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Updated: Apr 19, 2026
11:18
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
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Randomized forward mode gradient for spiking neural networks in scientific machine learning.
Ruyin Wan1, Qian Zhang2, George Em Karniadakis2
1School of Engineering, Brown University, Providence, 02912, RI, USA.
Summary
This study introduces a novel, biologically plausible training method for spiking neural networks (SNNs) using weight perturbation. This approach offers an efficient alternative to back-propagation for machine learning and neuromorphic hardware.
Area of Science:
- Artificial Intelligence
- Computational Neuroscience
- Machine Learning
Background:
- Spiking neural networks (SNNs) offer energy-efficient computation with deep learning capabilities.
- Traditional back-propagation training for SNNs faces challenges in cost, biological plausibility, and hardware efficiency.
Purpose of the Study:
- To develop an alternative, more efficient training method for SNNs.
- To address the limitations of back-propagation in SNN training.
Main Methods:
- Implemented a weight perturbation technique within a forward-mode gradient framework.
- Estimated gradients by analyzing network output changes after perturbing weight matrices with noise.
Main Results:
- Achieved competitive accuracy on regression tasks, including solving partial differential equations (PDEs).
- Demonstrated the effectiveness of the weight perturbation method as an alternative to back-propagation.
Conclusions:
- The proposed training approach is suitable for SNNs and neuromorphic systems.
- This method shows potential for hardware compatibility and efficient SNN implementation.
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