2,767 research outputs found
Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks
Spiking Neural Networks (SNNs) use spatiotemporal spike patterns to represent and transmit information, which are not only biologically realistic but also suitable for ultralow-power event-driven neuromorphic implementation. Just like other deep learning techniques, Deep Spiking Neural Networks (DeepSNNs) benefit from the deep architecture. However, the training of DeepSNNs is not straightforward because the wellstudied error back-propagation (BP) algorithm is not directly applicable. In this paper, we first establish an understanding as to why error back-propagation does not work well in DeepSNNs.We then propose a simple yet efficient Rectified Linear Postsynaptic Potential function (ReL-PSP) for spiking neurons and a Spike-Timing-Dependent Back-Propagation (STDBP) learning algorithm for DeepSNNs where the timing of individual spikes is used to convey information (temporal coding), and learning (back-propagation) is performed based on spike timing in an event-driven manner. We show that DeepSNNs trained with the proposed single spike time-based learning algorithm can achieve state-of-the-art classification accuracy. Furthermore, by utilizing the trained model parameters obtained from the proposed STDBP learning algorithm, we demonstrate ultra-low power inference operations on a recently proposed neuromorphic inference accelerator. The experimental results also show that the neuromorphic hardware consumes 0.751 mW of the total power consumption and achieves a low latency of 47.71 ms to classify an image from the MNIST dataset. Overall, this work investigates the contribution of spike timing dynamics for information encoding, synaptic plasticity and decision making, providing a new perspective to the design of future DeepSNNs and neuromorphic hardware
SuperSpike: Supervised learning in multi-layer spiking neural networks
A vast majority of computation in the brain is performed by spiking neural
networks. Despite the ubiquity of such spiking, we currently lack an
understanding of how biological spiking neural circuits learn and compute
in-vivo, as well as how we can instantiate such capabilities in artificial
spiking circuits in-silico. Here we revisit the problem of supervised learning
in temporally coding multi-layer spiking neural networks. First, by using a
surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based
three factor learning rule capable of training multi-layer networks of
deterministic integrate-and-fire neurons to perform nonlinear computations on
spatiotemporal spike patterns. Second, inspired by recent results on feedback
alignment, we compare the performance of our learning rule under different
credit assignment strategies for propagating output errors to hidden units.
Specifically, we test uniform, symmetric and random feedback, finding that
simpler tasks can be solved with any type of feedback, while more complex tasks
require symmetric feedback. In summary, our results open the door to obtaining
a better scientific understanding of learning and computation in spiking neural
networks by advancing our ability to train them to solve nonlinear problems
involving transformations between different spatiotemporal spike-time patterns
Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations
We present a new back propagation based training algorithm for discrete-time
spiking neural networks (SNN). Inspired by recent deep learning algorithms on
binarized neural networks, binary activation with a straight-through gradient
estimator is used to model the leaky integrate-fire spiking neuron, overcoming
the difficulty in training SNNs using back propagation. Two SNN training
algorithms are proposed: (1) SNN with discontinuous integration, which is
suitable for rate-coded input spikes, and (2) SNN with continuous integration,
which is more general and can handle input spikes with temporal information.
Neuromorphic hardware designed in 40nm CMOS exploits the spike sparsity and
demonstrates high classification accuracy (>98% on MNIST) and low energy
(48.4-773 nJ/image).Comment: 2017 IEEE Biomedical Circuits and Systems (BioCAS
Biologically plausible deep learning -- but how far can we go with shallow networks?
Training deep neural networks with the error backpropagation algorithm is
considered implausible from a biological perspective. Numerous recent
publications suggest elaborate models for biologically plausible variants of
deep learning, typically defining success as reaching around 98% test accuracy
on the MNIST data set. Here, we investigate how far we can go on digit (MNIST)
and object (CIFAR10) classification with biologically plausible, local learning
rules in a network with one hidden layer and a single readout layer. The hidden
layer weights are either fixed (random or random Gabor filters) or trained with
unsupervised methods (PCA, ICA or Sparse Coding) that can be implemented by
local learning rules. The readout layer is trained with a supervised, local
learning rule. We first implement these models with rate neurons. This
comparison reveals, first, that unsupervised learning does not lead to better
performance than fixed random projections or Gabor filters for large hidden
layers. Second, networks with localized receptive fields perform significantly
better than networks with all-to-all connectivity and can reach backpropagation
performance on MNIST. We then implement two of the networks - fixed, localized,
random & random Gabor filters in the hidden layer - with spiking leaky
integrate-and-fire neurons and spike timing dependent plasticity to train the
readout layer. These spiking models achieve > 98.2% test accuracy on MNIST,
which is close to the performance of rate networks with one hidden layer
trained with backpropagation. The performance of our shallow network models is
comparable to most current biologically plausible models of deep learning.
Furthermore, our results with a shallow spiking network provide an important
reference and suggest the use of datasets other than MNIST for testing the
performance of future models of biologically plausible deep learning.Comment: 14 pages, 4 figure
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
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