M A Rol de Lama, J P Lozano, V Ortiz, F J Sánchez-Vázquez, J A Madrid
How to engage medical students in chronobiology: an example on autorhythmometry
Advances in physiology education
recurrent neural networks rnn ubiquitously used neuroscience capture neural dynamics behaviors living systems however comes complex cognitive tasks traditional methods training rnns fall short capturing crucial aspects animal behavior address challenge take inspiration commonly used though rarely appreciated approach experimental neuroscientists toolkit behavioral shaping solution leverages task compositionality models animals relevant learning experiences prior task taking target temporal wagering task previously studied rats designed pretraining curriculum simpler cognitive tasks prerequisites performing well pretraining tasks simplified versions temporal wagering task reflect relevant subcomputations show approach required rnns adopt similar strategies rats including longtimescale inference latent states conventional pretraining approaches fail capture mechanistically pretraining supports development key dynamical systems features needed implementing inference valuebased decision making overall approach addresses gap neural network model training incorporating inductive biases animals important modeling complex behaviors rely computational abilities acquired past experiences