A Population Study of Integrate-and-Fire-or-Burst Neurons. - Alex Casti; Ahmet Omurtag; Andrew T. Sornborger; Ehud Kaplan; Bruce W. Knight; Jonathan D. Victor; L. Sirovich (Neural Comput., 2002)

Spatiotemporal analysis of optical imaging data. - Andrew T. Sornborger; C. Sailstad; Ehud Kaplan; L. Sirovich (NeuroImage, 2003)

Extraction of periodic multivariate signals: mapping of voltage-dependent dye fluorescence in the mouse heart. - Andrew T. Sornborger; Lawrence Sirovich; Gregory Morley (IEEE Trans. Medical Imaging, 2003)

Higher-order operator splitting methods for deterministic parabolic equations. - Andrew T. Sornborger (Int. J. Comput. Math., 2007)

Dimensionally-reduced visual cortical network model predicts network response and connects system- and cellular-level descriptions. - Louis Tao; Andrew T. Sornborger (J. Comput. Neurosci., 2010)

Near-real-time connectivity estimation for multivariate neural data. - Anne C. Smith; Christopher P. Fall; Andrew T. Sornborger (EMBC, 2011)

Improved dimensionally-reduced visual cortical network using stochastic noise modeling. - Louis Tao; Jeremy L. Praissman; Andrew T. Sornborger (J. Comput. Neurosci., 2012)

A mechanism for graded, dynamically routable current propagation in pulse-gated synfire chains and implications for information coding. - Andrew T. Sornborger; Zhuo Wang; Louis Tao (J. Comput. Neurosci., 2015)

A pulse-gated, predictive neural circuit. - Yuxiu Shao; Andrew T. Sornborger; Louis Tao (ACSSC, 2016)

A multitaper, causal decomposition for stochastic, multivariate time series: Application to high-frequency calcium imaging data. - Andrew T. Sornborger; James D. Lauderdale (ACSSC, 2016)

Graded, Dynamically Routable Information Processing with Synfire-Gated Synfire Chains. - Zhuo Wang; Andrew T. Sornborger; Louis Tao (PLoS Comput. Biol., 2016)

Mutual Information and Information Gating in Synfire Chains. - Zhuocheng Xiao; Binxu Wang; Andrew T. Sornborger; Louis Tao (Entropy, 2018)

Toward prethreshold gate-based quantum simulation of chemical dynamics: using potential energy surfaces to simulate few-channel molecular collisions. - Andrew T. Sornborger; Phillip Stancil; Michael R. Geller (Quantum Inf. Process., 2018)

Unsupervised Dictionary Learning via a Spiking Locally Competitive Algorithm. - Yijing Watkins; Austin Thresher; Peter F. Schultz; Andreas Wild; Andrew Sornborger; Garrett T. Kenyon (ICONS, 2019)

A Mechanism for Synaptic Copy Between Neural Circuits. - Yuxiu Shao; Binxu Wang; Andrew T. Sornborger; Louis Tao (Neural Comput., 2019)

Quantum-assisted quantum compiling. - Sumeet Khatri; Ryan LaRose; Alexander Poremba; Lukasz Cincio; Andrew T. Sornborger; Patrick J. Coles (Quantum, 2019)

Using Sinusoidally-Modulated Noise as a Surrogate for Slow-Wave Sleep to Accomplish Stable Unsupervised Dictionary Learning in a Spike-Based Sparse Coding Model. - Yijing Watkins; Edward Kim 006; Andrew Sornborger; Garrett T. Kenyon (CVPR Workshops, 2020)

Implementing Backpropagation for Learning on Neuromorphic Spiking Hardware. - Alpha Renner; Forrest Sheldon; Anatoly Zlotnik; Louis Tao; Andrew T. Sornborger (NICE, 2020)

Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets. - Kunal Sharma; Marco Cerezo; Zoë Holmes; Lukasz Cincio; Andrew T. Sornborger; Patrick J. Coles (CoRR, 2020)

Absence of Barren Plateaus in Quantum Convolutional Neural Networks. - Arthur Pesah; Marco Cerezo; Samson Wang; Tyler J. Volkoff; Andrew T. Sornborger; Patrick J. Coles (CoRR, 2020)

A neuromorphic control architecture inspired by the limbic system. - Ignácio Rubio Scola; Luis Rodolfo García Carrillo; Terrence C. Stewart; Andrew T. Sornborger (CDC, 2021)

Long-time simulations with high fidelity on quantum hardware. - Joe Gibbs; Kaitlin Gili; Zoë Holmes; Benjamin Commeau; Andrew Arrasmith; Lukasz Cincio; Patrick J. Coles; Andrew T. Sornborger (CoRR, 2021)

The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware. - Alpha Renner; Forrest Sheldon; Anatoly Zlotnik; Louis Tao; Andrew T. Sornborger (CoRR, 2021)

Machine Learning Changes the Rules for Flux Limiters. - Nga Nguyen-Fotiadis; Michael M. McKerns; Andrew Sornborger (CoRR, 2021)

Generalization in quantum machine learning from few training data. - Matthias C. Caro; Hsin-Yuan Huang; Marco Cerezo; Kunal Sharma; Andrew T. Sornborger; Lukasz Cincio; Patrick J. Coles (CoRR, 2021)

A Neuromorphic Normalization Algorithm for Stabilizing Synaptic Weights with Application to Dictionary Learning in LCA. - Diego Chavez Arana; Alpha Renner; Andrew T. Sornborger (NICE, 2022)

