The DOI matches its Crossref record; 204 of the 204 references it cites were found in a registry or an index.
Introduction to spiking neural networks: Information processing, learning and applications
Open accessAbstract
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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Cited by
A review of learning in biologically plausible spiking neural networks
Taherkhani, A., Belatreche, A., Li, Y., Cosma, G., Maguire, L. P., & McGinnity, T. M. (2020) · Neural Networks · doi:10.1016/j.neunet.2019.09.036 · Cited by 422
Spiking Neural Networks and online learning: An overview and perspectives
Lobo, J. L., Del Ser, J., Bifet, A., & Kasabov, N. (2020) · Neural Networks · doi:10.1016/j.neunet.2019.09.004 · Cited by 304
Metadata
- Authors
- Ponulak, 2., & Kasinski, A.
- Venue
- Acta Neurobiologiae Experimentalis
- Published
- 2011-12-31
- Volume
- 71
- Issue
- 4
- Pages
- 409-433
- ISSN
- 0065-1400 (ISSN-L)
- 1689-0035
- OpenAlex
- W2038786761
- Cited by
- 368
- References
- 204
- FWCI
- 1.81
- Type
- journal article
- Language
- en
- Open access
- diamond
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