Introduction to Neuromorphic Computing

Neuromorphic computing is an emerging field that has the potential to drastically influence every human’s life within the next decades. Neuromorphic computing explores the computing process of the brain and attempts to replicate it onto modern electronics. It offers improvements on current computer architecture, the von Neumann architecture, and will lead to more efficient computing, easier development of machine learning, and further integration of electronics and biology

Emulating short-term synaptic dynamics with memristive devices

Neuromorphic architectures offer great promise for achieving computation capacities beyond conventional Von Neumann machines. The essential elements for achieving this vision are highly scalable synaptic mimics that do not undermine biological fidelity. Here we demonstrate that single solid-state TiO2 memristors can exhibit non-associative plasticity phenomena observed in biological synapses, supported by their metastable memory state transition properties. We show that, contrary to conventional uses of solid-state memory, the existence of rate-limiting volatility is a key feature for capturing short-term synaptic dynamics. We also show how the temporal dynamics of our prototypes can be exploited to implement spatio-temporal computation, demonstrating the memristors full potential for building biophysically realistic neural processing systems

Capacitive effects and memristive switching in three terminal multilayered MoS<inf>2</inf>devices

We report on the electrical properties of gated two-terminal multilayered molybdenum disulfide (MoS2) memristor devices having a planar architecture. The approach based on highly dispersed MoS2 flakes drop cast onto a bottom gated Si/SiO2 (100nm) wafer containing metal Pd contact electrodes yields devices that exhibit a number of complex properties including memristive and capacitive effects as well as multiple non-zero-crossing current-voltage hysteresis effects. The devices also show a reaction to a varying gate bias. An increasingly positive gate led to the devices displaying a linear ohmic I-V response while an increasingly negative gate bias drove the system to behave more memristive with a widening hysteresis loop

A CMOS Spiking Neuron for Dense Memristor-Synapse Connectivity for Brain-Inspired Computing

Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing. Existing silicon neurons have molded neural biophysical dynamics but are incompatible with memristor synapses, or used extra training circuitry thus eliminating much of the density advantages gained by using memristors, or were energy inefficient. Here we describe a novel CMOS spiking leaky integrate-and-fire neuron circuit. Building on a reconfigurable architecture with a single opamp, the described neuron accommodates a large number of memristor synapses, and enables online spike timing dependent plasticity (STDP) learning with optimized power consumption. Simulation results of an 180nm CMOS design showed 97% power efficiency metric when realizing STDP learning in 10,000 memristor synapses with a nominal 1M{\Omega} memristance, and only 13{\mu}A current consumption when integrating input spikes. Therefore, the described CMOS neuron contributes a generalized building block for large-scale brain-inspired neuromorphic systems.Comment: This is a preprint of an article accepted for publication in International Joint Conference on Neural Networks (IJCNN) 201

A geographically distributed bio-hybrid neural network with memristive plasticity

Throughout evolution the brain has mastered the art of processing real-world inputs through networks of interlinked spiking neurons. Synapses have emerged as key elements that, owing to their plasticity, are merging neuron-to-neuron signalling with memory storage and computation. Electronics has made important steps in emulating neurons through neuromorphic circuits and synapses with nanoscale memristors, yet novel applications that interlink them in heterogeneous bio-inspired and bio-hybrid architectures are just beginning to materialise. The use of memristive technologies in brain-inspired architectures for computing or for sensing spiking activity of biological neurons8 are only recent examples, however interlinking brain and electronic neurons through plasticity-driven synaptic elements has remained so far in the realm of the imagination. Here, we demonstrate a bio-hybrid neural network (bNN) where memristors work as "synaptors" between rat neural circuits and VLSI neurons. The two fundamental synaptors, from artificial-to-biological (ABsyn) and from biological-to- artificial (BAsyn), are interconnected over the Internet. The bNN extends across Europe, collapsing spatial boundaries existing in natural brain networks and laying the foundations of a new geographically distributed and evolving architecture: the Internet of Neuro-electronics (IoN).Comment: 16 pages, 10 figure

Unidirectional synapse-like behavior of Zr/ZrO2-NT/Au layered structure

Zirconia nanotubular layer with an outer tube diameter 25 nm was synthesized by potentiostatic anodization. The Zr/ZrO2-NT/Au memristive structure is fabricated using stencil mask and magnetron sputtering techniques. Current-voltage characteristics are measured in full cycles of resistive switching with varying parameters of the applied harmonic voltage. An equivalent circuit with unidirectional electrical conductivity for the studied structure is proposed. Estimates of the electrical resistance of memristors in high-and intermediate resistivity states are performed. The high synaptic plasticity of memristors based on the Zr/ZrO2-NT/Au structure is shown. © 2018 IEEE