RECOLLECT: A Biologically Plausible Memory Network for Learning-to-Learn
Author Information
Author(s): van den Berg Alexandra R., Roelfsema Pieter R., Bohte Sander M.
Primary Institution: Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
Hypothesis
Can a biologically plausible gated memory network effectively learn-to-learn and manage memory retention and forgetting?
Conclusion
RECOLLECT successfully learns to represent task-relevant information and can adaptively forget it, demonstrating effective learning-to-learn capabilities.
Supporting Evidence
- RECOLLECT can flexibly retain or forget information using a single memory gate.
- It successfully learns to represent task-relevant information over long memory delays.
- Networks trained with RECOLLECT exhibited learning-to-learn capabilities on a reversal bandit task.
- Performance improved significantly with the inclusion of an end-of-episode signal.
Takeaway
The RECOLLECT model is like a smart brain that can remember important things and forget unimportant ones, helping it learn better over time.
Methodology
The study involved training the RECOLLECT model on pro-/anti-saccade and reversal bandit tasks to evaluate its memory and learning capabilities.
Limitations
Learning was slower compared to previous models, and the architecture was tested with a single layer of memory units.
Statistical Information
P-Value
0.003
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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