90 citations · 187 across the 6 of their papers we have counts for
6 papers
Supervised Learning with First-to-Spike Decoding in Multilayer Spiking Neural Networks
Brian Gardner, André Grüning
Experimental studies support the notion of spike-based neuronal information processing in the brain, with neural circuits exhibiting a wide range of temporally-based coding strateg…
Supervised Learning in Temporally-Coded Spiking Neural Networks with Approximate Backpropagation
Andrew Stephan, Brian Gardner, Steven J. Koester +1
In this work we propose a new supervised learning method for temporally-encoded multilayer spiking networks to perform classification. The method employs a reinforcement signal tha…
An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
Hyeryung Jang, Osvaldo Simeone, Brian Gardner +1
Spiking neural networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synap…
An Introduction to Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
Hyeryung Jang, Osvaldo Simeone, Brian Gardner +1
Spiking Neural Networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synap…
Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding
Brian Gardner, André Grüning
Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a…
Encoding Spike Patterns in Multilayer Spiking Neural Networks
Brian Gardner, Ioana Sporea, André Grüning
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 representa…