Jonathan J. Cannon, Ph.D.
Massachusetts Institute of Technology
Department of Brain and Cognitive Science
Bldg. 46-4077, 43 Vassar Street 02139, Cambridge, MA (USA)
2009 – 2014 Ph.D. & MA., Mathematics – Neural network modeling. Boston University
2004 – 2008 ScB., Mathematics/Computer Science. Brown University
2022 – present Assistant Prof. in Psychology, Neuroscience, and Behavior. McMaster University.
Affiliate: School of Computational Science and Engineering
Research questions: What are the computational, algorithmic, and neurophysiological underpinnings of our sense of rhythm, and how do they fit into the larger picture of brain and motor function?
2019 – 2022 Postdoctoral Researcher in Neuroscience. MIT.
Advisor: Prof. Pawan Sinha
Research question: How are temporal predictive abilities affected by autism?
- Designed and ran EEG and psychophysics experiments with autistic and neurotypical participants.
- Developed and built technological solution to synchronize sound recording and EEG systems.
- Designed and programmed online behavioral experiments.
- Conducted systematic review of literature on prediction in autism.
2014 – 2016 Postdoctoral Researcher in Neuroscience. Brandeis University.
Advisor: Prof. Paul Miller
Research question: How can both synaptic and intrinsic homeostasic mechanisms cooperate to control neuronal firing rates in a network?
- Demonstrated stable neural homeostasis by simultaneous intrinsic and synaptic processes (“dual homeostasis”) in a variety of computational models.
- Demonstrated optimization of neural network’s capacity to integrate and to transmit its inputs by dual homeostasis.
- Derived mathematical conditions for dual homeostatic stability in analytically tractable models.
2009 – 2014 Graduate Researcher in Mathematical Neuroscience. Boston University.
Advisor: Prof. Nancy Kopell
Research question: Do the physiological origins of gamma oscillations in the brain support their hypothesized role in facilitating communication between brain areas through phase coherence?
- Developed reduced, analytically tractable models of neural circuits that generate gamma rhythms.
- Proved analytically that these models behaved well under periodic forcing and entrained with phase alignment advantageous to directed neural communication.
2008 – 2009 Research Assistant in Machine Learning. Brown University.
Advisor: Elie Bienenstock. Tree learning for natural language parsing.
Summers 2006, 2007 Research Assistant in Machine Learning. Washington University.
Advisor: Robert Pless. Manifold learning for CT scan reconstruction.
2022 – 2027 NSERC Discovery Grant. $142,500
Exploring and Modeling Entrainment to Rhythm in the Motor System
Teaching and Advising
2016 – 2019 Teacher of Math and Science. Meridian Academy, Boston, MA.
- Taught and developed curriculum for middle and high school courses: “Calculus/Physics”, “Human Biology and Decision-Making” , “Engineering”, “Mathematical Modeling”, “Doing Research In Math And Science”
- Guided student investigations outside of class on complex dynamical systems, abstract algebra, linear algebra, object-oriented programming, Pythagorean triplets, neural nets
2015 Instructor of Computational Neuroscience. Brandeis University.
Received 4.67/5 overall rating and 4.69/5 instructor rating.
- Redesigned semester-long computational neuroscience course primarily for undergraduates covering:
- Neural data analysis in the time and frequency domains
- Hodgkin-Huxley neuron modeling
- Neural network models of supervised and unsupervised learning
- Taught two classes a week through lecture and reading-guided discussion.
- Created and evaluated weekly reading and coding assignments.
- Guided and gave feedback on final coding and literature review projects.
2013 Instructor of Introductory Quantitative Biology. Boston University.
- Redesigned semester-long undergraduate quantitative biology course covering:
- Discrete and continuous time dynamical systems
- Models of biophysical processes, population growth, and ecosystems
- Taught two classes a week through lecture and student interaction.
- Assigned and evaluated problem sets and student presentations.
2012 Teaching assistant: Calculus 1. Boston University.
- Roamed room during problem-solving sessions helping students understand course material.
2008–2009 Instructor: Vector Calculus Wheeler High School, Providence, RI.
- Guide three accelerated high school students in student-centered study of vector calculus.
Advising & Mentorship
2022 – present Graduate research supervisor. McMaster Unviersity.
Matin Yousefabadi: PhD student in Psychology, Neuroscience, and Behaviour
Yassaman Ommi: Masters student in Computational Science and Engineering
2022 – present Graduate supervisory committee member. McMaster Unviersity.
Emily Wood: PhD student in Psychology, Neuroscience, and Behaviour
2022 – present Undergraduate research supervisor. McMaster University.
