor

As global data generation continues to rise, there is an increasing demand for revolutionary in-memory computing methodologies and efficient machine learning solutions. Despite recent progress in electrical and electro-optical simulations of machine learning devices, the all-optical nonthermal function remains challenging, with single wavelength operation still elusive. Here we report on an optical and monochromatic way of neuromorphic signal processing for brain-inspired functions, eliminating the need for electrical pulses. Multilevel synaptic potentiation-depression cycles are successfully achieved optically by leveraging photovoltaic charge generation and polarization within the photoferroelectric substrate interfaced with the graphene sensor. Furthermore, the demonstrated low-power prototype device is able to reproduce exact signal profile of brain tissues yet with more than 2 orders of magnitude faster response. The reported properties should trigger all-optical and low power artificial neuromorphic development based on photoferroelectric structures.

Porous crystalline materials for memories and neuromorphic computing systems

Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems.

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.