Paper trains SNNs for classification using first-to-spike decoding.
problem Training SNNs for classification under GLM model.
method Proposes first-to-spike decoding method for SNNs.
result Improves accuracy and efficiency of SNN classification.
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3 results for “first-to-spike”
SNNs as first-to-spike policies improve energy efficiency in RL control.
problem Reducing energy consumption in RL control systems.
method Policy gradient approach with GLM for spiking neurons.
result Online trained SNNs outperform ANN conversion in energy efficiency and control performance.
Study shows SNNs are vulnerable to adversarial attacks and proposes a robust training method.
problem Vulnerability of spiking neural networks to adversarial attacks.
method Examined rate and time encoding/decoding; proposed robust training mechanism.
result Proposed training method enhances SNN performance under white-box attacks.