6,036 research outputs found
Exploiting Device Mismatch in Neuromorphic VLSI Systems to Implement Axonal Delays
Sheik S, Chicca E, Indiveri G. Exploiting Device Mismatch in Neuromorphic VLSI Systems to Implement Axonal Delays. Presented at the International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia.Axonal delays are used in neural computation to implement faithful models of biological neural systems, and in spiking neural networks models to solve computationally demanding tasks. While there is an increasing number of software simulations of spiking neural networks that make use of axonal delays, only a small fraction of currently existing hardware neuromorphic systems supports them. In this paper we demonstrate a strategy to implement temporal delays in hardware spiking neural networks distributed across multiple Very Large Scale Integration (VLSI) chips. This is achieved by exploiting the inherent device mismatch present in the analog circuits that implement silicon neurons and synapses inside the chips, and the digital communication infrastructure used to configure the network topology and transmit the spikes across chips. We present an example of a recurrent VLSI spiking neural network that employs axonal delays and demonstrate how the proposed strategy efficiently implements them in hardware
An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing
The aggregate-label learning paradigm tackles the long-standing temporary credit assignment (TCA) problem in neuroscience and machine learning, enabling spiking neural networks to learn multimodal sensory clues with delayed feedback signals. However, the existing aggregate-label learning algorithms only work for single spiking neurons, and with low learning efficiency, which limit their real-world applicability. To address these limitations, we first propose an efficient threshold-driven plasticity algorithm for spiking neurons, namely ETDP. It enables spiking neurons to generate the desired number of spikes that match the magnitude of delayed feedback signals and to learn useful multimodal sensory clues embedded within spontaneous spiking activities. Furthermore, we extend the ETDP algorithm to support multi-layer spiking neural networks (SNNs), which significantly improves the applicability of aggregate-label learning algorithms. We also validate the multi-layer ETDP learning algorithm in a multimodal computation framework for audio-visual pattern recognition. Experimental results on both synthetic and realistic datasets show significant improvements in the learning efficiency and model capacity over the existing aggregate-label learning algorithms. It, therefore, provides many opportunities for solving real-world multimodal pattern recognition tasks with spiking neural networks
Phase Diagram of Spiking Neural Networks
In computer simulations of spiking neural networks, often it is assumed that
every two neurons of the network are connected by a probability of 2\%, 20\% of
neurons are inhibitory and 80\% are excitatory. These common values are based
on experiments, observations, and trials and errors, but here, I take a
different perspective, inspired by evolution, I systematically simulate many
networks, each with a different set of parameters, and then I try to figure out
what makes the common values desirable. I stimulate networks with pulses and
then measure their: dynamic range, dominant frequency of population activities,
total duration of activities, maximum rate of population and the occurrence
time of maximum rate. The results are organized in phase diagram. This phase
diagram gives an insight into the space of parameters -- excitatory to
inhibitory ratio, sparseness of connections and synaptic weights. This phase
diagram can be used to decide the parameters of a model. The phase diagrams
show that networks which are configured according to the common values, have a
good dynamic range in response to an impulse and their dynamic range is robust
in respect to synaptic weights, and for some synaptic weights they oscillate in
or frequencies, even in absence of external stimuli.Comment: oscillations are studied in this versio
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
Unsupervised Learning with Self-Organizing Spiking Neural Networks
We present a system comprising a hybridization of self-organized map (SOM)
properties with spiking neural networks (SNNs) that retain many of the features
of SOMs. Networks are trained in an unsupervised manner to learn a
self-organized lattice of filters via excitatory-inhibitory interactions among
populations of neurons. We develop and test various inhibition strategies, such
as growing with inter-neuron distance and two distinct levels of inhibition.
The quality of the unsupervised learning algorithm is evaluated using examples
with known labels. Several biologically-inspired classification tools are
proposed and compared, including population-level confidence rating, and
n-grams using spike motif algorithm. Using the optimal choice of parameters,
our approach produces improvements over state-of-art spiking neural networks
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