A model for short-term and long-term learning in continuous-time recurrent neural networks
Paper in proceedings, 2010

A biologically inspired computational model for learning in continuous-time recurrent neural networks is introduced and described. The model includes both short-term learning, dependent on neural activity, and long-term learning, dependent on synaptic tagging and artificial gene regulation. Even though many aspects of learning remain to be included in the model, it is shown that, in its present state, the model can reproduce important aspects of fundamental forms of learning such as habituation and sensitization.

learning and memory

Neural networks


Mattias Wahde

Chalmers, Applied Mechanics

Proceedings of FAN/iFAN 2010

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