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Found 6 papers, 0 papers with code
Supervised Learning with First-to-Spike Decoding in Multilayer Spiking Neural Networks
The proposed learning rule supports multiple spikes fired by stochastic hidden neurons, and yet is stable by relying on first-spike responses generated by a deterministic output layer.
Supervised Learning in Temporally-Coded Spiking Neural Networks with Approximate Backpropagation
In this work we propose a new supervised learning method for temporally-encoded multilayer spiking networks to perform classification.
An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be leveraged by energy-efficient hardware implementations, which can offer significant energy reductions as compared to conventional artificial neural networks (ANNs).
An Introduction to Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
This paper aims at providing an introduction to SNNs by focusing on a probabilistic signal processing methodology that enables the direct derivation of learning rules leveraging the unique time encoding capabilities of SNNs.
Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding
We also find FILT to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision.
Encoding Spike Patterns in Multilayer Spiking Neural Networks
Information encoding in the nervous system is supported through the precise spike-timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains unclear.
