12 search results - page 2 / 3 » Reinforcement Learning, Spike-Time-Dependent Plasticity, and... |
NECO
2006
2006
Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning
13 years 5 months ago
In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimi...
ESANN
2007
13 years 6 months ago
2007
Abstract. We developed a computational model of learning in the Mushroom Body, a region of multimodal integration in the insect brain. Using realistic neural dynamics and a biologi...
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...
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...
ECAL
2001
Springer
13 years 9 months ago
2001
Springer
Reinforcement learning (RL) is a fundamental process by which organisms learn to achieve a goal from interactions with the environment. Using Artificial Life techniques we derive ...