An energy-efficient spiking neural network with continuous learning for self-adaptive brain-machine interface
Fuente:
arXiv
Saved in:
| Main Authors: | Biyan, Zhou, Basu, Arindam |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning
by: Jain, Isha, et al.
Published: (2024)
by: Jain, Isha, et al.
Published: (2024)
Reconsidering the energy efficiency of spiking neural networks
by: Yan, Zhanglu, et al.
Published: (2024)
by: Yan, Zhanglu, et al.
Published: (2024)
Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfaces
by: Zhou, Biyan, et al.
Published: (2023)
by: Zhou, Biyan, et al.
Published: (2023)
Architectural Exploration of Hybrid Neural Decoders for Neuromorphic Implantable BMI
by: Mohan, Vivek, et al.
Published: (2025)
by: Mohan, Vivek, et al.
Published: (2025)
Similarity-based context aware continual learning for spiking neural networks
by: Han, Bing, et al.
Published: (2024)
by: Han, Bing, et al.
Published: (2024)
Robust and continuous machine learning of usage habits to adapt digital interfaces to user needs
by: Petit, Eric, et al.
Published: (2025)
by: Petit, Eric, et al.
Published: (2025)
Three factor delay learning rules for spiking neural networks
by: Vassallo, Luke, et al.
Published: (2026)
by: Vassallo, Luke, et al.
Published: (2026)
Universal and efficient graph neural networks with dynamic attention for machine learning interatomic potentials
by: Bi, Shuyu, et al.
Published: (2026)
by: Bi, Shuyu, et al.
Published: (2026)
Bayesian continual learning and forgetting in neural networks
by: Bonnet, Djohan, et al.
Published: (2025)
by: Bonnet, Djohan, et al.
Published: (2025)
Nonlinear spiked covariance matrices and signal propagation in deep neural networks
by: Wang, Zhichao, et al.
Published: (2024)
by: Wang, Zhichao, et al.
Published: (2024)
Learning fast changing slow in spiking neural networks
by: Capone, Cristiano, et al.
Published: (2024)
by: Capone, Cristiano, et al.
Published: (2024)
On the power of graph neural networks and the role of the activation function
by: Khalife, Sammy, et al.
Published: (2023)
by: Khalife, Sammy, et al.
Published: (2023)
Differentiable architecture search with multi-dimensional attention for spiking neural networks
by: Man, Yilei, et al.
Published: (2024)
by: Man, Yilei, et al.
Published: (2024)
Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics
by: Mei, Jie, et al.
Published: (2025)
by: Mei, Jie, et al.
Published: (2025)
Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classification
by: Pabian, Mateusz, et al.
Published: (2024)
by: Pabian, Mateusz, et al.
Published: (2024)
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
by: Haas, Moritz, et al.
Published: (2023)
by: Haas, Moritz, et al.
Published: (2023)
Optimal feature rescaling in machine learning based on neural networks
by: Vitrò, Federico Maria, et al.
Published: (2024)
by: Vitrò, Federico Maria, et al.
Published: (2024)
Fractional-order spike-timing-dependent gradient descent for multi-layer spiking neural networks
by: Yang, Yi, et al.
Published: (2024)
by: Yang, Yi, et al.
Published: (2024)
Application-oriented automatic hyperparameter optimization for spiking neural network prototyping
by: Fra, Vittorio
Published: (2025)
by: Fra, Vittorio
Published: (2025)
Fast gradient-free activation maximization for neurons in spiking neural networks
by: Pospelov, Nikita, et al.
Published: (2023)
by: Pospelov, Nikita, et al.
Published: (2023)
Neuromorphic on-chip reservoir computing with spiking neural network architectures
by: Karki, Samip, et al.
Published: (2024)
by: Karki, Samip, et al.
Published: (2024)
Decoding finger velocity from cortical spike trains with recurrent spiking neural networks
by: Liu, Tengjun, et al.
Published: (2024)
by: Liu, Tengjun, et al.
