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
Reads0
Chats0
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
More filters
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
More filters
Journal ArticleDOI
Fast sigmoidal networks via spiking neurons
TL;DR: It is shown that networks of relatively realistic mathematical models for biological neurons in principle can simulate arbitrary feedforward sigmoidal neural nets in a way that has previously not been considered and are universal approximators in the sense that they can approximate with regard to temporal coding any given continuous function of several variables.
Journal Article
Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks
TL;DR: A temporal encoding of continuously valued data to obtain adjustable clustering capacity and precision with an efficient use of neurons is developed and encoded in a population code by neurons with graded and overlapping sensitivity profiles.
Journal ArticleDOI
Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks
TL;DR: In this paper, a spiking neural network based on spike-time coding and Hebbian learning is proposed for unsupervised clustering on real-world data, and temporal synchrony in a multilayer network can induce hierarchical clustering.
Journal ArticleDOI
Spatial and temporal pattern analysis via spiking neurons.
Thomas Natschläger,Berthold Ruf +1 more
TL;DR: This paper shows how these delays can be learned using exclusively locally available information and gives rise to a biologically plausible algorithm for finding clusters in a high-dimensional input space with networks of spiking neurons, even if the environment is changing dynamically.