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Toward a Free-Response Paradigm of Decision Making in Spiking Neural Networks
Zhichao Zhu1,2, Yang Qi3,4,5, Wenlian Lu6,7,8,9,10
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China.
Neural Computation
|January 9, 2025
Summary
This study introduces a new theory for spiking neural networks (SNNs) that improves decision-making speed and accuracy by training SNNs to express confidence. This approach enhances energy efficiency and real-time response for complex tasks.
Area of Science:
- Computational Neuroscience
- Artificial Intelligence
- Machine Learning
Background:
- Spiking neural networks (SNNs) offer energy efficiency but face challenges in real-time inference due to multi-step processing.
- Existing artificial neural networks lack the nuanced decision-making speed-accuracy trade-offs observed in biological systems.
Purpose of the Study:
- To develop a theory for decision making in SNNs that incorporates attributes like reaction time and decision confidence.
- To enhance the speed, accuracy, and energy efficiency of SNNs for complex decision-making tasks.
Main Methods:
- Introduced a novel learning objective to train SNNs for both correct decisions and confidence shaping.
- Developed a stopping policy to optimize inference time and use stopping time as a correctness indicator.
- Untangled the interplay between signal and noise in SNN decision processes.
Main Results:
- SNNs trained with the new objective demonstrated improved confidence expression and reduced trial-to-trial variability.
- The proposed methods led to shorter latency in reaching desired accuracy levels.
- The stopping policy further enhanced the time efficiency of SNN inference.
Conclusions:
- Integrating stochasticity and confidence shaping into SNNs unlocks new capabilities for complex decision-making.
- This research advances SNNs towards more biologically plausible and efficient information processing.
- The findings pave the way for improved SNN applications in real-time, resource-constrained environments.
