| Default mode network connectivity in stable vs progressive mild cognitive impairment JR Petrella, FC Sheldon, SE Prince, VD Calhoun, PM Doraiswamy Neurology 76 (6), 511-517, 2011 | 361 | 2011 |
| Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers JL Shaffer, JR Petrella, FC Sheldon, KR Choudhury, VD Calhoun, ... Radiology 266 (2), 583-591, 2013 | 324* | 2013 |
| Evidence of Exponential Speed‐Up in the Solution of Hard Optimization Problems FL Traversa, P Cicotti, F Sheldon, M Di Ventra Complexity 2018 (1), 7982851, 2018 | 42 | 2018 |
| Taming a nonconvex landscape with dynamical long-range order: Memcomputing ising benchmarks F Sheldon, FL Traversa, M Di Ventra Physical Review E 100 (5), 053311, 2019 | 33* | 2019 |
| The backpropagation algorithm implemented on spiking neuromorphic hardware A Renner, F Sheldon, A Zlotnik, L Tao, A Sornborger Nature Communications 15 (1), 9691, 2024 | 25 | 2024 |
| Conducting-insulating transition in adiabatic memristive networks FC Sheldon, M Di Ventra Physical Review E 95 (1), 012305, 2017 | 22 | 2017 |
| Global minimization via classical tunneling assisted by collective force field formation F Caravelli, FC Sheldon, FL Traversa Science Advances 7 (52), eabh1542, 2021 | 20 | 2021 |
| Stress-testing memcomputing on hard combinatorial optimization problems F Sheldon, P Cicotti, FL Traversa, M Di Ventra IEEE transactions on neural networks and learning systems 31 (6), 2222-2226, 2019 | 18 | 2019 |
| Computational capacity of , memristive, and hybrid reservoirs FC Sheldon, A Kolchinsky, F Caravelli Physical Review E 106 (4), 045310, 2022 | 12 | 2022 |
| Critical branching processes in digital memcomputing machines SRB Bearden, F Sheldon, M Di Ventra Europhysics Letters 127 (3), 30005, 2019 | 10 | 2019 |
| Fully analog memristive circuits for optimization tasks: A comparison FC Sheldon, F Caravelli, C Coffrin Handbook of Unconventional Computing: VOLUME 2: Implementations, 193-213, 2022 | 3 | 2022 |
| Phases of memristive circuits via an interacting disorder approach F Caravelli, FC Sheldon arXiv preprint arXiv:2009.00114, 2020 | 3 | 2020 |
| The computational capacity of Mem-LRC reservoirs F Sheldon, F Caravelli Proceedings of the 2020 Annual Neuro-Inspired Computational Elements …, 2020 | 3 | 2020 |
| Number of attractors in the critical Kauffman model is exponential TMA Fink, FC Sheldon Physical Review Letters 131 (26), 267402, 2023 | 2 | 2023 |
| Implementing backpropagation for learning on neuromorphic spiking hardware A Renner, F Sheldon, A Zlotnik, L Tao, A Sornborger Proceedings of the 2020 Annual Neuro-Inspired Computational Elements …, 2020 | 2 | 2020 |
| Phase-dependent noise in Josephson junctions F Sheldon, S Peotta, M Di Ventra The European Physical Journal Applied Physics 81 (1), 10601, 2018 | 2 | 2018 |
| Insights from number theory into the critical Kauffman model with connectivity one F Sheldon, T Fink Journal of Physics A: Mathematical and Theoretical, 2023 | 1 | 2023 |
| The computational capacity of memristor reservoirs FC Sheldon, A Kolchinsky, F Caravelli Arxiv manuscript, 2020 | 1 | 2020 |
| Simple solution of the critical Kauffman model with connectivity one TMA Fink, FC Sheldon networks 104, 048701, 2010 | 1 | 2010 |
| Network analysis of memristive device circuits: dynamics, stability and correlations F Barrows, FC Sheldon, F Caravelli arXiv preprint arXiv:2402.16015, 2024 | | 2024 |
M. Di VentraProfessor of Physics, University of California, San DiegoVerified email at physics.ucsd.edu
