Book ChapterDOI
Region-Based Encoding Method Using Multi-dimensional Gaussians for Networks of Spiking Neurons
Lakshmi Narayana Panuku,C. Chandra Sekhar +1 more
- pp 73-82
TLDR
This paper proposes a region-based encoding method that places multi-dimensional Gaussian receptive fields in the data-inhabited regions, and captures the correlation among the variables.Abstract:
In this paper, we address the issues in representation of continuous valued variables by firing times of neurons in the spiking neural network used for clustering multi-variate data. The existing range-based encoding method encodes each dimension separately. This method does not make use of the correlation among the different variables, and the knowledge of the distribution of data. We propose a region-based encoding method that places multi-dimensional Gaussian receptive fields in the data-inhabited regions, and captures the correlation among the variables. Effectiveness of the proposed encoding method in clustering the complex 2-dimensional and 3-dimensional data sets is demonstrated.read more
Citations
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Journal ArticleDOI
Unsupervised anomaly detection in multivariate time series with online evolving spiking neural networks
TL;DR: In this paper , the authors presented a method for detecting anomalies in streaming multivariate times series by using an adapted evolving Spiking Neural Network (SNN), which uses the precise times of the incoming spikes for adjusting the synaptic weights, an adapted, real-time-capable and efficient encoding technique for multivariate data based on multi-dimensional Gaussian receptive fields and a continuous outlier scoring function for an improved interpretability of the classifications.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI
Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type
Guo-Qiang Bi,Mu-ming Poo +1 more
TL;DR: The results underscore the importance of precise spike timing, synaptic strength, and postsynaptic cell type in the activity-induced modification of central synapses and suggest that Hebb’s rule may need to incorporate a quantitative consideration of spike timing that reflects the narrow and asymmetric window for the induction of synaptic modification.
Journal ArticleDOI
Networks of spiking neurons: the third generation of neural network models
TL;DR: It is shown that networks of spiking neurons are, with regard to the number of neurons that are needed, computationally more powerful than other neural network models based on McCulloch Pitts neurons and sigmoidal gates.
Book ChapterDOI
Spiking Neuron Models
W. Gerstner,W. K. Kistler +1 more
TL;DR: Note: book Reference LCN-BOOK-2002-001 URL: http://diwww.epfl.ch/~gerstner/BUCH.html
Journal ArticleDOI
A neuronal learning rule for sub-millisecond temporal coding
Wulfram Gerstner,Wulfram Gerstner,Richard Kempter,J. Leo van Hemmen,Hermann Wagner,Hermann Wagner +5 more
TL;DR: A modelling study based on computer simulations of a neuron in the laminar nucleus of the barn owl shows that the necessary degree of coherence in the signal arrival times can be attained during ontogenetic development by virtue of an unsupervised hebbian learning rule.