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2020 – today
- 2022
[c6]Diego Chavez Arana, Alpha Renner, Andrew T. Sornborger:
A Neuromorphic Normalization Algorithm for Stabilizing Synaptic Weights with Application to Dictionary Learning in LCA. NICE 2022: 58-60- [i8]Yen Ting Lin, Robert B. Lowrie, Denis Aslangil, Yigit Subasi, Andrew T. Sornborger:
Koopman von Neumann mechanics and the Koopman representation: A perspective on solving nonlinear dynamical systems with quantum computers. CoRR abs/2202.02188 (2022) - [i7]Matthias C. Caro, Hsin-Yuan Huang, Nicholas Ezzell, Joe Gibbs, Andrew T. Sornborger, Lukasz Cincio, Patrick J. Coles, Zoë Holmes:
Out-of-distribution generalization for learning quantum dynamics. CoRR abs/2204.10268 (2022) - [i6]Joe Gibbs, Zoë Holmes, Matthias C. Caro, Nicholas Ezzell, Hsin-Yuan Huang, Lukasz Cincio, Andrew T. Sornborger, Patrick J. Coles:
Dynamical simulation via quantum machine learning with provable generalization. CoRR abs/2204.10269 (2022) - 2021
- [c5]Ignácio Rubio Scola, Luis Rodolfo García Carrillo, Terrence C. Stewart, Andrew T. Sornborger:
A neuromorphic control architecture inspired by the limbic system. CDC 2021: 1413-1418 - [i5]Joe Gibbs, Kaitlin Gili, Zoë Holmes, Benjamin Commeau, Andrew Arrasmith, Lukasz Cincio, Patrick J. Coles, Andrew T. Sornborger:
Long-time simulations with high fidelity on quantum hardware. CoRR abs/2102.04313 (2021) - [i4]Alpha Renner, Forrest Sheldon, Anatoly Zlotnik, Louis Tao, Andrew T. Sornborger:
The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware. CoRR abs/2106.07030 (2021) - [i3]Matthias C. Caro, Hsin-Yuan Huang, Marco Cerezo, Kunal Sharma, Andrew T. Sornborger, Lukasz Cincio, Patrick J. Coles:
Generalization in quantum machine learning from few training data. CoRR abs/2111.05292 (2021) - 2020
- [c4]Alpha Renner
, Forrest Sheldon, Anatoly Zlotnik, Louis Tao, Andrew T. Sornborger:
Implementing Backpropagation for Learning on Neuromorphic Spiking Hardware. NICE 2020: 12:1-12:3 - [i2]Kunal Sharma, Marco Cerezo, Zoë Holmes, Lukasz Cincio, Andrew T. Sornborger, Patrick J. Coles:
Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets. CoRR abs/2007.04900 (2020) - [i1]Arthur Pesah, Marco Cerezo, Samson Wang, Tyler Volkoff, Andrew T. Sornborger, Patrick J. Coles:
Absence of Barren Plateaus in Quantum Convolutional Neural Networks. CoRR abs/2011.02966 (2020)
2010 – 2019
- 2019
- [j7]Yuxiu Shao, Binxu Wang, Andrew T. Sornborger, Louis Tao:
A Mechanism for Synaptic Copy Between Neural Circuits. Neural Comput. 31(10): 1964-1984 (2019) - 2018
- [j6]Zhuocheng Xiao, Binxu Wang, Andrew T. Sornborger, Louis Tao:
Mutual Information and Information Gating in Synfire Chains. Entropy 20(2): 102 (2018) - [j5]Andrew T. Sornborger, Phillip Stancil, Michael R. Geller:
Toward prethreshold gate-based quantum simulation of chemical dynamics: using potential energy surfaces to simulate few-channel molecular collisions. Quantum Inf. Process. 17(5): 106 (2018) - 2016
- [j4]Zhuo Wang, Andrew T. Sornborger, Louis Tao:
Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains. PLoS Comput. Biol. 12(6) (2016) - [c3]Yuxiu Shao, Andrew T. Sornborger, Louis Tao:
A pulse-gated, predictive neural circuit. ACSSC 2016: 1051-1055 - [c2]Andrew T. Sornborger, James D. Lauderdale:
A multitaper, causal decomposition for stochastic, multivariate time series: Application to high-frequency calcium imaging data. ACSSC 2016: 1056-1060 - 2015
- [j3]Andrew T. Sornborger, Zhuo Wang, Louis Tao:
A mechanism for graded, dynamically routable current propagation in pulse-gated synfire chains and implications for information coding. J. Comput. Neurosci. 39(2): 181-195 (2015) - 2012
- [j2]Louis Tao, Jeremy L. Praissman, Andrew T. Sornborger:
Improved dimensionally-reduced visual cortical network using stochastic noise modeling. J. Comput. Neurosci. 32(2): 367-376 (2012) - 2011
- [c1]Anne C. Smith, Christopher P. Fall, Andrew T. Sornborger:
Near-real-time connectivity estimation for multivariate neural data. EMBC 2011: 4721-4724 - 2010
- [j1]Louis Tao, Andrew T. Sornborger:
Dimensionally-reduced visual cortical network model predicts network response and connects system- and cellular-level descriptions. J. Comput. Neurosci. 28(1): 91-106 (2010)
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