Teaching Signal Synchronization in Deep Neural Networks with Prospective Neurons.
Publisher DOI
PubMed ID
42601032
Abstract
Working memory requires the brain to maintain information from the recent past to guide ongoing behavior. Neurons can contribute to this capacity by slowly integrating their inputs over time, creating persistent activity that outlasts the original stimulus. However, when these slowly integrating neurons are organized hierarchically, they introduce cumulative delays that create a fundamental challenge for learning: teaching signals that indicate whether behavior was correct or incorrect arrive out-of-sync with the neural activity they are meant to instruct. Here, we demonstrate that neurons enhanced with an adaptive current can compensate for these delays by responding to external stimuli prospectively, effectively predicting future inputs to synchronize with them. First, we show that such prospective neurons enable teaching signal synchronization across a range of learning algorithms that propagate error signals through hierarchical networks. Second, we demonstrate that this successfully guides learning in slowly integrating neurons, enabling the formation and retrieval of memories over extended timescales. We support our findings with a mathematical analysis of the prospective coding mechanism and learning experiments on motor control tasks. Together, our results reveal how neural adaptation could solve a critical timing problem and enable efficient learning in dynamic environments.
Date Issued
2026-08-17
Publication Type
Article
Subject(s)
Language(s)
en
Author(s)
Zucchet, Nicolas | |
Feng, Qianqian | |
Laborieux, Axel | |
Zenke, Friedemann | |
Sacramento, João |
Journal
Neural Computation
Publisher
Massachusetts Institute of Technology Press
ISSN
1530-888X
0899-7667
Access(Rights)
metadata.only