Evolving spiking neural networks for audiovisual information processing
By Simei Gomes Wysoski, Lubica Benuskova, Nikola K. Kasabov. 2010
152 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Developmental Biology, Experimental and Cognitive Psychology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Maryam Gholami Doborjeh
- Zohreh Gholami Doborjeh
- Nikola K. Kasabov
- S. R. Kheradpisheh
- J. Urgen Schmidhuber
- Masoud Ghodrati
- Anthony S. Maida
- Amirhossein Tavanaei
In Developmental Biology
In Experimental and Cognitive Psychology
Citations in the corpus
- Deep learning in neural networks: An overview (2014, background): “Spiking nets were evolved to achieve reasonable performance on small face recognition data sets (Wysoski et al., 2010) and to control simple robots (Floreano and Mattiussi, 2001; Hagras et al.”
- Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. (2013, methodology): “their similarity is above a certain threshold Sim, the newneuron will be merged with the most similar one, averaging the connectionweights and the threshold of the two neurons (Kasabov, 2007; Wysoski et al., 2010).”
- Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks (2017, background): “, neuromorphic hardware [12]–[14], SNN for image and speech processing as trains of spikes [19]–[21], unsupervised [23] and supervised learning and classification systems [25]–[27], and so on.”
- Deep Learning in Spiking Neural Networks (2018, background): “SNNs have become the focus of a number of recent applications in many areas of pattern recognition such as visual processing [27], [28], [29], [30], speech recognition [31], [32], [33], [34], [35], [36], [37], and medical diagnosis [38], [39].”
- NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data (2014, unknown): “The second approach does not exclude using evolutionary computation to optimize the GRN as it is the case in the CNGM simulator (Kasabov, 2007; Wysoski et al., 2010).”