Frances S. Chance

Cognitive & Emerging Computing

Author profile picture

Cognitive & Emerging Computing

fschanc@sandia.gov

ZDNet: Dragonfly brain circuits could help create superchips

(505) 845-7956

Sandia National Laboratories, New Mexico
P.O. Box 5800
Albuquerque, NM 87185-1327

Biography

My research focus is to understand how biological neural networks represent, transform, and transmit information in the brain. At Sandia Labs I use computational modeling and mathematical analysis of neurons and neural networks to understand the basic computations that underlie sensory processing and cognition.

I am intrigued by potential parallels between the operations of neural systems and the challenges faced by modern computers. At Sandia my research program has expanded to include applying knowledge of neural systems towards the development of novel neuro-inspired algorithms and brain-based architectures to improve the performance of computing systems and other engineered systems.

Education

  • Ph.D. Brandeis University
  • M.S. Brandeis University
  • B.S. California Institute of Technology

Publications

James Bradley Aimone, Suma George Cardwell, Frances S. Chance, Srideep Musuvathy, Fredrick Rothganger, William Mark Severa, John Darby Smith, Corinne Michelle Teeter, Craig Michael Vineyard, Felix Wang, (2022). Neural Mini-Apps: Lighter, Faster Diagnostic Tests for Neuromorphic Computing Document ID: 1573711

Luke Garrison Parker, Frances S. Chance, Suma George Cardwell, (2022). Benchmarking a Bio-inspired SNN on a Neuromorphic System NICE 2022Neuro-Inspired Computational Elements Conference Document ID: 1493715

Craig Michael Vineyard, James Bradley Aimone, Suma George Cardwell, Frances S. Chance, Srideep Musuvathy, Fredrick Rothganger, John Darby Smith, William Mark Severa, Corinne Michelle Teeter, Felix Wang, (2022). Neural Mini-Apps as a Tool for Neuromorphic Computing Insight Neuro-Inspired Computational Elements (NICE) Workshop 2022 Document ID: 1493378

James Boyle, Frances S. Chance, Suma George Cardwell, Mark Plagge, Andreas Gerstlauer, (2022). Codesign of Next-Generation Neuromorphic Architectures UT-Austin Sandia Day 2022 Document ID: 1482229

Luke Garrison Parker, Frances S. Chance, Suma George Cardwell, (2022). Benchmarking a Bio-inspired SNN on a Neuromorphic System NICE 2022Neuro-Inspired Computational Elements Conference Document ID: 1470993

Craig Michael Vineyard, Suma George Cardwell, Frances S. Chance, Srideep Musuvathy, Fredrick Rothganger, William Mark Severa, John Darby Smith, Corinne Michelle Teeter, Felix Wang, James Bradley Aimone, (2022). Neural Mini-Apps as a Tool for Neuromorphic Computing Insight The 9th Annual Neuro-Inspired Computational Elements (NICE) workshop Document ID: 1470530

Frances S. Chance, Suma George Cardwell, Gonzalez-Bellido, Scott Koziol, (2022). Neuroscience-Inspired Approaches to Low-Power Computing 2022 Energy Consequences of Information Workshop Document ID: 1459899

Frances S. Chance, (2021). Lessons from dragonflies for neuromorphic computing MARCUS project meeting Document ID: 1392943

Frances S. Chance, Charles Q. Little, Marcus McKenzie, Ryan Anthony Dellana, Daniel E. Small, Thomas R. Gayle, David K. Novick, (2021). Biologically Inspired Interception on an Unmanned System https://www.osti.gov/search/identifier:1827493 Document ID: 1368736

Frances S. Chance, (2021). Lessons from a dragonfly?s brain for neuromorphic computing invited briefing at Lockheed Martin Document ID: 1380876

Luke Garrison Parker, Frances S. Chance, Suma George Cardwell, (2021). Implementation of Dragonfly Interception Model on Neuromorphic Hardware International Conference on Neuromorphic Systems Document ID: 1330664

Frances S. Chance, (2021). Lessons from Dragonflies in Brain-inspired Computing Society for Brain Mapping & Therapeutics Annual Congress Document ID: 1330108

Frances S. Chance, (2021). Dragonfly AI: The computer bug you want IEEE Spectrum https://www.osti.gov/search/identifier:1834329 Document ID: 1317994

