19 search results - page 3 / 4 » An adaptive spike-timing-dependent plasticity rule |
IWANN
2005
Springer
13 years 10 months ago
2005
Springer
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...
ICANN
2003
Springer
13 years 10 months ago
2003
Springer
Many activity dependent learning rules have been proposed in order to model long-term potentiation (LTP). Our aim is to derive a spike time dependent learning rule from a probabili...
NIPS
2004
13 years 6 months ago
2004
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
HIS
2008
13 years 6 months ago
2008
Artificial Neural Networks for online learning problems are often implemented with synaptic plasticity to achieve adaptive behaviour. A common problem is that the overall learning...
ISCAS
2011
IEEE
12 years 9 months ago
2011
IEEE
— we present an implementation of a programmable axonal propagation delay circuit which uses one first-order logdomain low-pass filter. Delays may be programmed in the 550ms rang...