Sigma-Delta Neural Network Conversion on Loihi 2
Neuromorphic computing aims to improve the efficiency of artificial neural networks by taking inspiration from biological neurons and leveraging temporal sparsity, spatial sparsity, and compute near/in memory. Although these approaches have shown efficiency gains, training these spiking neural networks (SNN) remains difficult.
Brehove, Matthew +4 more
openaire +2 more sources
Biologically inspired neuromorphic-XAI synergy for transparent and low-carbon healthcare intelligence. [PDF]
Sungheetha A +5 more
europepmc +1 more source
Towards the neuromorphic Cyber-Twin: an architecture for cognitive defense in digital twin ecosystems. [PDF]
Nasir N, Al Hamadi H.
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Spike-based Q-learning in a non-von Neumann architecture. [PDF]
Shin D +13 more
europepmc +1 more source
Can neuromorphic computing help reduce AI's high energy cost? [PDF]
Ornes S.
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Bridging neuromorphic computing and deep learning for next-generation neural data interpretation. [PDF]
Zhang M, Wang T, Zhu Z.
europepmc +1 more source
Learning and inference with correlated neural variability. [PDF]
Qi Y +7 more
europepmc +1 more source
Editorial: Physical neuromorphic computing and its industrial applications. [PDF]
Yamane T, Hirose A, Offrein BJ.
europepmc +1 more source
Portfolio Optimization: A Neurodynamic Approach Based on Spiking Neural Networks. [PDF]
Khan AH, Mohammed AM, Li S.
europepmc +1 more source
In situ perturbation experiments: natural venting sites, spatial/temporal gradients in ocean pH, manipulative in situ pCO2 perturbations [PDF]
Barry, JP, Hall-Spencer, JM, Tyrell, T
core +1 more source

