Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Biophysiologically plausible implementations of the maximum operation
Neural Computation
Polychronization: Computation with Spikes
Neural Computation
Neurons Tune to the Earliest Spikes Through STDP
Neural Computation
What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?
Neural Computation
On the Computational Power of Winner-Take-All
Neural Computation
A winner-take-all mechanism based on presynaptic inhibition feedback
Neural Computation
A canonical neural circuit for cortical nonlinear operations
Neural Computation
Dynamics and storage capacity of neural networks with small-world topology
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
The right delay: detecting specific spike patterns with STDP and axonal conduction delays
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Spike-Based image processing: can we reproduce biological vision in hardware?
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Spike-timing-dependent construction
Neural Computation
Spatio-temporal spike pattern classification in neuromorphic systems
Living Machines'13 Proceedings of the Second international conference on Biomimetic and Biohybrid Systems
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Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in equally dense distracter spike trains can be robustly detected and learned by a single neuron equipped with spike-timing-dependent plasticity (STDP) (Masquelier, Guyonneau, & Thorpe, 2008). To be precise, the neuron becomes selective to successive coincidences of the pattern. Here we extend this scheme to a more realistic scenario with multiple repeating patterns and multiple STDP neurons “listening” to the incoming spike trains. These “listening” neurons are in competition: as soon as one fires, it strongly inhibits the others through lateral connections (one-winner-take-all mechanism). This tends to prevent the neurons from learning the same (parts of the) repeating patterns, as shown in simulations. Instead, the population self-organizes, trying to cover the different patterns or coding one pattern by the successive firings of several neurons, and a powerful distributed coding scheme emerges. Taken together, these results illustrate how the brain could easily encode and decode information in the spike times, a theory referred to as temporal coding, and how STDP could play a key role by detecting repeating patterns and generating selective response to them.