7,715 research outputs found
Community detection with spiking neural networks for neuromorphic hardware
We present results related to the performance of an algorithm for community
detection which incorporates event-driven computation. We define a mapping
which takes a graph G to a system of spiking neurons. Using a fully connected
spiking neuron system, with both inhibitory and excitatory synaptic
connections, the firing patterns of neurons within the same community can be
distinguished from firing patterns of neurons in different communities. On a
random graph with 128 vertices and known community structure we show that by
using binary decoding and a Hamming-distance based metric, individual
communities can be identified from spike train similarities. Using bipolar
decoding and finite rate thresholding, we verify that inhibitory connections
prevent the spread of spiking patterns.Comment: Conference paper presented at ORNL Neuromorphic Workshop 2017, 7
pages, 6 figure
Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals
Spiking neural networks (SNNs) enable power-efficient implementations due to
their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN
that uses unsupervised learning to extract discriminative features from speech
signals, which can subsequently be used in a classifier. The architecture
consists of a spiking convolutional/pooling layer followed by a fully connected
spiking layer for feature discovery. The convolutional layer of leaky,
integrate-and-fire (LIF) neurons represents primary acoustic features. The
fully connected layer is equipped with a probabilistic spike-timing-dependent
plasticity learning rule. This layer represents the discriminative features
through probabilistic, LIF neurons. To assess the discriminative power of the
learned features, they are used in a hidden Markov model (HMM) for spoken digit
recognition. The experimental results show performance above 96% that compares
favorably with popular statistical feature extraction methods. Our results
provide a novel demonstration of unsupervised feature acquisition in an SNN
The impact of spike timing variability on the signal-encoding performance of neural spiking models
It remains unclear whether the variability of neuronal spike trains in vivo arises due to biological noise sources or represents highly precise encoding of temporally varying synaptic input signals. Determining the variability of spike timing can provide fundamental insights into the nature of strategies used in the brain to represent and transmit information in the form of discrete spike trains. In this study, we employ a signal estimation paradigm to determine how variability in spike timing affects encoding of random time-varying signals. We assess this for two types of spiking models: an integrate-and-fire model with random threshold and a more biophysically realistic stochastic ion channel model. Using the coding fraction and mutual information as information-theoretic measures, we quantify the efficacy of optimal linear decoding of random inputs from the model outputs and study the relationship between efficacy and variability in the output spike train. Our findings suggest that variability does not necessarily hinder signal decoding for the biophysically plausible encoders examined and that the functional role of spiking variability depends intimately on the nature of the encoder and the signal processing task; variability can either enhance or impede decoding performance
Supervised Learning in Multilayer Spiking Neural Networks
The current article introduces a supervised learning algorithm for multilayer
spiking neural networks. The algorithm presented here overcomes some
limitations of existing learning algorithms as it can be applied to neurons
firing multiple spikes and it can in principle be applied to any linearisable
neuron model. The algorithm is applied successfully to various benchmarks, such
as the XOR problem and the Iris data set, as well as complex classifications
problems. The simulations also show the flexibility of this supervised learning
algorithm which permits different encodings of the spike timing patterns,
including precise spike trains encoding.Comment: 38 pages, 4 figure
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