Published: (2024)
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks
by: Lim, Dong-Young, et al.
Published: (2021)
by: Lim, Dong-Young, et al.
Published: (2021)
Principles of Lipschitz continuity in neural networks
by: Luo, Róisín
Published: (2026)
by: Luo, Róisín
Published: (2026)
Order parameters and phase transitions of continual learning in deep neural networks
by: Shan, Haozhe, et al.
Published: (2024)
by: Shan, Haozhe, et al.
Published: (2024)
Covariant spatio-temporal receptive fields for spiking neural networks
by: Pedersen, Jens Egholm, et al.
Published: (2024)
by: Pedersen, Jens Egholm, et al.
Published: (2024)
Certified machine learning: A posteriori error estimation for physics-informed neural networks
by: Hillebrecht, Birgit, et al.
Published: (2022)
by: Hillebrecht, Birgit, et al.
Published: (2022)
Reliable and efficient inverse analysis using physics-informed neural networks with normalized distance functions and adaptive weight tuning
by: Deguchi, Shota, et al.
Published: (2025)
by: Deguchi, Shota, et al.
Published: (2025)
Effects of structural properties of neural networks on machine learning performance
by: Arya, Yash, et al.
Published: (2025)
by: Arya, Yash, et al.
Published: (2025)
On the rates of convergence for learning with convolutional neural networks
by: Yang, Yunfei, et al.
Published: (2024)
by: Yang, Yunfei, et al.
Published: (2024)
Who cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventions
by: D'Amico, Bernardino, et al.
Published: (2025)
by: D'Amico, Bernardino, et al.
Published: (2025)
Causal pieces: analysing and improving spiking neural networks piece by piece
by: Dold, Dominik, et al.
Published: (2025)
by: Dold, Dominik, et al.
Published: (2025)
Structured adaptive and random spinners for fast machine learning computations
by: Bojarski, Mariusz, et al.
Published: (2016)
by: Bojarski, Mariusz, et al.
Published: (2016)
Interpolating neural network: A novel unification of machine learning and interpolation theory
by: Park, Chanwook, et al.
Published: (2024)
by: Park, Chanwook, et al.
Published: (2024)
Consistent machine learning for topology optimization with microstructure-dependent neural network material models
by: Vijayakumaran, Harikrishnan, et al.
Published: (2024)
by: Vijayakumaran, Harikrishnan, et al.
Published: (2024)
Stable neural networks and connections to continuous dynamical systems
by: Ehrhardt, Matthias J., et al.
Published: (2025)
by: Ehrhardt, Matthias J., et al.
Published: (2025)
Fixed-budget online adaptive learning for physics-informed neural networks. Towards parameterized problem inference
by: Nguyen, Thi Nguyen Khoa, et al.
Published: (2022)
by: Nguyen, Thi Nguyen Khoa, et al.
Published: (2022)
Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning
by: Chavlis, Spyridon, et al.
Published: (2024)
by: Chavlis, Spyridon, et al.
Published: (2024)
Loss shaping enhances exact gradient learning with Eventprop in spiking neural networks
by: Nowotny, Thomas, et al.
Published: (2022)
by: Nowotny, Thomas, et al.
Published: (2022)
Target noise: A pre-training based neural network initialization for efficient high resolution learning
by: Wang, Shaowen, et al.
Published: (2026)
by: Wang, Shaowen, et al.
Published: (2026)
Similar Items
-
Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning
by: Jain, Isha, et al.
Published: (2024) -
Reconsidering the energy efficiency of spiking neural networks
by: Yan, Zhanglu, et al.
Published: (2024) -
Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfaces
by: Zhou, Biyan, et al.
Published: (2023) -
Architectural Exploration of Hybrid Neural Decoders for Neuromorphic Implantable BMI
by: Mohan, Vivek, et al.
Published: (2025) -
Similarity-based context aware continual learning for spiking neural networks
by: Han, Bing, et al.
Published: (2024)