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Found 2 papers, 1 papers with code
Perturbation-based Learning for Recurrent Neural Networks
To this end, we extend the recently introduced activity-based node perturbation (ANP) method to operate in the time domain, leading to more efficient learning and generalization.
Understanding spiking networks through convex optimization
Here we turn these findings around and show that virtually all inhibition-dominated SNNs can be understood through the lens of convex optimization, with network connectivity, timescales, and firing thresholds being intricately linked to the parameters of underlying convex optimization problems.
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