116 citations · 198 across the 10 of their papers we have counts for
11 papers
Compute-in-Memory Implementation of State Space Models for Event Sequence Processing
Xiaoyu Zhang, Mingtao Hu, Sen Lu +4
State space models (SSMs) have recently emerged as a powerful framework for long sequence processing, outperforming traditional methods on diverse benchmarks. Fundamentally, SSMs c…
Neuromorphic Cybersecurity with Semi-supervised Lifelong Learning
Md Zesun Ahmed Mia, Malyaban Bal, Sen Lu +4
Inspired by the brain's hierarchical processing and energy efficiency, this paper presents a Spiking Neural Network (SNN) architecture for lifelong Network Intrusion Detection Syst…
State Space Models Naturally Produce Time Cell and Oscillatory Behaviors and Scale to Abstract Cognitive Functions
Sen Lu, Xiaoyu Zhang, Mingtao Hu +3
A grand challenge in modern neuroscience is bridging the gap between the microscale neural circuits and cognitive functions. While extensive knowledge exists about neuronal connect…
Stochastic Spiking Neural Networks with First-to-Spike Coding
Yi Jiang, Sen Lu, Abhronil Sengupta
Spiking Neural Networks (SNNs), recognized as the third generation of neural networks, are known for their bio-plausibility and energy efficiency, especially when implemented on ne…
Benchmarking Spiking Neural Network Learning Methods with Varying Locality
Jiaqi Lin, Sen Lu, Malyaban Bal +1
Spiking Neural Networks (SNNs), providing more realistic neuronal dynamics, have been shown to achieve performance comparable to Artificial Neural Networks (ANNs) in several machin…
Deep Unsupervised Learning Using Spike-Timing-Dependent Plasticity
Sen Lu, Abhronil Sengupta
Spike-Timing-Dependent Plasticity (STDP) is an unsupervised learning mechanism for Spiking Neural Networks (SNNs) that has received significant attention from the neuromorphic hard…