Learning to represent signals spike by spike.
Networks based on coordinated spike coding can encode information with high efficiency in the spike trains of individual neurons. These networks exhibit single-neuron variability and tuning curves as typically observed in cortex, but paradoxically coincide with a precise, non-redundant spike-based p...
Main Authors: | Wieland Brendel, Ralph Bourdoukan, Pietro Vertechi, Christian K Machens, Sophie Denève |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2020-03-01
|
Series: | PLoS Computational Biology |
Online Access: | https://doi.org/10.1371/journal.pcbi.1007692 |
Similar Items
-
Spike-based population coding and working memory.
by: Martin Boerlin, et al.
Published: (2011-02-01) -
Non-classical receptive field properties reflecting functional aspects of optimal spike based inference
by: Denève Sophie, et al.
Published: (2009-07-01) -
The geometry of robustness in spiking neural networks
by: Nuno Calaim, et al.
Published: (2022-05-01) -
Back-Propagation Learning in Deep Spike-By-Spike Networks
by: David Rotermund, et al.
Published: (2019-08-01) -
Automated and parallelized spike collision tests to identify spike signal projections
by: Keita Mitani, et al.
Published: (2022-10-01)