Arya Masoumi: Online experiment design in rhythm cognition, EEG analysis
Davina Premraj: EEG analysis
Jacob Duda: Whole-brain modeling
Lochana Kandimbige: Experiment design in rhythm cognition
Olivia Valentini: Literature review on time perception in Parkinson’s
2019 – 2022 Undergraduate research advisor. M.I.T.
Advised and guided three undergraduates in participation in research involving EEG analysis, behavioral data analysis, and online experiment design and programming.
2016 – 2019 High school research advisor. Meridian Academy, Boston, MA.
Advised three students in year-long research projects, including extensive literature review and creative synthesis of learning.
2016 Undergraduate mentor. Brandeis University
Advised Brandeis undergraduate in year-long project designing an experiment and a data analysis strategy to study neurofeedback in meditation.
2013 – 2014 Undergraduate mentor.
Remotely advised undergraduate student in thesis project on neural circuit modeling.
2014 – 2016 Guest violin instructor.
Taught enrichment string orchestra workshops at elementary, middle, and high school levels at public schools in Waltham, Mansfield, and Rockport, MA.
2014 – 2015, Private violin teacher.
2008 – 2009 Taught regular lessons to students of assorted ages and levels.
2022 Leadership and Professional Strategies and Skills (LEAPS) Training. MIT.
“Sharpen your professional strategies and skills”
Half-semester leadership training course for graduate students and postdocs
2022 CIFAR Neuroscience of Consciousness Winter School. Online.
Three-day event where “tomorrow’s neuroscience leaders work closely with world-class researchers.”
2016 – 2017 New Teachers Collaborative. Francis Parker Charter School.
Year-long bi-weekly course on progressive teaching methods.
2016 Summer School in Adaptive Neurotechnologies. Wadsworth Center.
NIH-funded intensive three-week course on technologies supporting real-time adaptive interaction with the nervous system.
IMPACT program. MIT.
NIH-funded six-month mentorship in science communication for real-world impact.
2010 Complex Systems Summer School. Santa Fe Institute.
Intensive four-week introduction to complex behavior in mathematical, physical, living, and social systems.
Cinelytė, U., Cannon, J., Patel, A., & Mullensiefen, D. (2022). Testing beat perception without sensory cues to the beat: the Beat-Drop Alignment Test (BDAT). Attention, Perception, & Psychophysics, In Press
- Kaplan T, Cannon J, Jamone L, Pearce M (2022) Modeling enculturated bias in entrainment to rhythmic patterns. PLOS Computational Biology 18(9): e1010579.
- Cannon, J. (2021). Expectancy-based rhythmic entrainment as continuous Bayesian inference. PLOS Computational Biology, 17(6), e1009025.
- Cannon, J., O’Brien, A., Bungert, L., & Sinha, P. (2021). Prediction in Autism Spectrum Disorder: A Systematic Review of Empirical Evidence. Autism Research, 14(4), 604–630.
- Cannon, J., & Patel, A. (2021). How beat perception co-opts motor neurophysiology. Trends in Cognitive Science, 25(2), 137–150.
- Miller, P., & Cannon, J. (2018). Combined mechanisms of neural firing rate homeostasis. Biological Cybernetics, 113(1), 47–59.
- Cannon, J. (2017b). Analytical calculation of mutual information between weakly coupled poisson-spiking neurons in models of dynamically gated communication. Neural Computation, 29(1), 118–145.
- Cannon, J., & Miller, P. (2017). Stable control of firing rate mean and variance by dual homeostatic mechanisms. Journal of Mathematical Neuroscience, 7(1), 1–38.
- Cannon, J., & Miller, P. (2016). Synaptic and intrinsic homeostasis cooperate to optimize single neuron response properties and tune integrator circuits. Journal of Neurophysiology, 116(5), 2004–2022.
- Cannon, J., & Kopell, N. (2015). The leaky oscillator: Properties of inhibition-based rhythms revealed through the singular phase response curve. SIAM J. on Applied Dynamical Systems, 14(4), 1930–1977.
- Cannon, J., Kopell, N., Gardner, T., & Markowitz, J. (2015). Neural sequence generation using spatiotemporal patterns of inhibition. PLOS Computational Biology, 11(11), e1004581.
- Cannon, J., McCarthy, M., Lee, S., Lee, J., Börgers, C., Whittington, M., & Kopell, N. (2014). Neurosystems: brain rhythms and cognitive processing. European Journal of Neuroscience, 39(5), 705–19.
- Cannon, J. (2014b). On the interaction of multiple gamma-rhythmic populations (Doctoral dissertation). Boston University.
- Zinselmeyer, B., Dempster, J., Wokosin, D., Cannon, J., Pless, R., Parker, I., & Miller, M. (2009). Two-photon microscopy and multidimensional analysis of cell dynamics (T. Handel & D. Hamel, Eds.). In T. Handel & D. Hamel (Eds.), Methods in enzymology. Elsevier Science Publishing Co Inc.
