Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition.

By Nikola K. Kasabov, K. Dhoble, Nuttapod Nuntalid, Giacomo Indiveri. Neural Networks, 2013

383 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/337878293b38b0db01f32a444b4dc072f0c6fbb8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nikola K. Kasabov
2. Maryam Gholami Doborjeh
3. Zohreh Gholami Doborjeh
4. Edwin David Lughofer
5. J. J. Rubio
6. Plamen Parvanov Angelov
7. Igor Skrjanc
8. Dejan Dovzan

In Cognitive Neuroscience

1. Maryam Gholami Doborjeh
2. Zohreh Gholami Doborjeh
3. Nikola K. Kasabov

In Electrical and Electronic Engineering

1. Maryam Gholami Doborjeh
2. Zohreh Gholami Doborjeh
3. Nikola K. Kasabov

Citations in the corpus

• Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks (2017, methodology): “Here, we have used the deSNN classifier [25].”
• NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data (2014, neutral): “The dynamic eSNN (deSNN) (Kasabov et al., 2013) combines rank-order and temporal (e.g. STDP) learning rules.”
• A review of learning in biologically plausible spiking neural networks (2019, neutral): “…a dynamic eSNN (deSNN) was proposed to capture the information in more complex spatiotemporal input patterns composed of multiple spikes. deSNN (Kasabov et al., 2013) uses rank-order learning (similar to eSNN (Wysoski et al., 2008)) as well as the spike driven synaptic plasticity (SDSP)…”