Abstract:
:Mechanisms influencing learning in neural networks are usually investigated on either a local or a global scale. The former relates to synaptic processes, the latter to unspecific modulatory systems. Here we study the interaction of a local learning rule that evaluates coincidences of pre- and postsynaptic action potentials and a global modulatory mechanism, such as the action of the basal forebrain onto cortical neurons. The simulations demonstrate that the interaction of these mechanisms leads to a learning rule supporting fast learning rates, stability, and flexibility. Furthermore, the simulations generate two experimentally testable predictions on the dependence of backpropagating action potential on basal forebrain activity and the relative timing of the activity of inhibitory and excitatory neurons in the neocortex.
journal_name
Neural Computjournal_title
Neural computationauthors
Sánchez-Montañés MA,Verschure PF,König Pdoi
10.1162/089976600300015682subject
Has Abstractpub_date
2000-03-01 00:00:00pages
519-29issue
3eissn
0899-7667issn
1530-888Xjournal_volume
12pub_type
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