150 research outputs found
Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding
Precise spike timing as a means to encode information in neural networks is
biologically supported, and is advantageous over frequency-based codes by
processing input features on a much shorter time-scale. For these reasons, much
recent attention has been focused on the development of supervised learning
rules for spiking neural networks that utilise a temporal coding scheme.
However, despite significant progress in this area, there still lack rules that
have a theoretical basis, and yet can be considered biologically relevant. Here
we examine the general conditions under which synaptic plasticity most
effectively takes place to support the supervised learning of a precise
temporal code. As part of our analysis we examine two spike-based learning
methods: one of which relies on an instantaneous error signal to modify
synaptic weights in a network (INST rule), and the other one on a filtered
error signal for smoother synaptic weight modifications (FILT rule). We test
the accuracy of the solutions provided by each rule with respect to their
temporal encoding precision, and then measure the maximum number of input
patterns they can learn to memorise using the precise timings of individual
spikes as an indication of their storage capacity. Our results demonstrate the
high performance of FILT in most cases, underpinned by the rule's
error-filtering mechanism, which is predicted to provide smooth convergence
towards a desired solution during learning. We also find FILT to be most
efficient at performing input pattern memorisations, and most noticeably when
patterns are identified using spikes with sub-millisecond temporal precision.
In comparison with existing work, we determine the performance of FILT to be
consistent with that of the highly efficient E-learning Chronotron, but with
the distinct advantage that FILT is also implementable as an online method for
increased biological realism.Comment: 26 pages, 10 figures, this version is published in PLoS ONE and
incorporates reviewer comment
Multi-layered Spiking Neural Network with Target Timestamp Threshold Adaptation and STDP
Spiking neural networks (SNNs) are good candidates to produce
ultra-energy-efficient hardware. However, the performance of these models is
currently behind traditional methods. Introducing multi-layered SNNs is a
promising way to reduce this gap. We propose in this paper a new threshold
adaptation system which uses a timestamp objective at which neurons should
fire. We show that our method leads to state-of-the-art classification rates on
the MNIST dataset (98.60%) and the Faces/Motorbikes dataset (99.46%) with an
unsupervised SNN followed by a linear SVM. We also investigate the sparsity
level of the network by testing different inhibition policies and STDP rules
Frameworks for SNNs: a Review of Data Science-oriented Software and an Expansion of SpykeTorch
Developing effective learning systems for Machine Learning (ML) applications
in the Neuromorphic (NM) field requires extensive experimentation and
simulation. Software frameworks aid and ease this process by providing a set of
ready-to-use tools that researchers can leverage. The recent interest in NM
technology has seen the development of several new frameworks that do this, and
that add up to the panorama of already existing libraries that belong to
neuroscience fields. This work reviews 9 frameworks for the development of
Spiking Neural Networks (SNNs) that are specifically oriented towards data
science applications. We emphasize the availability of spiking neuron models
and learning rules to more easily direct decisions on the most suitable
frameworks to carry out different types of research. Furthermore, we present an
extension to the SpykeTorch framework that gives users access to a much broader
choice of neuron models to embed in SNNs and make the code publicly available
Neuromorphic Engineering Editors' Pick 2021
This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors
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