Monkey Prefrontal Cortex Learns to Minimize Sequence Prediction Error
Huzi Cheng,
Matthew V. Chafee,
Rachael K. Blackman
et al.
Abstract:In this study, we develop a novel recurrent neural network (RNN) model of prefrontal cortex that predicts sensory inputs, actions, and outcomes at the next time step. Synaptic weights in the model are adjusted to minimize sequence prediction error, adapting a deep learning rule similar to those of large language models. The model, called Sequence Prediction Error Learning (SPEL), is a simple RNN that predicts world state at the next time step, but that differs from standard RNNs by using its own prediction err… Show more
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