9,192 research outputs found
Efficient Design of Triplet Based Spike-Timing Dependent Plasticity
Spike-Timing Dependent Plasticity (STDP) is believed to play an important
role in learning and the formation of computational function in the brain. The
classical model of STDP which considers the timing between pairs of
pre-synaptic and post-synaptic spikes (p-STDP) is incapable of reproducing
synaptic weight changes similar to those seen in biological experiments which
investigate the effect of either higher order spike trains (e.g. triplet and
quadruplet of spikes), or, simultaneous effect of the rate and timing of spike
pairs on synaptic plasticity. In this paper, we firstly investigate synaptic
weight changes using a p-STDP circuit and show how it fails to reproduce the
mentioned complex biological experiments. We then present a new STDP VLSI
circuit which acts based on the timing among triplets of spikes (t-STDP) that
is able to reproduce all the mentioned experimental results. We believe that
our new STDP VLSI circuit improves upon previous circuits, whose learning
capacity exceeds current designs due to its capability of mimicking the
outcomes of biological experiments more closely; thus plays a significant role
in future VLSI implementation of neuromorphic systems
A CMOS Synapse Design Implementing Tunable Asymmetric Spike Timing-Dependent Plasticity
A CMOS synapse design is presented which can perform tunable asymmetric spike timing-dependent learning in asynchronous spiking neural networks. The overall design consists of three primary subcircuit blocks, and the operation of each is described. Pair-based Spike Timing-Dependent Plasticity (STDP) of the entire synapse is then demonstrated through simulation using the Cadence Virtuoso platform. Tuning of the STDP curve learning window and rate of synaptic weight change is possible using various control parameters. With appropriate settings, it is shown the resulting learning rule closely matches that observed in biological systems
Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices
Biologically plausible computing systems require fine- grain tuning of analog synaptic characteristics. In this study, lithium- doped silicate resistive random access memory with a titanium nitride (TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state- dependent decay to be reliably achieved. As a result, this device offers multi- bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short- term memory and long- term memory are emulated across dynamical timescales. Spike- timing- dependent plasticity and paired- pulse facilitation are also demonstrated. These mechanisms are capable of self- pruning to generate efficient neural networks. Time- dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in humanâs higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.In this study, lithium- doped silicate resistive random access memory with a titanium nitride (TiN) electrode is shown to mimic biological synapses. The TiN electrode effectively stores lithium ions, a principle widely adopted from battery construction, and enables reliable state- dependent decay. This device offers multi- bit functionality and synaptic plasticity, short- term memory and long- term memory, spike- timing- dependent plasticity and paired- pulse facilitation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163426/3/smll202003964-sup-0001-SuppMat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163426/2/smll202003964_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163426/1/smll202003964.pd
Anticipated Synchronization in a Biologically Plausible Model of Neuronal Motifs
Two identical autonomous dynamical systems coupled in a master-slave
configuration can exhibit anticipated synchronization (AS) if the slave also
receives a delayed negative self-feedback. Recently, AS was shown to occur in
systems of simplified neuron models, requiring the coupling of the neuronal
membrane potential with its delayed value. However, this coupling has no
obvious biological correlate. Here we propose a canonical neuronal microcircuit
with standard chemical synapses, where the delayed inhibition is provided by an
interneuron. In this biologically plausible scenario, a smooth transition from
delayed synchronization (DS) to AS typically occurs when the inhibitory
synaptic conductance is increased. The phenomenon is shown to be robust when
model parameters are varied within physiological range. Since the DS-AS
transition amounts to an inversion in the timing of the pre- and post-synaptic
spikes, our results could have a bearing on spike-timing-dependent-plasticity
models
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
Design and Implementation of BCM Rule Based on Spike-Timing Dependent Plasticity
The Bienenstock-Cooper-Munro (BCM) and Spike Timing-Dependent Plasticity
(STDP) rules are two experimentally verified form of synaptic plasticity where
the alteration of synaptic weight depends upon the rate and the timing of pre-
and post-synaptic firing of action potentials, respectively. Previous studies
have reported that under specific conditions, i.e. when a random train of
Poissonian distributed spikes are used as inputs, and weight changes occur
according to STDP, it has been shown that the BCM rule is an emergent property.
Here, the applied STDP rule can be either classical pair-based STDP rule, or
the more powerful triplet-based STDP rule. In this paper, we demonstrate the
use of two distinct VLSI circuit implementations of STDP to examine whether BCM
learning is an emergent property of STDP. These circuits are stimulated with
random Poissonian spike trains. The first circuit implements the classical
pair-based STDP, while the second circuit realizes a previously described
triplet-based STDP rule. These two circuits are simulated using 0.35 um CMOS
standard model in HSpice simulator. Simulation results demonstrate that the
proposed triplet-based STDP circuit significantly produces the threshold-based
behaviour of the BCM. Also, the results testify to similar behaviour for the
VLSI circuit for pair-based STDP in generating the BCM
Enhancement of synchronization in a hybrid neural circuit by spike timing dependent plasticity
Synchronization of neural activity is fundamental for many functions of the brain. We demonstrate that spike-timing dependent plasticity (STDP) enhances synchronization (entrainment) in a hybrid circuit composed of a spike generator, a dynamic clamp emulating an excitatory plastic synapse, and a chemically isolated neuron from the Aplysia abdominal ganglion. Fixed-phase entrainment of the Aplysia neuron to the spike generator is possible for a much wider range of frequency ratios and is more precise and more robust with the plastic synapse than with a nonplastic synapse of comparable strength. Further analysis in a computational model of HodgkinHuxley-type neurons reveals the mechanism behind this significant enhancement in synchronization. The experimentally observed STDP plasticity curve appears to be designed to adjust synaptic strength to a value suitable for stable entrainment of the postsynaptic neuron. One functional role of STDP might therefore be to facilitate synchronization or entrainment of nonidentical neurons
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
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