Frances S. Chance, (2021). Not all computer bugs are bad: Looking to insects for neural-inspired computing Modeling and Computation Seminar at the University of Arizona (Dept. of Mathematics) https://www.osti.gov/search/identifier:1866336 Document ID: 1305390

Frances S. Chance, (2020). Neuromorphic Processing and Sensing for Interception https://www.osti.gov/search/identifier:1665934 Document ID: 1207398

Frances S. Chance, (2020). Interception from a Dragonfly Neural Network Model ICONS Conference International Conference on Neuromorphic Systems https://www.osti.gov/search/identifier:1805334 Document ID: 1161073

Frances S. Chance, (2020). Dragonfly-Inspired Interception CSRI Summer Student Seminar Series https://www.osti.gov/search/identifier:1807436 Document ID: 1140158

Frances S. Chance, James Bradley Aimone, Srideep Musuvathy, Michael Reed Smith, Craig Michael Vineyard, Felix Wang, (2020). Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence Frontiers in Computational Neuroscience https://www.osti.gov/search/identifier:1617318 Document ID: 1116284

Suma George Cardwell, James Bradley Aimone, Craig Michael Vineyard, William Mark Severa, Frances S. Chance, (2020). Truly heterogeneous HPC: co-design to achieve what science needs from HPC Smoky Mountains Computational Sciences & Engineering Conference Document ID: 1115949

Frances S. Chance, (2020). Not all computer bugs are bad: What can we learn from insects for neural-inspired computing? Sandia New Ideas Research Forum https://www.osti.gov/search/identifier:1772539 Document ID: 1104505

Frances S. Chance, (2020). A biologically-inspired approach to interception EDFA Magazine Document ID: 1104110

Frances S. Chance, (2020). Dragonflies A Lesson in Missile Defense Sandia Emeritus Event https://www.osti.gov/search/identifier:1768147 Document ID: 1102829

Frances S. Chance, (2019). Lessons from dragonflies in brain-inspired computing Society for Brain Mapping and Therapeutics Annual Congress Document ID: 1068354

Srideep Musuvathy, James Bradley Aimone, Suma George Cardwell, Frances S. Chance, Ryan Anthony Dellana, Yang Ho, Leah Evelyn Reeder, William Mark Severa, Craig Michael Vineyard, Felix Wang, (2019). Fugu: Algorithm Development for Neuromorphic Hardware Intel Neuromorphic Research Community Fall Workshop https://www.osti.gov/search/identifier:1646072 Document ID: 1032818

Frances S. Chance, (2019). Dragonfly-Inspired Algorithms for Intercept Trajectory Planning https://www.osti.gov/search/identifier:1569338 Document ID: 1032111

Frances S. Chance, (2019). Dragofly-Inspired Intercept Approaches Missile Defense Agency Workshop https://www.osti.gov/search/identifier:1645948 Document ID: 1020541

Frances S. Chance, (2019). Dragonfly-Inspired Algorithms for Intercept Trajectory Planning Sandia Computing and Information Sciences External Review Board https://www.osti.gov/search/identifier:1807166 Document ID: 997730

Frances S. Chance, (2019). A Biologically-Inspired Algorithm for Interception International Conference on Neuromorphic Systems (ICONS) 2019 https://www.osti.gov/search/identifier:1641156 Document ID: 985793

Frances S. Chance, (2019). Strategies of Dragonfly Interception Annual Meeting of the Organization for Computational Neurosciences https://www.osti.gov/search/identifier:1641130 Document ID: 985527

Frances S. Chance, (2019). Strategies of Dragonfly Interception CNS201928th Annual Meeting of the Organization for Computational Neurosciences Document ID: 936934

Frances S. Chance, Christina E. Warrender, (2018). Implementing Neural Adaptive Filtering in Engineered Detection Systems https://www.osti.gov/search/identifier:1474262 Document ID: 865661

Frances S. Chance, Christina E. Warrender, (2018). Retinal-Inspired Algorithms for Detection of Moving Objects International Conference on Neuromorphic Systems https://www.osti.gov/search/identifier:1806779 Document ID: 841335

Frances S. Chance, Christina E. Warrender, (2018). Retinal Motion-Detection Under Noisy Conditions 2018 Meeting of Organization of Computational Neurosciences (CNS2018) https://www.osti.gov/search/identifier:1569533 Document ID: 830466

