4 papers
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 to bridge the gap between the detailed mapping of microscale neural circuits and mechanistic understanding of cognitive functions. While…
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…
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…