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» Fractionally Predictive Spiking Neurons
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NIPS
2008
13 years 7 months ago
On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing
Randomly connected recurrent neural circuits have proven to be very powerful models for online computations when a trained memoryless readout function is appended. Such Reservoir ...
Benjamin Schrauwen, Lars Buesing, Robert A. Legens...
JCNS
2000
104views more  JCNS 2000»
13 years 5 months ago
Subthreshold Voltage Noise Due to Channel Fluctuations in Active Neuronal Membranes
Abstract. Voltage-gated ion channels in neuronal membranes fluctuate randomly between different conformational states due to thermal agitation. Fluctuations between conducting and ...
Peter N. Steinmetz, Amit Manwani, Christof Koch, M...
NIPS
2007
13 years 7 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...
NN
2007
Springer
13 years 5 months ago
Edge of chaos and prediction of computational performance for neural circuit models
We analyze in this article the significance of the edge of chaos for real-time computations in neural microcircuit models consisting of spiking neurons and dynamic synapses. We ...
Robert A. Legenstein, Wolfgang Maass
IEAAIE
2005
Springer
13 years 11 months ago
Movement Prediction from Real-World Images Using a Liquid State Machine
Prediction is an important task in robot motor control where it is used to gain feedback for a controller. With such a self-generated feedback, which is available before sensor rea...
Harald Burgsteiner, Mark Kröll, Alexander Leo...