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IJCNN
2008
IEEE
14 years 1 months ago
Biologically realizable reward-modulated hebbian training for spiking neural networks
— Spiking neural networks have been shown capable of simulating sigmoidal artificial neural networks providing promising evidence that they too are universal function approximat...
Silvia Ferrari, Bhavesh Mehta, Gianluca Di Muro, A...
ICONIP
2007
13 years 9 months ago
Analog CMOS Circuits Implementing Neural Segmentation Model Based on Symmetric STDP Learning
We proposed a neural segmentation model that is suitable for implementation in analog VLSIs using conventional CMOS technology. The model consists of neural oscillators mutually co...
Gessyca Maria Tovar, Eric Shun Fukuda, Tetsuya Asa...
NECO
2007
150views more  NECO 2007»
13 years 7 months ago
Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule
Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is ...
Dorit Baras, Ron Meir
NIPS
2001
13 years 9 months ago
Self-regulation Mechanism of Temporally Asymmetric Hebbian Plasticity
Recent biological experimental findings have shown that the synaptic plasticity depends on the relative timing of the pre- and postsynaptic spikes which determines whether Long Te...
N. Matsumoto, M. Okada
IWANN
2005
Springer
14 years 1 months ago
Real-Time Spiking Neural Network: An Adaptive Cerebellar Model
Abstract. A spiking neural network modeling the cerebellum is presented. The model, consisting of more than 2000 conductance-based neurons and more than 50 000 synapses, runs in re...
Christian Boucheny, Richard R. Carrillo, Eduardo R...