253 research outputs found
An organic nanoparticle transistor behaving as a biological synapse
Molecule-based devices are envisioned to complement silicon devices by
providing new functions or already existing functions at a simpler process
level and at a lower cost by virtue of their self-organization capabilities.
Moreover, they are not bound to von Neuman architecture and this feature may
open the way to other architectural paradigms. Neuromorphic electronics is one
of them. Here we demonstrate a device made of molecules and nanoparticles, a
nanoparticle organic memory filed-effect transistor (NOMFET), which exhibits
the main behavior of a biological spiking synapse. Facilitating and depressing
synaptic behaviors can be reproduced by the NOMFET and can be programmed. The
synaptic plasticity for real time computing is evidenced and described by a
simple model. These results open the way to rate coding utilization of the
NOMFET in dynamical neuromorphic computing circuits.Comment: To be publsihed in Adv. Func. Mater. Revised version. One pdf file
including main paper and supplementary informatio
Emulating long-term synaptic dynamics with memristive devices
The potential of memristive devices is often seeing in implementing
neuromorphic architectures for achieving brain-like computation. However, the
designing procedures do not allow for extended manipulation of the material,
unlike CMOS technology, the properties of the memristive material should be
harnessed in the context of such computation, under the view that biological
synapses are memristors. Here we demonstrate that single solid-state TiO2
memristors can exhibit associative plasticity phenomena observed in biological
cortical synapses, and are captured by a phenomenological plasticity model
called triplet rule. This rule comprises of a spike-timing dependent plasticity
regime and a classical hebbian associative regime, and is compatible with a
large amount of electrophysiology data. Via a set of experiments with our
artificial, memristive, synapses we show that, contrary to conventional uses of
solid-state memory, the co-existence of field- and thermally-driven switching
mechanisms that could render bipolar and/or unipolar programming modes is a
salient feature for capturing long-term potentiation and depression synaptic
dynamics. We further demonstrate that the non-linear accumulating nature of
memristors promotes long-term potentiating or depressing memory transitions
A memristive nanoparticle/organic hybrid synapstor for neuro-inspired computing
A large effort is devoted to the research of new computing paradigms
associated to innovative nanotechnologies that should complement and/or propose
alternative solutions to the classical Von Neumann/CMOS association. Among
various propositions, Spiking Neural Network (SNN) seems a valid candidate. (i)
In terms of functions, SNN using relative spike timing for information coding
are deemed to be the most effective at taking inspiration from the brain to
allow fast and efficient processing of information for complex tasks in
recognition or classification. (ii) In terms of technology, SNN may be able to
benefit the most from nanodevices, because SNN architectures are intrinsically
tolerant to defective devices and performance variability. Here we demonstrate
Spike-Timing-Dependent Plasticity (STDP), a basic and primordial learning
function in the brain, with a new class of synapstor (synapse-transistor),
called Nanoparticle Organic Memory Field Effect Transistor (NOMFET). We show
that this learning function is obtained with a simple hybrid material made of
the self-assembly of gold nanoparticles and organic semiconductor thin films.
Beyond mimicking biological synapses, we also demonstrate how the shape of the
applied spikes can tailor the STDP learning function. Moreover, the experiments
and modeling show that this synapstor is a memristive device. Finally, these
synapstors are successfully coupled with a CMOS platform emulating the pre- and
post-synaptic neurons, and a behavioral macro-model is developed on usual
device simulator.Comment: A single pdf file, with the full paper and the supplementary
information; Adv. Func. Mater., on line Dec. 13 (2011
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
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
Fan-In analysis of a leaky integrator circuit using charge transfer synapses
It is shown that a simple leaky integrator (LI) circuit operating in a dynamic mode can allow spatial and temporal summation of weighted synaptic outputs. The circuit incorporates a current mirror configuration to sum charge packets released from charge transfer synapses and an n-channel MOSFET, operating in subthreshold, serves to implement a leakage capability, which sets the decay time for the postsynaptic response. The focus of the paper is to develop an analytical model for fan-in and validate the model against simulation and experimental results obtained from a prototype chip fabricated in the AMS 0.35 μm mixed signal CMOS technology. We show that the model predicts the theoretical limit on fan-in, relates the magnitude of the postsynaptic response to weighted synaptic inputs and captures the transient response of the LI when stimulated with spike inputs
Six networks on a universal neuromorphic computing substrate
In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and digital transmission of action potentials. Major advantages of this emulation device, which has been explicitly designed as a universal neural network emulator, are its inherent parallelism and high acceleration factor compared to conventional computers. Its configurability allows the realization of almost arbitrary network topologies and the use of widely varied neuronal and synaptic parameters. Fixed-pattern noise inherent to analog circuitry is reduced by calibration routines. An integrated development environment allows neuroscientists to operate the device without any prior knowledge of neuromorphic circuit design. As a showcase for the capabilities of the system, we describe the successful emulation of six different neural networks which cover a broad spectrum of both structure and functionality
Compensating Inhomogeneities of Neuromorphic VLSI Devices Via Short-Term Synaptic Plasticity
Recent developments in neuromorphic hardware engineering make mixed-signal VLSI neural network models promising candidates for neuroscientific research tools and massively parallel computing devices, especially for tasks which exhaust the computing power of software simulations. Still, like all analog hardware systems, neuromorphic models suffer from a constricted configurability and production-related fluctuations of device characteristics. Since also future systems, involving ever-smaller structures, will inevitably exhibit such inhomogeneities on the unit level, self-regulation properties become a crucial requirement for their successful operation. By applying a cortically inspired self-adjusting network architecture, we show that the activity of generic spiking neural networks emulated on a neuromorphic hardware system can be kept within a biologically realistic firing regime and gain a remarkable robustness against transistor-level variations. As a first approach of this kind in engineering practice, the short-term synaptic depression and facilitation mechanisms implemented within an analog VLSI model of I&F neurons are functionally utilized for the purpose of network level stabilization. We present experimental data acquired both from the hardware model and from comparative software simulations which prove the applicability of the employed paradigm to neuromorphic VLSI devices
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