Neuromorphic Signal Processing Research Articles

Bio‐inspired Two‐dimensional Nanofluidic Ionic Transistor for Neuromorphic Signal Processing

AbstractVoltage‐gated ion channels prevalent in neurons play important roles in generating action potential and information transmission by responding to transmembrane potential. Fabricating bio‐inspired ionic transistors with ions as charge carriers will be crucial for realizing neuro‐inspired devices and brain‐liking computing. Here, we reported a two‐dimensional nanofluidic ionic transistor based on a MXene membrane with sub‐1 nm interlayer channels. By applying a gating voltage on the MXene nanofluidic, a transmembrane potential will be generated to active the ionic transistor, which is similar to the transmembrane potential of neuron cells and can be effectively regulated by changing membrane parameters, e.g., thickness, composition, and interlayer spacing. For the symmetric MXene nanofluidic, a high on/off ratio of ~2000 can be achieved by forming an ionic depletion or accumulation zone, contingent on the sign of the gating potential. An asymmetric PET/MXene‐composited nanofluidic transitioned the ionic transistor from ambipolar to unipolar, resulting in a more sensitive gate voltage characteristic with a low subthreshold swing of 560 mV/decade. Furthermore, ionic logic gate circuits, including the “NOT”, “NAND”, and “NOR” gate, were implemented for neuromorphic signal processing successfully, which provides a promising pathway towards highly parallel, low energy consumption, and ion‐based brain‐like computing.

Bio-inspired Two-dimensional Nanofluidic Ionic Transistor for Neuromorphic Signal Processing.

Voltage-gated ion channels prevalent in neurons play important roles in generating action potential and information transmission by responding to transmembrane potential. Fabricating bio-inspired ionic transistors with ions as charge carriers will be crucial for realizing neuro-inspired devices and brain-liking computing. Here, we reported a two-dimensional nanofluidic ionic transistor based on a MXene membrane with sub-1 nm interlayer channels. By applying a gating voltage on the MXene nanofluidic, a transmembrane potential will be generated to active the ionic transistor, which is similar to the transmembrane potential of neuron cells and can be effectively regulated by changing membrane parameters, e.g., thickness, composition, and interlayer spacing. For the symmetric MXene nanofluidic, a high on/off ratio of ~2000 can be achieved by forming an ionic depletion or accumulation zone, contingent on the sign of the gating potential. An asymmetric PET/MXene-composited nanofluidic transitioned the ionic transistor from ambipolar to unipolar, resulting in a more sensitive gate voltage characteristic with a low subthreshold swing of 560 mV/decade. Furthermore, ionic logic gate circuits, including the "NOT", "NAND", and "NOR" gate, were implemented for neuromorphic signal processing successfully, which provides a promising pathway towards highly parallel, low energy consumption, and ion-based brain-like computing.

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.

Efficient Neuromorphic Signal Processing with Resonator Neurons

Efficient Neuromorphic Signal Processing with Resonator Neurons

A Native SPICE Implementation of Memristor Models for Simulation of Neuromorphic Analog Signal Processing Circuits

Since the memristor emerged as a programmable analog storage device, it has stimulated research on the design of analog/mixed-signal circuits with the memristor as the enabler of in-memory computation. Due to the difficulty in evaluating the circuit-level nonidealities of both memristors and CMOS devices, SPICE-accuracy simulation tools are necessary for perfecting the art of neuromorphic analog/mixed-signal circuit design. This article is dedicated to a native SPICE implementation of the memristor device models published in the open literature and develops case studies of applying such a circuit simulation with MOSFET models to study how device-level imperfections can make adversarial effects on the analog circuits that implement neuromorphic analog signal processing. Methods on memristor stamping in the framework of modified nodal analysis formulation are presented, and implementation results are reported. Furthermore, functional simulations on neuromorphic signal processing circuits including memristors and CMOS devices are carried out to validate the effectiveness of the native SPICE implementation of memristor models from the perspectives of simulation accuracy, efficiency, and convergence for large-scale simulation tasks.

Multi-channel optical neuromorphic processor for frequency-multiplexed signals

Here we develop the first optical neuromorphic processor for frequency-multiplexed and multi-channel signals and demonstrate its application for orthogonal frequency-division multiplexing and wavelength-division multiplexing. The presented architecture supports multichannel operation through incorporation of the optical Fourier transform and dispersion-managed fiber echo state network analogue enabling processing of dual-quadrature high bandwidth signals. We demonstrate applicability of the design for prediction and equalization tasks. The proposed technology paves the way to multi-channel neuromorphic signal processing.

Artificial tactile peripheral nervous system supported by self-powered transducers

The tactile peripheral nervous system innervating human hands, which is essential for sensitive haptic exploration and dexterous object manipulation, features overlapped receptive fields in the skin, arborization of peripheral neurons and many-to-many synaptic connections. Inspired by the structural features of the natural system, we report a supersensitive artificial slowly adapting tactile afferent nervous system based on the triboelectric nanogenerator technology. Using tribotronic transistors in the design of mechanoreceptors, the artificial afferent nervous system exhibits the typical adapting behaviours of the biological counterpart in response to mechanical stimulations. The artificial afferent nervous system is self-powered in the transduction and event-driven in the operation. Moreover, it has inherent proficiency of neuromorphic signal processing, delivering a minimum resolvable dimension two times smaller than the inter-receptor distance which is the lower limit of the dimension that existing electronic skins can resolve. These results open up a route to scalable neuromorphic skins aiming at the level of human’s exceptional perception for neurorobotic and neuroprosthetic applications.

