arXiv:2602.18131v2 Announce Type: replace Abstract: Temporal Predictive Coding provides a layer-local, parallelisable mechanism for learning in recurrent systems, making it an attractive candidate for online local learning on neuromorphic and edge hardware. However, its recurrent parameter update captures only local temporal relationships, neglecting the historic influence of parameters along the latent-state trajectory, and therefore struggles to assign credit over longer temporal horizons. This work combines for the first time Temporal Predictive Coding with Real-Time Recurrent Learning (tPC
The continuous push for more efficient and generalizable AI on edge hardware makes improvements in recurrent learning mechanisms highly relevant.
Improving the ability of AI to learn long-range temporal dependencies locally and in parallel could significantly enhance performance and efficiency in real-world applications, especially on resource-constrained devices.
This research provides a pathway for recurrent AI systems, particularly on neuromorphic and edge hardware, to better understand and utilize historical data for more complex tasks.
- · Neuromorphic hardware manufacturers
- · Edge AI developers
- · Robotics
- · AI agents
- · Traditional high-compute reliance for temporal learning
More sophisticated and less compute-intensive AI models can be deployed on edge devices.
This could accelerate the development and adoption of AI agents and autonomous systems with improved real-time decision-making capabilities.
Enhanced on-device AI capabilities may reduce the need for constant cloud connectivity, changing fundamental infrastructure requirements for many applications.
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Read at arXiv cs.LG