An animals’ ability to learn how to make decisions based on sensory evidence is often well described by Reinforcement Learning (RL) frameworks. These frameworks, however, typically apply to event-based representations and lack the explicit and fine-grained notion of time needed to study psychophysically relevant measures like reaction times and psychometric curves. Here, we develop and use a biologically plausible continuous-time RL scheme of CT-AuGMEnT (Continuous-Time Attention-Gated MEmory Tagging) to study these behavioural quantities. We show how CT-AuGMEnT implements on-policy SARSA learning as a biologically plausible form of reinforcement learning with working memory units using ‘attentional’ feedback. We show that the CT-AuGMEnT model efficiently learns tasks in continuous time and can learn to accumulate relevant evidence through time. This allows the model to link task difficulty to psychophysical measurements such as accuracy and reaction-times. We further show how the implementation of a separate accessory network for feedback allows the model to learn continuously, also in case of significant transmission delays between the network's feedforward and feedback layers and even when the accessory network is randomly initialized. Our results demonstrate that CT-AuGMEnT represents a fully time-continuous biologically plausible end-to-end RL model for learning to integrate evidence and make decisions.
Bibliographical noteFunding Information:
Prof. Pieter Roelfsema (M) is director of the Netherlands Institute for Neuroscience and he also heads the lab “Vision & Cognition” at this institute. Additionally, he is a part-time professor at the University of Amsterdam and also at the Free University Amsterdam. He investigates how neurons in different brain areas work together during visual cognition and he proposed the influential theory that the processing of visual stimuli occurs in different phases with different contributions of feedforward and feedback connections. Roelfsema has received many awards including the NWO VICI award and the EU ERC advanced grant.
DZ is supported by NWO (Ideas grant 656.000.005, “DEVIS”), and PRR was supported by NWO (Ideas grant 656–000-002 “REASON”), and the EuropeanUnion (grant agreement 7202070 “Human Brain Project”, and ERC grant agreement 339490 “Cortic_al_gorithms”).
© 2021 The Author(s)
Copyright 2021 Elsevier B.V., All rights reserved.
- Continuous-time SARSA
- Neural networks
- Reinforcement learning
- Selective attention
- Working memory