Koopman von Neumann mechanics and the Koopman representation: A perspective on solving nonlinear dynamical systems with quantum computers. - Yen Ting Lin; Robert B. Lowrie; Denis Aslangil; Yigit Subasi; Andrew T. Sornborger (CoRR, 2022)

Out-of-distribution generalization for learning quantum dynamics. - Matthias C. Caro; Hsin-Yuan Huang; Nicholas Ezzell; Joe Gibbs; Andrew T. Sornborger; Lukasz Cincio; Patrick J. Coles; Zoë Holmes (CoRR, 2022)

Dynamical simulation via quantum machine learning with provable generalization. - Joe Gibbs; Zoë Holmes; Matthias C. Caro; Nicholas Ezzell; Hsin-Yuan Huang; Lukasz Cincio; Andrew T. Sornborger; Patrick J. Coles (CoRR, 2022)

Inference-Based Quantum Sensing. - Cinthia Huerta Alderete; Max Hunter Gordon; Frederic Sauvage; Akira Sone; Andrew T. Sornborger; Patrick J. Coles; Marco Cerezo (CoRR, 2022)

Biologically plausible robust control with Neural Network weight reset for unmanned aircraft systems under impulsive disturbances. - Ignácio Rubio Scola; Luis Rodolfo García Carrillo; Andrew T. Sornborger; João P. Hespanha 001 (ACC, 2023)

Spiking Neural Network-based Control Applied to an Underactuated System. - Abel García-Barrientos; Diego Chavez Arana; Eduardo Steed Espinoza; Luis Rodolfo García Carrillo; António Osório-Cordero; Andrew T. Sornborger (CCE, 2023)

Spiking LCA in a Neural Circuit with Dictionary Learning and Synaptic Normalization. - Diego Chavez Arana; Alpha Renner; Andrew T. Sornborger (NICE, 2023)

Efficient quantum linear solver algorithm with detailed running costs. - David Jennings; Matteo Lostaglio; Sam Pallister; Andrew T. Sornborger; Yigit Subasi (CoRR, 2023)

Binary operations on neuromorphic hardware with application to linear algebraic operations and stochastic equations. - Oleksandr Iaroshenko; Andrew T. Sornborger; Diego Chavez Arana (Neuromorph. Comput. Eng., 2023)

An Interface-Type Memristive Device for Artificial Synapse and Neuromorphic Computing. - Sundar Kunwar; Zachary Jernigan; Zach Hughes; Chase Somodi; Michael Saccone; Francesco Caravelli; Pinku Roy; Di Zhang; Haiyan Wang; Quanxi Jia; Judith L. MacManus-Driscoll; Garrett T. Kenyon; et al. (Adv. Intell. Syst., 2023)

An Interface-Type Memristive Device for Artificial Synapse and Neuromorphic Computing. - Sundar Kunwar; Zachary Jernigan; Zach Hughes; Chase Somodi; Michael D. Saccone; Francesco Caravelli; Pinku Roy; Di Zhang; Haiyan Wang; Quanxi Jia; Judith L. MacManus-Driscoll; Garrett T. Kenyon; et al. (Adv. Intell. Syst., 2023)

Spiking Neural Network-Based Control of an Unmanned Aerial System Implemented on a Customized Neural Flight Simulation Environment. - Omar A. García A.; Diego Chavez Arana; Eduardo Steed Espinoza; Ignácio Rubio Scola; Luis Rodolfo García Carrillo; Andrew T. Sornborger (ACC, 2024)

Real-Time Implementation of a Spiking Neural Network-Based Control: An Application for the Ball and Plate System. - Diego Chavez Arana; Omar A. García A.; Ignácio Rubio Scola; Eduardo Steed Espinoza; Luis Rodolfo García Carrillo; Andrew Sornborger (ACC, 2024)

Spiking Neural Network-based Flight Controller. - Diego Chavez Arana; Omar A. García A.; Luis Rodolfo García Carrillo; Ignácio Rubio Scola; Eduardo Steed Espinoza; Andrew T. Sornborger (NICE, 2024)

Probabilistic Flux Limiters. - Nga T. T. Nguyen-Fotiadis; Robert Chiodi; Michael M. McKerns; Daniel Livescu; Andrew Sornborger (CoRR, 2024)

Gate-based quantum simulation of Gaussian bosonic circuits on exponentially many modes. - Alice Barthe; Marco Cerezo; Andrew T. Sornborger; Martin Larocca; Diego García-Martín (CoRR, 2024)

Neuromorphic on-chip reservoir computing with spiking neural network architectures. - Samip Karki; Diego Chavez Arana; Andrew T. Sornborger; Francesco Caravelli (CoRR, 2024)

The cost of solving linear differential equations on a quantum computer: fast-forwarding to explicit resource counts. - David Jennings; Matteo Lostaglio; Robert B. Lowrie; Sam Pallister; Andrew T. Sornborger (Quantum, 2024)

CaliQEC: In-situ Qubit Calibration for Surface Code Quantum Error Correction. - Xiang Fang; Keyi Yin; Yuchen Zhu; Jixuan Ruan; Dean Tullsen; Zhiding Liang; Andrew Sornborger; Ang Li 006; Travis S. Humble; Yufei Ding 001; Yunong Shi (ISCA, 2025)

Improving the efficiency of learning-based error mitigation. - Piotr Czarnik; Michael M. McKerns; Andrew T. Sornborger; Lukasz Cincio (Quantum, 2025)