One-shot learning of paired associations by a reservoir computing model with Hebbian plasticity
M Ganesh Kumar,Cheston Tan,Camilo Libedinsky,Shih-Cheng Yen,Andrew Tan · COSYNE 2022
Fri, Mar 18, 2022
One-shot learning can be achieved by animals and algorithms, but how animals do it is poorly understood as most of the algorithms are not biologically plausible. Experiments studying one-shot learning in rodents have shown that after initial gradual learning of associations between cues and locations, new associations can be learned with just a single exposure to each new cue-location pair. Foster, Morris and Dayan (2000) developed a neuro-symbolic actor-critic and coordinate learning agent that exhibited one-shot learning to displaced single locations in an open field maze using dead reckonin