168 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
Spike-based local synaptic plasticity: a survey of computational models and neuromorphic circuits
Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of real-time, energy-efficient, and adaptive neuromorphic processing systems. A large number of spike-based learning models have recently been proposed following different approaches. However, it is difficult to assess if these models can be easily implemented in neuromorphic hardware, and to compare their features and ease of implementation. To this end, in this survey, we provide an overview of representative brain-inspired synaptic plasticity models and mixed-signal complementary metal–oxide–semiconductor neuromorphic circuits within a unified framework. We review historical, experimental, and theoretical approaches to modeling synaptic plasticity, and we identify computational primitives that can support low-latency and low-power hardware implementations of spike-based learning rules. We provide a common definition of a locality principle based on pre- and postsynaptic neural signals, which we propose as an important requirement for physical implementations of synaptic plasticity circuits. Based on this principle, we compare the properties of these models within the same framework, and describe a set of mixed-signal electronic circuits that can be used to implement their computing principles, and to build efficient on-chip and online learning in neuromorphic processing systems
Spike-based local synaptic plasticity: A survey of computational models and neuromorphic circuits
Understanding how biological neural networks carry out learning using
spike-based local plasticity mechanisms can lead to the development of
powerful, energy-efficient, and adaptive neuromorphic processing systems. A
large number of spike-based learning models have recently been proposed
following different approaches. However, it is difficult to assess if and how
they could be mapped onto neuromorphic hardware, and to compare their features
and ease of implementation. To this end, in this survey, we provide a
comprehensive overview of representative brain-inspired synaptic plasticity
models and mixed-signal CMOS neuromorphic circuits within a unified framework.
We review historical, bottom-up, and top-down approaches to modeling synaptic
plasticity, and we identify computational primitives that can support
low-latency and low-power hardware implementations of spike-based learning
rules. We provide a common definition of a locality principle based on pre- and
post-synaptic neuron information, which we propose as a fundamental requirement
for physical implementations of synaptic plasticity. Based on this principle,
we compare the properties of these models within the same framework, and
describe the mixed-signal electronic circuits that implement their computing
primitives, pointing out how these building blocks enable efficient on-chip and
online learning in neuromorphic processing systems
Multiple-Step Quantized Triplet STDP Implemented with Memristive Synapse
As an extension of the pairwise spike-timingdependent plasticity (STDP)
learning rule, the triplet STDP is provided with greater capability in
characterizing the synaptic changes in the biological neural cell. In this
work, a novel mixedsignal circuit scheme, called multiple-step quantized
triplet STDP, is designed to provide a precise and flexible implementation of
coactivation triplet STDP learning rule in memristive synapse spiking neural
network. The robustness of the circuit is greatly improved through the
utilization of pulse-width encoded weight modulation signals. The circuit
performance is studied through the simulations which are carried out in MATLAB
Simulink & Simscape, and assessment is given by comparing the results of
circuits with the algorithmic approaches.Comment: 5 pages, 10 figure
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