Frances S. Chance, Christina E. Warrender, (2018). Retinal-Inspired Algorithms for Detection of Moving Objects International Conference on Neuromorphic Systems 2018 https://www.osti.gov/search/identifier:1530690 Document ID: 819709

Frances S. Chance, Christina E. Warrender, (2018). Retinal Motion-Detection Under Noisy Conditions Organization for Computational Neurosciences Meeting Document ID: 772447

Frances S. Chance, Christina E. Warrender, (2018). Retinal-Inspired Algorithms for Motion Detection Neuro Inspired Computational Elements (NICE) Workshop 2018 https://www.osti.gov/search/identifier:1497542 Document ID: 761350

Frances S. Chance, Christina E. Warrender, (2018). Retinal-Inspired Algorithms for Motion Detection (NICE 2018 summary slide) Neuro Inspired Computational Elements (NICE) Workshop 2018 https://www.osti.gov/search/identifier:1497544 Document ID: 761358

Frances S. Chance, (2017). Can the sum of the parts be greater than the whole? Neural-inspired computation through study of canonical circuits. invited technical seminar for the Group for Neural Theory at Ecole Normale Superieure Document ID: 693090

Frances S. Chance, James Bradley Aimone, Kristofor Carlson, Warren Leon Davis, Jacob Aaron Hobbs, Timothy Malcolm Shead, Stephen Joseph Verzi, Craig Michael Vineyard, (2017). Keeping the brain in brain-inspired computing: Test and Evaluation for MICrONS Sandia CIS Review Board Document ID: 659846

Frances S. Chance, (2017). Theoretical Neuroscience in the Age of Big Data and Machine Learning Advancing Neuroscience with the National Labs https://www.osti.gov/search/identifier:1367227 Document ID: 624524

Frances S. Chance, (2016). DOE National Laboratories and BRAIN: MICrONS at Sandia National Laboratories Brain Initiative Investigators Meeting https://www.osti.gov/search/identifier:1413637 Document ID: 565700

Frances S. Chance, (2016). Modeling Information Multiplexing in the Hippocampus https://www.osti.gov/search/identifier:1761822 Document ID: 530883

Frances S. Chance, (2016). Computation in the Hippocampus CCR Summer Seminar Series https://www.osti.gov/search/identifier:1514637 Document ID: 507487

Frances S. Chance, (2016). Machine Intelligence from Cortical Networks: Technical Area 1 – Sandia National Laboratories – Phase 1 site visits MICrONS Phase 1 site visits https://www.osti.gov/search/identifier:1514573 Document ID: 475498

Frances S. Chance, (2016). Neural Adaptive Filtering in Detection Systems The Ninth Workshop on Fault-Tolerant Spaceborne Computing Employing New Technologies, 2016 https://www.osti.gov/search/identifier:1368904 Document ID: 464131

Frances S. Chance, James Bradley Aimone, Kristofor David Carlson, Warren Leon Davis, Timothy Malcolm Shead, Craig Michael Vineyard, (2016). Sandia MICrONS Phase 1 kickoff slides Machine Intelligence from Cortical Networks (MICrONS) Program Kickoff https://www.osti.gov/search/identifier:1514321 Document ID: 386851

Craig Michael Vineyard, James Bradley Aimone, Michael Lewis Bernard, Kristofor David Carlson, Frances S. Chance, James C. Forsythe, Conrad D. James, Fredrick Rothganger, William Mark Severa, Ann Speed, Stephen Joseph Verzi, Christina E. Warrender, John S. Wagner, Leann Adams Miller, (2015). Neural Computing at Sandia National Laboratories Rebooting Computing Summit (RCS 4) https://www.osti.gov/search/identifier:1335732 Document ID: 365383

Frances S. Chance, Andrew (University of Florida) Maurer, Sara (University of Florida) Burke, Carol (University of Arizona) Barnes, (2015). Different weightings of input components to hippocampal CA1 place cells in young and aged rats Annual Meeting for the Organization for Computational Neurosciences https://www.osti.gov/search/identifier:1530960 Document ID: 308197

Frances S. Chance, Andrew P. (University of Florida) Maurer, Sara N. (University of Florida) Burke, Carol A. (University of Arizona) Barnes, (2015). Different weightings of input components to hippocampal CA1 place cells in young and aged rats Cns 2015 Document ID: 221670

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