Introduction to spiking neural networks: Information processing, learning and applications.

By Filip Ponulak, Andrzej J. Kasinski. Acta Neurobiologiae Experimentalis, 2011

365 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/088c2cf86dc366fd6e659605f1f88d13de32dd50

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Citations in the corpus

A review of learning in biologically plausible spiking neural networks (2019, neutral): “Dopamine is a neuromodulator which is emitted by dopaminergic cells (Ponulak, F. & Kasinski, 2011).”
An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems (2014, background): “46 and 47 and are composed of spiking neurons communicating by sequences of spikes, use time differences between pulses to encode information and are able to process substantial amount of information with a relatively small number of spikes.(28,60) As both computationally powerful and biologically m”
Learning in spiking neural networks (2013, methodology): “A link between the theory behind this type of learning and its biological implementation in neural networks has been found only recently (Ponulak and Kasinski, 2011).”
Deep Learning With Spiking Neurons: Opportunities and Challenges (2018, background): “…machine learning, computational neuroscience, neuromorphic engineering, and embedded systems design have tried to bridge the gap between the big success of DNNs in AI applications and the promise of spiking neural networks (SNNs) (Maass, 1997; Ponulak and Kasinski, 2011; Grüning and Bohte, 2014).”