922 research outputs found
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
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
Chaotic memristor
We suggest and experimentally demonstrate a chaotic memory resistor
(memristor). The core of our approach is to use a resistive system whose
equations of motion for its internal state variables are similar to those
describing a particle in a multi-well potential. Using a memristor emulator,
the chaotic memristor is realized and its chaotic properties are measured. A
Poincar\'{e} plot showing chaos is presented for a simple nonautonomous circuit
involving only a voltage source directly connected in series to a memristor and
a standard resistor. We also explore theoretically some details of this system,
plotting the attractor and calculating Lyapunov exponents. The multi-well
potential used resembles that of many nanoscale memristive devices, suggesting
the possibility of chaotic dynamics in other existing memristive systems.Comment: Applied Physics A (in press
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
A neuromorphic chip that combines CMOS analog spiking neurons and memristive
synapses offers a promising solution to brain-inspired computing, as it can
provide massive neural network parallelism and density. Previous hybrid analog
CMOS-memristor approaches required extensive CMOS circuitry for training, and
thus eliminated most of the density advantages gained by the adoption of
memristor synapses. Further, they used different waveforms for pre and
post-synaptic spikes that added undesirable circuit overhead. Here we describe
a hardware architecture that can feature a large number of memristor synapses
to learn real-world patterns. We present a versatile CMOS neuron that combines
integrate-and-fire behavior, drives passive memristors and implements
competitive learning in a compact circuit module, and enables in-situ
plasticity in the memristor synapses. We demonstrate handwritten-digits
recognition using the proposed architecture using transistor-level circuit
simulations. As the described neuromorphic architecture is homogeneous, it
realizes a fundamental building block for large-scale energy-efficient
brain-inspired silicon chips that could lead to next-generation cognitive
computing.Comment: This is a preprint of an article accepted for publication in IEEE
Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no.
2, June 201
Experimental study of artificial neural networks using a digital memristor simulator
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a fully digital implementation of a memristor hardware simulator, as the core of an emulator, based on a behavioral model of voltage-controlled threshold-type bipolar memristors. Compared to other analog solutions, the proposed digital design is compact, easily reconfigurable, demonstrates very good matching with the mathematical model on which it is based, and complies with all the required features for memristor emulators. We validated its functionality using Altera Quartus II and ModelSim tools targeting low-cost yet powerful field programmable gate array (FPGA) families. We tested its suitability for complex memristive circuits as well as its synapse functioning in artificial neural networks (ANNs), implementing examples of associative memory and unsupervised learning of spatio-temporal correlations in parallel input streams using a simplified STDP. We provide the full circuit schematics of all our digital circuit designs and comment on the required hardware resources and their scaling trends, thus presenting a design framework for applications based on our hardware simulator.Peer ReviewedPostprint (author's final draft
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