- Cannon, J. (2021). A neurophysiological process theory of motor and covert beat-keeping. [16th International Conference on Music Perception and Cognition / 11th triennial conference of ESCOM, July 28, video online at https://youtu.be/ut104v38yJQ]
- Cannon, J. (2021). A shared predictive framework for motor control and beat-based timing. [18th Rhythm Production and Perception Workshop, University of Oslo (virtual), June 22, video online at https://youtu.be/l8FbKeqRY08]
- Cannon, J. (2021). Expectancy-based rhythmic entrainment as continuous Bayesian inference. [Canadian Society for Brain, Behavior, and Cognitive Science (CSBBCS) meeting (virtual), June 18]
- Cannon, J. (2020). A Bayesian filtering framework for neural and sensorimotor entrainment [Neuromatch 3, October 27, video online at https://youtu.be/UeHSHwVW_Wg].
- Cannon, J. (2019). A neurocomputational model of beat-based temporal processing [Biennial Meeting of the Society for Music Perception and Cognition, NYU, New York, New York, Aug 6].
- Cannon, J. (2019). A neurocomputational model of beat-based temporal processing [17th Rhythm Production and Perception Workshop, Park Place Hotel and Conference Center, Traverse City, Michigan, Jun 18].
- Cannon, J. (2019). A neurocomputational model of beat-based temporal processing [New England Sequencing and Timing meeting, University of Connecticut, Storrs, CT, Apr 6].
- Cannon, J. (2017). Dual-mechanism firing rate homeostasis stabilizes multiple firing rate statistics [SIAM Conference on Applications of Dynamical Systems, Snowbird Resort, Snowbird, UT, May 25].
- Cannon, J. (2015). Stability analysis of dual activity-dependent homeostasis [Janelia Theoretical Neuroscience Workshop, Janelia Research Campus, Ashburn, VA, Nov 19].
- Cannon, J. (2015). Theoretical foundations and applications of dual activity-dependent homeostasis [Swartz Annual Meeting, Janelia Farm, Ashburn, VA, Aug 3].
- Cannon, J. (2022). Rhythm Entrainment as Dynamic Bayesian Inference. [BRAMS-CRBLM Lecture Series Guest Speaker, Université de Montréal, Nov. 9]
- Cannon, J. (2022). Physiology and Theory of Motor and Covert Rhythm Entrainment. [Psychology, Neuroscience & Behaviour Graduate Research Day, Keynote Speaker, McMaster University, April 8]
- Cannon, J. (2022). Physiology and Theory of Motor and Covert Entrainment. [Human Neuroscience Journal Club Guest Speaker, University of Alabama at Birmingham, February 4]
- Cannon, J. (2021). Expectancy-based rhythmic entrainment as continuous Bayesian inference. [Guest Talk, Center for Music in the Brain, Aarhus University, December 3]
- Cannon, J. (2021). What do we “use” brain rhythms for? [Guest lecture, “Nonlinear Dynamics and Chaos,” Tufts University, November 4]
- Cannon, J. (2021). Following neural clues toward a shared predictive framework for motor control and beat-based timing. [Neuroscience and Music Lab virtual meeting, University of Western Ontario, May 4]
- Cannon, J. & O’Brien, A. (2021). Prediction in Autism Spectrum Disorder: What Does the Empirical Evidence Say? [Merav Ahissar lab virtual meeting, Hebrew University of Jerusalem, April 19]
- Cannon, J. (2021). The shared predictive roots of motor control and beat-based timing. [Centre for Systematic Musicology virtual weekly research seminar, University of Graz, Austria, April 13]
- Cannon, J. (2021). Auditory rhythm tracking as variational inference: Mathematical framework and neural substrates. [Theoretical Neurobiology Research Team virtual meeting (with PI Karl Friston), University College London, April 6]
- Cannon, J. (2021). Neurophysiological And Cognitive Dynamics Of Rhythmic Time-Keeping [Department of Psychology, Neuroscience, and Behavior seminar, McMaster University, March 9]
- Cannon, J. & O’Brien, A. (2021). Prediction in Autism Spectrum Disorder: What Does the Empirical Evidence Say? [Centre for Autism meeting, University of Reading, March 3]
- Cannon, J. (2021). The shared predictive roots of motor control and beat-based timing [Timing Research Forum Journal Club, Feb 17, video online at https://youtu.be/D2NgKgbQ4vA?t=1638].
- Cannon, J. (2021). Rhythmic Entrainment as Dynamic Inference [NJIT Mathematical Biology Seminar, Feb 2].
- Cannon, J. (2016). An information-theoretic framework for dynamic gating of neuronal communication [Applied Analysis and Computation Seminar, UMass Amherst, MA, Mar 30].
