11 citations · 31 across the 14 of their papers we have counts for
7 papers · 1 filter
Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions
Sanja Karilanova, Subhrakanti Dey, Ayça Özçelikkale
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 Özçelikkale
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 Özçelikkale
Spiking neural networks (SNNs) are biologically inspired, event-driven models suited for temporal data processing and energy-efficient neuromorphic computing. In SNNs, richer neuro…
Low-Bit Data Processing Using Multiple-Output Spiking Neurons with Non-linear Reset Feedback
Sanja Karilanova, Subhrakanti Dey, Ayça Özçelikkale
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 Özçelikkale
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…
Distributed Continual Learning with CoCoA in High-dimensional Linear Regression
Martin Hellkvist, Ayça Özçelikkale, Anders Ahlén
We consider estimation under scenarios where the signals of interest exhibit change of characteristics over time. In particular, we consider the continual learning problem where di…
Communication Trade-offs in Federated Learning of Spiking Neural Networks
Soumi Chaki, David Weinberg, Ayca Özcelikkale
Spiking Neural Networks (SNNs) are biologically inspired alternatives to conventional Artificial Neural Networks (ANNs). Despite promising preliminary results, the trade-offs in th…