[HTML][HTML] Photonic and optoelectronic neuromorphic computing
Recent advances in neuromorphic computing have established a computational framework
that removes the processor-memory bottleneck evident in traditional von Neumann …
that removes the processor-memory bottleneck evident in traditional von Neumann …
Theoretically provable spiking neural networks
Spiking neural networks have attracted increasing attention in recent years due to their
potential of handling time-dependent data. Many algorithms and techniques have been …
potential of handling time-dependent data. Many algorithms and techniques have been …
Scalable nanophotonic-electronic spiking neural networks
Spiking neural networks (SNN) provide a new computational paradigm capable of highly
parallelized, real-time processing. Photonic devices are ideal for the design of high …
parallelized, real-time processing. Photonic devices are ideal for the design of high …
Understanding spiking networks through convex optimization
A Mancoo, S Keemink… - Advances in neural …, 2020 - proceedings.neurips.cc
Neurons mainly communicate through spikes, and much effort has been spent to understand
how the dynamics of spiking neural networks (SNNs) relates to their connectivity …
how the dynamics of spiking neural networks (SNNs) relates to their connectivity …
A superconducting nanowire-based architecture for neuromorphic computing
AE Lombo, J Lares, M Castellani… - Neuromorphic …, 2022 - iopscience.iop.org
Neuromorphic computing would benefit from the utilization of improved customized
hardware. However, the translation of neuromorphic algorithms to hardware is not easily …
hardware. However, the translation of neuromorphic algorithms to hardware is not easily …
A generalized spiking locally competitive algorithm for multiple optimization problems
X Du, ZK Tian, S Li, D Zhou - arXiv preprint arXiv:2407.03930, 2024 - arxiv.org
We introduce a generalized Spiking Locally Competitive Algorithm (LCA) that is biologically
plausible and exhibits adaptability to a large variety of neuron models and network …
plausible and exhibits adaptability to a large variety of neuron models and network …
Random sketching, clustering, and short-term memory in spiking neural networks
We study input compression in a biologically inspired model of neural computation. We
demonstrate that a network consisting of a random projection step (implemented via random …
demonstrate that a network consisting of a random projection step (implemented via random …
Bifurcation spiking neural network
Spiking neural networks (SNNs) have attracted much attention due to their great potential for
modeling time-dependent signals. The performance of SNNs depends not only on picking …
modeling time-dependent signals. The performance of SNNs depends not only on picking …
Counting to ten with two fingers: Compressed counting with spiking neurons
Y Hitron, M Parter - arXiv preprint arXiv:1902.10369, 2019 - arxiv.org
We consider the task of measuring time with probabilistic threshold gates implemented by
bio-inspired spiking neurons. In the model of spiking neural networks, network evolves in …
bio-inspired spiking neurons. In the model of spiking neural networks, network evolves in …
Minimax dynamics of optimally balanced spiking networks of excitatory and inhibitory neurons
Q Li, C Pehlevan - Advances in neural information …, 2020 - proceedings.neurips.cc
Excitation-inhibition balance is ubiquitously observed in the cortex. Recent studies suggest
an intriguing link between balance on fast timescales, tight balance, and efficient information …
an intriguing link between balance on fast timescales, tight balance, and efficient information …