Advancing spatio-temporal processing through adaptation in spiking neural networks.

Area of Science:

• Computational Neuroscience
• Neuromorphic Engineering
• Artificial Intelligence

Background:

• Spiking neural networks (SNNs) on neuromorphic hardware offer significant power savings.
• The leaky integrate-and-fire (LIF) neuron is standard for spike-based computation.
• Adaptive LIF neurons show promise for spatio-temporal processing but lack detailed understanding.

Purpose of the Study:

• To analyze the dynamical, computational, and learning properties of adaptive LIF neurons and networks.
• To understand the source of performance improvements in adaptive LIF neurons.
• To address challenges in stability and parameterization of adaptive LIF models.

Main Methods:

• Theoretical and empirical analysis of adaptive LIF neuron dynamics.
• Comparison of Euler-Forward and Symplectic Euler discretization methods.
• Evaluation on common event-based benchmark datasets.

Main Results:

• Conventional Euler-Forward discretization presents stability and parameterization challenges for adaptive LIF neurons.
• The Symplectic Euler method effectively resolves these challenges.
• Improved state-of-the-art performance on event-based benchmarks was achieved using Symplectic Euler.
• Adaptive LIF networks naturally exploit spatio-temporal input structures without normalization.

Conclusions:

• The Symplectic Euler method is crucial for stable and effective implementation of adaptive LIF neurons.
• Adaptive LIF neurons and networks offer a powerful approach for spatio-temporal data processing.
• This work provides a foundation for understanding and utilizing adaptive LIF neurons in neuromorphic systems.