EDITORIAL
Published on 17 Mar 2021
Editorial: Understanding and Bridging the Gap Between Neuromorphic Computing and Machine Learning
doi 10.3389/fncom.2021.665662
- 6,589 views
- 13 citations
- Frontiers in Computational Neuroscience
- Research Topics
- Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning
Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning
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Articles
ORIGINAL RESEARCH
Published on 02 Feb 2021
End-to-End Implementation of Various Hybrid Neural Networks on a Cross-Paradigm Neuromorphic Chip
doi 10.3389/fnins.2021.615279
- 3,902 views
- 10 citations
ORIGINAL RESEARCH
Published on 23 Dec 2020
Bitstream-Based Neural Network for Scalable, Efficient, and Accurate Deep Learning Hardware
doi 10.3389/fnins.2020.543472
- 3,148 views
- 5 citations
ORIGINAL RESEARCH
Published on 18 Sep 2020
Exploring Neuromodulation for Dynamic Learning
doi 10.3389/fnins.2020.00928
- 4,632 views
- 9 citations
ORIGINAL RESEARCH
Published on 21 Jul 2020
Bayesian Multi-objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neural Network Accelerator Design
doi 10.3389/fnins.2020.00667
- 7,336 views
- 43 citations
ORIGINAL RESEARCH
Published on 30 Jun 2020
Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization
doi 10.3389/fnins.2020.00653
- 6,390 views
- 83 citations
ORIGINAL RESEARCH
Published on 24 Jun 2020
Supervised Learning in All FeFET-Based Spiking Neural Network: Opportunities and Challenges
doi 10.3389/fnins.2020.00634
- 10,647 views
- 83 citations
ORIGINAL RESEARCH
Published on 28 May 2020
Neuromorphic Systems Design by Matching Inductive Biases to Hardware Constraints
doi 10.3389/fnins.2020.00437
- 2,445 views
ORIGINAL RESEARCH
Published on 12 May 2020
Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)
doi 10.3389/fnins.2020.00424
- 16,051 views
- 227 citations
ORIGINAL RESEARCH
Published on 05 May 2020
Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks
doi 10.3389/fnins.2020.00439
- 8,100 views
- 79 citations
HYPOTHESIS AND THEORY
Published on 29 Apr 2020
Noise Helps Optimization Escape From Saddle Points in the Synaptic Plasticity
doi 10.3389/fnins.2020.00343
- 3,546 views
- 1 citation
CORRECTION
Published on 15 Apr 2020
Corrigendum: A Curiosity-Based Learning Method for Spiking Neural Networks
doi 10.3389/fncom.2020.00028
- 1,756 views
- 1 citation
Published in
Frontiers in Computational Neuroscience
Participating Journals
Frontiers in Neuroscience
- 3.2 impact factor
- 6.2 citescore
Impact
- 131kTopic views
- 97kArticle views
- 26kArticle downloads