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ORCIDICONS 2020: Oak Ridge, Tennessee, USA
- Thomas E. Potok, Catherine D. Schuman:
Proceedings of the International Conference on Neuromorphic Systems, ICONS 2020, Oak Ridge, Tennessee, USA, July, 2020. ACM 2020, ISBN 978-1-4503-8851-1 - Bojian Yin
, Federico Corradi, Sander M. Bohté:
Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks. 1:1-1:8 - Angel Yanguas-Gil:
Coarse scale representation of spiking neural networks: backpropagation through spikes and application to neuromorphic hardware. 2:1-2:7 - Ali Lotfi-Rezaabad, Sriram Vishwanath:
Long Short-Term Memory Spiking Networks and Their Applications. 3:1-3:9 - Daniel Elbrecht, Catherine D. Schuman:
Neuroevolution of Spiking Neural Networks Using Compositional Pattern Producing Networks. 4:1-4:5 - Michael Hampo, David Fan, Todd Jenkins, Ashley DeMange, Stefan Westberg, Trevor J. Bihl, Tarek M. Taha:
Associative Memory in Spiking Neural Network Form Implemented on Neuromorphic Hardware. 5:1-5:8 - Jimmy Gammell, Sae Woo Nam, Adam N. McCaughan:
Layer-skipping connections facilitate training of layered networks using equilibrium propagation. 6:1-6:4 - Ruthvik Vaila, John N. Chiasson, Vishal Saxena:
Continuous Learning in a Single-Incremental-Task Scenario with Spike Features. 7:1-7:4 - Andrew Fountain, Cory E. Merkel:
Energy Constraints Improve Liquid State Machine Performance. 8:1-8:8 - Yutong Gao, Shang Wu, Gina C. Adam:
Batch Training for Neuromorphic Systems with Device Non-idealities. 9:1-9:4 - William Severa, Ryan Dellana, Craig M. Vineyard:
Effective Pruning of Binary Activation Neural Networks. 10:1-10:5 - Clemens J. S. Schaefer, Siddharth Joshi:
Quantizing Spiking Neural Networks with Integers. 11:1-11:8 - Byungik Ahn:
Implementation of a 12-Million Hodgkin-Huxley Neuron Network on a Single Chip. 12:1-12:8 - Janak Sharda, Nilabjo Dey, Ankesh Jain, Debanjan Bhowmik:
Reduction of the Weight-Decay Rate of Volatile Memory Synapses in an Analog Hardware Neural Network for Accurate and Scalable On-Chip Learning. 13:1-13:8 - Shikhar Tuli, Debanjan Bhowmik:
Design of a Conventional-Transistor-Based Analog Integrated Circuit for On-Chip Learning in a Spiking Neural Network. 14:1-14:8 - Zhehui Wang, Huaipeng Zhang, Tao Luo, Weng-Fai Wong, Anh-Tuan Do, Paramasivam Vishnu, Wei Zhang, Rick Siow Mong Goh:
NCPower: Power Modelling for NVM-based Neuromorphic Chip. 15:1-15:7 - J. Parker Mitchell, Catherine D. Schuman, Thomas E. Potok:
A Small, Low Cost Event-Driven Architecture for Spiking Neural Networks on FPGAs. 16:1-16:4 - Samiran Ganguly, Avik W. Ghosh:
Building Reservoir Computing Hardware Using Low Energy-Barrier Magnetics. 17:1-17:8 - Rajkumar Kubendran, Weier Wan, Siddharth Joshi, H.-S. Philip Wong, Gert Cauwenberghs:
A 1.52 pJ/Spike Reconfigurable Multimodal Integrate-and-Fire Neuron Array Transceiver. 18:1-18:4 - Rami A. Alzahrani, Alice C. Parker:
Neuromorphic Circuits With Neural Modulation Enhancing the Information Content of Neural Signaling. 19:1-19:8 - John Carter, Jocelyn Rego, Daniel Schwartz, Vikas Bhandawat, Edward Kim:
Learning Spiking Neural Network Models of Drosophila Olfaction. 20:1-20:5 - Frances S. Chance:
Interception from a Dragonfly Neural Network Model. 21:1-21:5 - Adithya Gurunathan, Laxmi R. Iyer:
Spurious learning in networks with Spike Driven Synaptic Plasticity. 22:1-22:8 - Ioannis Polykretis, Guangzhi Tang, Konstantinos P. Michmizos:
An Astrocyte-Modulated Neuromorphic Central Pattern Generator for Hexapod Robot Locomotion on Intel's Loihi. 23:1-23:9 - Neil Getty, Zixuan Zhao, Stephan Gruessner, Liaohai Chen, Fangfang Xia:
Recurrent and Spiking Modeling of Sparse Surgical Kinematics. 24:1-24:5 - Md. Shahanur Alam, Chris Yakopcic, Guru Subramanyam, Tarek M. Taha:
Memristor Based Neuromorphic Adaptive Resonance Theory for One-Shot Online Learning and Network Intrusion Detection. 25:1-25:8 - Kathleen E. Hamilton, Tiffany M. Mintz, Prasanna Date, Catherine D. Schuman:
Spike-based graph centrality measures. 26:1-26:8 - J. Darby Smith, William Severa, Aaron J. Hill, Leah Reeder, Brian Franke, Richard B. Lehoucq, Ojas D. Parekh, James B. Aimone:
Solving a steady-state PDE using spiking networks and neuromorphic hardware. 27:1-27:8 - Kathleen E. Hamilton, Prasanna Date, Bill Kay, Catherine D. Schuman:
Modeling epidemic spread with spike-based models. 28:1-28:5
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