Adaptive spatiotemporal neural networks through complementary hybridization.

Area of Science:

• Machine Intelligence
• Neuromorphic Computing
• Artificial Neural Networks

Background:

• Spatiotemporal data processing requires advanced models due to high spatial dimensions and temporal richness.
• Recurrent Neural Networks (RNNs) and Spiking Neural Networks (SNNs) offer distinct approaches (extrinsic vs. intrinsic dynamics) but have disparate paradigms.
• A unified framework for adaptive spatiotemporal data processing across diverse requirements is challenging.

Purpose of the Study:

• To propose a novel hybrid spatiotemporal neural network combining RNNs and SNNs.
• To create a unified modeling framework for adaptive processing of variable spatiotemporal data.
• To improve performance metrics like accuracy, robustness, and efficiency.

Main Methods:

• Developed hybrid spatiotemporal neural networks by integrating RNNs and SNNs.
• Employed a unified surrogate gradient learning framework.
• Utilized a Hessian-aware neuron selection method to balance neuron types.

Main Results:

• The hybrid model demonstrated superior adaptive ability by tuning the ratio of RNN and SNN neurons.
• Achieved improved performance across accuracy, robustness, and efficiency metrics on benchmarks.
• Outperformed conventional single-paradigm RNNs and SNNs.
• Showcased potential in a robotic task within varying environments.

Conclusions:

• The proposed hybrid spatiotemporal neural network offers a generic and effective route for processing diverse spatiotemporal data.
• This unified framework provides enhanced adaptive capabilities for real-world applications.
• The approach paves the way for more versatile and high-performing AI systems.