- Cannon, J. (2016). A mathematical framework for the transmission of oscillations and information among neuron populations [Physics Colloquium, UMass Boston MA, Apr 28].
- Cannon, J. (2015). A role for feedback inhibition and periodicity in sequence generation [NYU, Oct 2].
- Cannon, J. (2015). A role for feedback inhibition and periodicity in sequence generation [Cognitive Rhythms Collaborative retreat, Boston University, Boston, MA, Feb 23].
- Cannon, J. (2014a). A mathematical framework for the transmission of oscillations and information among neuronal populations [Brown/BU PDE Seminar, Boston University, Boston, MA, Dec 10].
- Cannon, J. (2021). A Bayesian framework for expectation-based rhythmic entrainment [Poster presented at Computational and Systems Neuroscience (COSYNE), Feb 26].
- Cannon, J. (2021). Prediction in Autism Spectrum Disorder: What Does the Empirical Evidence Say? [Poster presented at Society for Neuroscience (SfN) Annual Meeting, Jan 12].
- Cannon, J. (2015a). Information theory of dynamic neural gating [Poster presented at Swartz Annual Meeting, Aug 2 - 5, Ashburn, VA].
- Cannon, J., & Miller, P. (2015). The dynamics and consequences of dual-mechanism homeostasis [Poster presented at Computational and Systems Neuroscience (COSYNE), Mar 5–8, Salt Lake City, Utah].
- Cannon, J., Markowitz, J., Kopell, N., & Gardner, T. (2014a). Sequence generation by spatio-temporal cycles of inhibition [Poster presented at Society for Neuroscience (SfN) Annual Meeting, Nov 15 - 19, Washington, DC].
- Cannon, J., Markowitz, J., Kopell, N., & Gardner, T. (2014b). Sequence generation by spatio-temporal cycles of inhibition [Poster presented at Computational and Systems Neuroscience (COSYNE), Feb 26 - Mar 2, Salt Lake City, Utah].
- Cannon, J., & Kopell, N. (2013). Gamma rhythms under periodic forcing [Poster presented at Rhythmic Dynamics and Cognition Conference, June 2013, Boston, MA].
Leadership & Service
2015 – present Reviewer.
Science, Journal of Neural Computation, Journal of Chaos, Journal of Neurophysiology, European Journal of Neuroscience, Mathematical Intelligencer, e-Life, Current Biology, PLOS One, Timing & Time Perception, Frontiers in Psychology, Journal of Experimental Psychology, Cortex
2018 – 2021 Program committee chair, New England Folk Festival Association (NEFFA).
- Led 8-member committee in selecting programming for annual NEFFA festival.
- Recruited and led a committee to conceive, plan, and execute NEFFA’s pandemic programming, including a monthly online concert series and an online festival that drew over 2,000 attendees and raised $23,000 to support performers and techs.
- Member of Anti-Racism Committee.
2014 – 2018 Member of board and program committee, New England Folk Festival Association.
2015 – 2017 Co-founder. “Eyes Up”.
Vision research and rehabilitation project funded by Harvard Innovation Lab.
2014 Organizer. CV-Writing Professional Development Workshop.
Supported by the Boston University Department of Mathematics and Statistics.
2011 – 2012 Co-Organizer and Recording Secretary. Alpha Rhythms Working Group.
Brought together researchers from BU, MIT, and Brown to discuss cognitive role of alpha rhythms, supported by the Cognitive Rhythms Collaborative (CRC).
Founder and Organizer. Cognitive Rhythms Reading Group.
Supported by the Cognitive Rhythms Collaborative (CRC).
Other Professional Experience
2015 – present Co-founder, CFO, CTO, and CCO. “Flying Leap Games”.
Developed and launched storytelling card game “Wing It” now carried by over 200 retailers in the U.S., Canada, U.K., and Australia.
2013 – present Music Writer. Boston Herald “State of the Arts”, MyJewishLearning.com
Wrote and published online articles on traditional music, traditional dance, and music education
2010 – present Semi-professional Fiddler and Guitarist. Assorted dance bands
Played music for over 150 folk dances in eleven U.S. states.
2009 – present Semi-professional Violinist. Ezekiel’s Wheels Klezmer Band
Released three studio albums, nominated for 2020 Boston Music Awards “International Artist of the Year,” awarded “Audience Choice Award” and “Best Klezmer Band” at 2012 International Jewish Music Festival in Amsterdam.
2008 – 2009 Founder and Organizer. First annual Brown University Folk Festival.
Wrote grant proposals, received over $4000 support
Some experience: C++, NetLogo, Scheme, LATEX
Machine Learning Bayesian filtering, recurrent neural nets, hidden Markov models, Isomap, dimensionality reduction
Experiment Design Analog audio/EEG integration, PsychoPy/PsychoJS development