299 research outputs found
Connecting Spiking Neurons to a Spiking Memristor Network Changes the Memristor Dynamics
Memristors have been suggested as neuromorphic computing elements. Spike-time
dependent plasticity and the Hodgkin-Huxley model of the neuron have both been
modelled effectively by memristor theory. The d.c. response of the memristor is
a current spike. Based on these three facts we suggest that memristors are
well-placed to interface directly with neurons. In this paper we show that
connecting a spiking memristor network to spiking neuronal cells causes a
change in the memristor network dynamics by: removing the memristor spikes,
which we show is due to the effects of connection to aqueous medium; causing a
change in current decay rate consistent with a change in memristor state;
presenting more-linear dynamics; and increasing the memristor spiking
rate, as a consequence of interaction with the spiking neurons. This
demonstrates that neurons are capable of communicating directly with
memristors, without the need for computer translation.Comment: Conference paper, 4 page
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
Hardware design of LIF with Latency neuron model with memristive STDP synapses
In this paper, the hardware implementation of a neuromorphic system is
presented. This system is composed of a Leaky Integrate-and-Fire with Latency
(LIFL) neuron and a Spike-Timing Dependent Plasticity (STDP) synapse. LIFL
neuron model allows to encode more information than the common
Integrate-and-Fire models, typically considered for neuromorphic
implementations. In our system LIFL neuron is implemented using CMOS circuits
while memristor is used for the implementation of the STDP synapse. A
description of the entire circuit is provided. Finally, the capabilities of the
proposed architecture have been evaluated by simulating a motif composed of
three neurons and two synapses. The simulation results confirm the validity of
the proposed system and its suitability for the design of more complex spiking
neural network
Beyond Markov Chains, Towards Adaptive Memristor Network-based Music Generation
We undertook a study of the use of a memristor network for music generation,
making use of the memristor's memory to go beyond the Markov hypothesis. Seed
transition matrices are created and populated using memristor equations, and
which are shown to generate musical melodies and change in style over time as a
result of feedback into the transition matrix. The spiking properties of simple
memristor networks are demonstrated and discussed with reference to
applications of music making. The limitations of simulating composing memristor
networks in von Neumann hardware is discussed and a hardware solution based on
physical memristor properties is presented.Comment: 22 pages, 13 pages, conference pape
Evolving spiking networks with variable resistive memories
Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types. © 2014 by the Massachusetts Institute of Technology
Spiking memristor logic gates are a type of time-variant perceptron
Memristors are low-power memory-holding resistors thought to be useful for
neuromophic computing, which can compute via spike-interactions mediated
through the device's short-term memory. Using interacting spikes, it is
possible to build an AND gate that computes OR at the same time, similarly a
full adder can be built that computes the arithmetical sum of its inputs. Here
we show how these gates can be understood by modelling the memristors as a
novel type of perceptron: one which is sensitive to input order. The
memristor's memory can change the input weights for later inputs, and thus the
memristor gates cannot be accurately described by a single perceptron,
requiring either a network of time-invarient perceptrons or a complex
time-varying self-reprogrammable perceptron. This work demonstrates the high
functionality of memristor logic gates, and also that the addition of
theasholding could enable the creation of a standard perceptron in hardware,
which may have use in building neural net chips.Comment: 8 pages, 3 figures. Poster presentation at a conferenc
Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning
Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e. the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy characters are displayed and it is robust to a device-to-device variability of up to +/-30%
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