Open AccessJournal Article
Introduction to spiking neural networks: Information processing, learning and applications.
Filip Ponulak,Andrzej Kasiński +1 more
344
TL;DR: This paper summarizes basic properties of spiking neurons and spiking networks, and focuses, specifically, on models of spike-based information coding, synaptic plasticity and learning.
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Abstract: The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.
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Citations
A Cross-Correlated Delay Shift Supervised Learning Method for Spiking Neurons with Application to Interictal Spike Detection in Epilepsy.
TL;DR: Results presented show that the CCDS learning method achieves learning accuracy and learning speed comparable with ReSuMe, but improves classification accuracy when compared to both the Spike Pattern Association Neuron (SPAN) learning rule and the Tempotron learning rule.
37
Identifying data streams anomalies by evolving spiking restricted Boltzmann machines
TL;DR: This research introduces a real-time evolving spiking restricted Boltzmann machine approach, for efficient anomaly detection in data streams, and proves that the proposed algorithm maximizes the classification accuracy and at the same time minimizes the computational resources requirements.
36
Towards biological plausibility of electronic noses
Sankho Turjo Sarkar,Amol P. Bhondekar,Martin Macas,Ritesh Kumar,Rishemjit Kaur,Anupma Sharma,Ashu Gulati,Amod Kumar +7 more
TL;DR: A novel encoding scheme for neuronal code generation for odour recognition using an electronic nose using multiple Gaussian receptive fields superimposed over the temporal EN responses is presented, demonstrating a biomimetic approach for EN data analysis.
35
A digital implementation of 2D Hindmarsh–Rose neuron
TL;DR: A set of piece-wise linear approximations of a two-dimensional Hindmarsh–Rose neuron model for digital circuit implementation to achieve higher speeds and lower hardware costs in large-scale implementation of the biological neural networks.
35
An Efficient Uniform-Segmented Neuron Model for Large-Scale Neuromorphic Circuit Design: Simulation and FPGA Synthesis Results
TL;DR: A novel uniform piecewise linear segmentation approach for nonlinear function evaluations is presented, capable of accurately producing various responses exhibited by the original model and suitable for efficient large-scale implementation.
34
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