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cs.ET2025
StochEP: Stochastic Equilibrium Propagation for Spiking Convergent Recurrent Neural Networks
Jiaqi Lin, Yi Jiang, Abhronil Sengupta
Spiking Neural Networks (SNNs) promise energy-efficient, sparse, biologically inspired computation. Training them with Backpropagation Through Time (BPTT) and surrogate gradients a…
cs.LG2025
Scalable Equilibrium Propagation via Intermediate Error Signals for Deep Convolutional CRNNs
Jiaqi Lin, Malyaban Bal, Abhronil Sengupta
Equilibrium Propagation (EP) is a biologically inspired local learning rule first proposed for convergent recurrent neural networks (CRNNs), in which synaptic updates depend only o…
cs.LG2025
On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learning
Jiaqi Lin, Abhronil Sengupta
Recent research has shown the vulnerability of Spiking Neural Networks (SNNs) under adversarial examples that are nearly indistinguishable from clean data in the context of frame-b…