Topic: linear rate networks

Synaptic plasticity is key to the brain’s life-long learning capabilities, but the link between changes at individual synapses and emerging network properties remains elusive. Recent meta-learning approaches have started automating the search through the space of possible plasticity rules for plausible candidates. However, these approaches were either limited to narrow sets of rules, or did not investigate any network function beyond stability. Here, we identify thousands of co-active excitatory(E)-to-E, E-to-inhibitory(I), I-to-E and I-to-I Hebbian rules that elicit learning in large recurren