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Found 2 papers, 2 papers with code
A nonlinear hidden layer enables actor-critic agents to learn multiple paired association navigation
Biologically plausible classic actor-critic agents have been shown to learn to navigate to single reward locations, but which biologically plausible agents are able to learn multiple cue-reward location tasks has remained unclear.
One-shot learning of paired association navigation with biologically plausible schemas
But how schemas, conceptualized at Marr's computational level, correspond with neural implementations remains poorly understood, and a biologically plausible computational model of the rodent learning has not been demonstrated.
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