I joined the lab in October 2020 as a Postdoctoral Research Fellow. Prior to this I was a contractor at NASA Ames Research Center's Intelligent Robotics Group. There I worked on automated flood mapping, life detection, and automated exploration for planetary robotics. I earned my Ph.D in robotics in 2018 from Carnegie Mellon University, M.Sc. in Neuroscience from Oxford in 2011, M.Sc. Robotics from CMU in 2010, and B.Eng from Memorial University in 2005.

Publications

Journal Articles

• Nicole Sandra-Yaffa Dumont, Andreas Stöckel, P. Michael Furlong, Madeleine Bartlett, Chris Eliasmith, Terrence C. Stewart (2023) Biologically-Based Computation: How Neural Details and Dynamics Are Suited for Implementing a Variety of Algorithms. Brain Sciences, 13(2):245.  Abstract  PDF  DOI  External link
• P Michael Furlong, Chris Eliasmith (2023) A new paradigm for probabilistic neuromorphic programming. IEEE Brain e-Newsletter.  Abstract  PDF  External link
• Nicole Sandra-Yaffa Dumont, P. Michael Furlong, Jeff Orchard, Chris Eliasmith (2023) Exploiting semantic information in a spiking neural SLAM system. Frontiers in Neuroscience.  Abstract  PDF  DOI  External link
• P. Michael Furlong, Terrrence C. Stewart, Chris Eliasmith (2021) Fractional Binding in Vector Symbolic Representations for Efficient Mutual Information Exploration. ICRA Workshop: Towards Curious Robots: Modern Approaches for Intrinsically-Motivated Intelligent Behavior.  Abstract  PDF  External link

Conference and Workshop Papers

• P Michael Furlong, Katherine Simone, Nicole Dumont, Madeline Bartlett, Terrence C. Stewart, Jeff Orchard, Chris Eliasmith (2024) Biologically-plausible Markov Chain Monte Carlo Sampling from Vector Symbolic Algebra-encoded Distributions. In ICANN.  Abstract  PDF
• P Michael Furlong, Chris Eliasmith (2023) Bridging Cognitive Architectures and Generative Models with Vector Symbolic Algebras. In Proceedings of the 2023 AAAI Fall Symposium.  Abstract  PDF  External link
• Madeline Bartlett, Katherine Simone, Nicole Dumont, P Michael Furlong, Chris Eliasmith, Jeff Orchard, Terrence C. Stewart (2023) Improving Reinforcement Learning with Biologically Motivated Continuous State Representations. In Proceedings of the International Conference on Cognitive Modeling.  Abstract  PDF
• P. Michael Furlong, Chris Eliasmith (2022) Fractional Binding in Vector Symbolic Architectures as Quasi-Probability Statements. In 44th Annual Meeting of the Cognitive Science Society. Cognitive Science Society.  Abstract  PDF  External link
• Chris Eliasmith, P. Michael Furlong (2022) Continuous then discrete: A recommendation for building robotic brains. In Aleksandra Faust, David Hsu, and Gerhard Neumann, editors, Proceedings of the 5th Conference on Robot Learning, volume 164 of Proceedings of Machine Learning Research, 1758–1763. PMLR.  Abstract  PDF  External link

Technical Reports and Preprints

• P. Michael Furlong, Andreas Stöckel, Terrence C. Stewart, Chris Eliasmith (2022) Learned Legendre Predictor: Learning with Compressed Representations for Efficient Online Multistep Prediction. Technical Report, Centre for Theoretical Neuroscience.  Abstract  PDF  External link