Supervised learning in multilayer spiking neural networks
I Sporea, A Grüning - Neural computation, 2013 - ieeexplore.ieee.org
We introduce a supervised learning algorithm for multilayer spiking neural networks. The
algorithm overcomes a limitation of existing learning algorithms: it can be applied to neurons …
algorithm overcomes a limitation of existing learning algorithms: it can be applied to neurons …
Rapid feedforward computation by temporal encoding and learning with spiking neurons
Primates perform remarkably well in cognitive tasks such as pattern recognition. Motivated
by recent findings in biological systems, a unified and consistent feedforward system …
by recent findings in biological systems, a unified and consistent feedforward system …
Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule
Understanding how the human brain is able to efficiently perceive and understand a visual
scene is still a field of ongoing research. Although many studies have focused on the design …
scene is still a field of ongoing research. Although many studies have focused on the design …
Training spiking neural networks to associate spatio-temporal input–output spike patterns
In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input–
output spike trains)[1] we have proposed a supervised learning algorithm based on temporal …
output spike trains)[1] we have proposed a supervised learning algorithm based on temporal …
A new supervised learning algorithm for spiking neurons
Y Xu, X Zeng, S Zhong - Neural computation, 2013 - direct.mit.edu
The purpose of supervised learning with temporal encoding for spiking neurons is to make
the neurons emit a specific spike train encoded by the precise firing times of spikes. If only …
the neurons emit a specific spike train encoded by the precise firing times of spikes. If only …
Spike-based indirect training of a spiking neural network-controlled virtual insect
X Zhang, Z Xu, C Henriquez… - 52nd IEEE Conference on …, 2013 - ieeexplore.ieee.org
Spiking neural networks (SNNs) have been shown capable of replicating the spike patterns
observed in biological neuronal networks, and of learning via biologically-plausible …
observed in biological neuronal networks, and of learning via biologically-plausible …
Spatio-temporal spike pattern classification in neuromorphic systems
Spike-based neuromorphic electronic architectures offer an attractive solution for
implementing compact efficient sensory-motor neural processing systems for robotic …
implementing compact efficient sensory-motor neural processing systems for robotic …
Volatile and nonvolatile selective switching of a photo-assisted initialized atomic switch
A photo-assisted atomic switch, which has a photoconductive molecular layer in a gap of
about 20 nm between an Ag 2 S electrode and a Pt electrode, is set to a conventional gap …
about 20 nm between an Ag 2 S electrode and a Pt electrode, is set to a conventional gap …
[图书][B] Spike timing: mechanisms and function
PM DiLorenzo, JD Victor - 2013 - books.google.com
Neuronal communication forms the basis for all behavior, from the smallest movement to our
grandest thought processes. Among the many mechanisms that support these functions …
grandest thought processes. Among the many mechanisms that support these functions …
The Convallis rule for unsupervised learning in cortical networks
The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming
known in increasing detail, but the computational principles by which cortical plasticity …
known in increasing detail, but the computational principles by which cortical plasticity …