6 papers
Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions
Sanja Karilanova, Subhrakanti Dey, Ayça Ãzçelikkale
Spiking neural networks (SNNs) are biologically inspired energy-efficient models that use sparse binary spike-based communication between neurons, making them attractive for resour…
Time-Varying Deep State Space Models for Sequences with Switching Dynamics
Sanja Karilanova, Subhrakanti Dey, Ayça Ãzçelikkale
The identification and modeling of time-varying systems is a fundamental challenge in signal processing and system identification. To address this challenge, we propose a class of…
Delays in Spiking Neural Networks: A State Space Model Approach
Sanja Karilanova, Subhrakanti Dey, Ayça Ãzçelikkale +1
Spiking neural networks (SNNs) are biologically inspired, event-driven models suited for temporal data processing and energy-efficient neuromorphic computing. In SNNs, richer neuro…
Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networks
Sanja Karilanova, Maxime Fabre, Emre Neftci +1
Spiking Neural Networks (SNNs) are biologically-inspired deep neural networks that efficiently extract temporal information while offering promising gains in terms of energy effici…
Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback
Sanja Karilanova, Subhrakanti Dey, Ayça Ãzçelikkale
Neuromorphic computing is an emerging technology enabling low-latency and energy-efficient signal processing. A key algorithmic tool in neuromorphic computing is spiking neural net…
State-Space Model Inspired Multiple-Input Multiple-Output Spiking Neurons
Sanja Karilanova, Subhrakanti Dey, Ayça Ãzçelikkale
In spiking neural networks (SNNs), the main unit of information processing is the neuron with an internal state. The internal state generates an output spike based on its component…