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» Modeling spiking neural networks
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KES
2008
Springer
13 years 7 months ago
On the use of spiking neural network for EEG classification
This paper presents a new classification technique of continuous EEG recordings, based on a network of spiking neurons. Human EEG signals published on the BCI Competition website w...
Piyush Goel, Honghai Liu, David J. Brown, Avijit D...
NIPS
2007
13 years 9 months ago
A neural network implementing optimal state estimation based on dynamic spike train decoding
It is becoming increasingly evident that organisms acting in uncertain dynamical environments often employ exact or approximate Bayesian statistical calculations in order to conti...
Omer Bobrowski, Ron Meir, Shy Shoham, Yonina C. El...
ICES
2010
Springer
277views Hardware» more  ICES 2010»
13 years 5 months ago
An Efficient, High-Throughput Adaptive NoC Router for Large Scale Spiking Neural Network Hardware Implementations
Recently, a reconfigurable and biologically inspired paradigm based on network-on-chip (NoC) and spiking neural networks (SNNs) has been proposed as a new method of realising an ef...
Snaider Carrillo, Jim Harkin, Liam McDaid, Sandeep...
ICANN
2007
Springer
14 years 1 months ago
Neuronal Multistability Induced by Delay
Abstract. Feedback circuits are important for understanding the emergence of patterns of neural activity. In this contribution we study how a delayed circuit representing a recurre...
Cristina Masoller, M. C. Torrent, Jordi Garc&iacut...
NCI
2004
132views Neural Networks» more  NCI 2004»
13 years 9 months ago
A comparison between spiking and differentiable recurrent neural networks on spoken digit recognition
In this paper we demonstrate that Long Short-Term Memory (LSTM) is a differentiable recurrent neural net (RNN) capable of robustly categorizing timewarped speech data. We measure ...
Alex Graves, Nicole Beringer, Jürgen Schmidhu...