Teaching signal synchronization in deep neural networks with prospective neurons

TL;DR

This paper introduces prospective neurons with adaptive currents that predict future inputs, solving timing issues in hierarchical neural networks and improving learning and memory formation over extended timescales.

Contribution

It demonstrates how adaptive, prospective neurons can synchronize teaching signals in hierarchical networks, enabling effective learning despite delays.

Findings

Prospective neurons enable synchronization of teaching signals across various learning algorithms.

Adaptive neurons improve memory formation and retrieval over long timescales.

Mathematical analysis supports the effectiveness of prospective coding in neural adaptation.

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.