Teaching signal synchronization in deep neural networks with prospective neurons

2025-11-18

AI Summary

In deep neural networks, hierarchical accumulation of integration delays in slow-integrating neurons causes severe temporal misalignment between teaching signals and the neural activity to be modulated, impeding efficient learning. To address this, we propose “proactive neurons”—a novel mechanism that introduces adaptive current dynamics to predict future inputs and actively compensate for propagation delays, thereby achieving precise temporal alignment between teaching signals and neural activity in multilayer slow-integration networks for the first time. Our approach integrates neurodynamical modeling, an enhanced temporal error backpropagation algorithm, and proactive coding within hierarchical recurrent architectures. Theoretical analysis rigorously establishes the synchronization efficacy of the mechanism. Experiments demonstrate substantial improvements in learning efficiency for motor control tasks and robust support for stable long-term memory formation and reliable memory retrieval.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Neuro-Symbolic Learning
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Teaching signals arrive out-of-sync with neural activity in hierarchical networks
• Slowly integrating neurons create cumulative delays during learning
• Neural adaptation enables synchronization of teaching signals with inputs

Innovation

• Prospective neurons predict future inputs for synchronization
• Adaptive current compensates delays in hierarchical networks
• Enables teaching signal synchronization across learning algorithms