Dispersion-Managed Fiber Echo State Network Analogue With High (Including THz) Bandwidth

We propose a design for high (including THz) bandwidth neuromorphic signal processing based on fiber echo state network analogue and demonstrate through numerical modeling its efficiency for distortion mitigation in optical communications with high-order (including 1024-QAM) formats. This is achieved via all-optical implementation of masking and neural functionality by utilizing dispersion and nonlinear properties of the fiber. The design is flexible and format-transparent making it relevant for future communication systems. The model is simple to implement, as it requires only two attenuators and pumps in addition to a traditional nonlinear optical loop mirror (NOLM), and offers a vast range of optimization parameters.

Neuromorphic Functions of Light in Parity-Time-Symmetric Systems.

As an elementary processor of neural networks, a neuron performs exotic dynamic functions, such as bifurcation, repetitive firing, and oscillation quenching. To achieve ultrafast neuromorphic signal processing, the realization of photonic equivalents to neuronal dynamic functions has attracted considerable attention. However, despite the nonconservative nature of neurons due to energy exchange between intra‐ and extra‐cellular regions through ion channels, the critical role of non‐Hermitian physics in the photonic analogy of a neuron has been neglected. Here, a neuromorphic non‐Hermitian photonic system ruled by parity‐time symmetry is presented. For a photonic platform that induces the competition between saturable gain and loss channels, dynamical phases are classified with respect to parity‐time symmetry and stability. In each phase, unique oscillation quenching functions and nonreciprocal oscillations of light fields are revealed as photonic equivalents of neuronal dynamic functions. The proposed photonic system for neuronal functionalities will become a fundamental building block for light‐based neural signal processing.

Ultra-fast artificial neuron: generation of picosecond-duration spikes in a current-driven antiferromagnetic auto-oscillator

We demonstrate analytically and numerically, that a thin film of an antiferromagnetic (AFM) material, having biaxial magnetic anisotropy and being driven by an external spin-transfer torque signal, can be used for the generation of ultra-short “Dirac-delta-like” spikes. The duration of the generated spikes is several picoseconds for typical AFM materials and is determined by the inplane magnetic anisotropy and the effective damping of the AFM material. The generated output signal can consist of a single spike or a discrete group of spikes (“bursting”), which depends on the repetition (clock) rate, amplitude, and shape of the external control signal. The spike generation occurs only when the amplitude of the control signal exceeds a certain threshold, similar to the action of a biological neuron in response to an external stimulus. The “threshold” behavior of the proposed AFM spike generator makes possible its application not only in the traditional microwave signal processing but also in the future neuromorphic signal processing circuits working at clock frequencies of tens of gigahertz.

Asynchronous spiking photonic neuron for lightwave neuromorphic signal processing

We developed an asynchronous spiking photonic neuron that forms the basic building block for hybrid analog/digital lightwave neuromorphic processing. Our approach enables completely asynchronous spiking in response to input signals while maximizing the throughput relative to synchronous approaches. Asynchronous operation is achieved by generating the spike source for the photonic neuron through four-wave mixing. This hybrid analog/digital photonic neuron has an electro-absorption modulator as the temporal integration unit for analog processing, while the digital processing portion employs optical thresholding in a highly Ge-doped nonlinear loop mirror.

Signal feature recognition based on lightwave neuromorphic signal processing

We developed a hybrid analog/digital lightwave neuromorphic processing device that effectively performs signal feature recognition. The approach, which mimics the neurons in a crayfish responsible for the escape response mechanism, provides a fast and accurate reaction to its inputs. The analog processing portion of the device uses the integration characteristic of an electro-absorption modulator, while the digital processing portion employ optical thresholding in a highly Ge-doped nonlinear loop mirror. The device can be configured to respond to different sets of input patterns by simply varying the weights and delays of the inputs. We experimentally demonstrated the use of the proposed lightwave neuromorphic signal processing device for recognizing specific input patterns.

Lightwave Neuromorphic Signal Processing [In The Spotlight

This article discusses a technique that promises to deliver improved optical computing. Specifically, neuromorphic engineering that can inspire novel optical computing devices. Neuromorphic engineering aims to develop practical computing and signal processing devices based on an understanding of the biophysics of neuronal computation.

Analysis of the interaction between the retinal ON and OFF channels using CNN‐UM models

AbstractQuantitative retinal modeling is an important tool for analyzing the circuitry underlying the functional organization of the retina, and developing neuromorphic signal processing for retinal prostheses. The Cellular Neural Network (CNN) is a suitable tool for both purposes due to its structure. Using multilayer CNN models we analyzed two phenomena: First the signal distortion caused by nonlinear synapses and secondly the use of inhibitory interaction between ON and OFF channels. Our simulations show that the ON–OFF interaction can reduce the signal distortion caused by nonlinear synapses. Copyright © 2008 John Wiley & Sons, Ltd.