Professor of Computer Science

  • Rohr Science 216
  • Phone: (619) 849-2269


Dr. McKinstry holds a PhD in Computer Science from UCSD.  Prior to doctoral work, he earned an MS in Computer Science from USC and a BS in the same field from PLNU.  Dr. McKinstry's speciality is artificial intelligence.  He is a senior fellow at the Neurosciences Institute in La Jolla, and has worked in industry as both a software engineer and researcher.


Dr. McKinstry has done extensive research in neuroscience and artificial intelligence.  His publication topics include predictive motor control, machine consciousness and brain-based devices.  He is a fellow at the Neurosciences Institute as a theoretical neurobiology researcher.


Outside of the classroom, Dr. McKinstry enjoys teaching Sunday school and spending time with his family.  His other hobbies including golfing and playing Bridge and Rook.


J.L. McKinstry, and G.M. Edelman (2013). Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device. Front. Neurorobot.  7:10. doi: 10.3389/fnbot.2013.00010.


Chen Y, McKinstry JL and Edelman GM (2013) Versatile networks of simulated spiking neurons displaying winner-take-all behavior. Front. Comput. Neurosci. 7:16. doi: 10.3389/fncom.2013.00016


J.G. Fleischer, J.L. McKinstry, D.E. Edelman, and G.M. Edelman.   The Case For Using Brain-Based Devices To Study Consciousness.  In, J.L. Krichmar and H. Wagatsuma (Eds.),   Neuromorphic and Brain-Based Robots: Trends and Perspectives,  Cambridge University Press,  To appear.


J.L. McKinstry, A.K. Seth, G.M. Edelman, and J.L. Krichmar (2008). Embodied models of delayed neural responses: Spatiotemporal categorization and predictive motor control in brain-based devices.  Neural Networks 21:553-61.


J. L. McKinstry, G.M. Edelman, J.L. Krichmar (2006). A cerebellar model for predictive motor control tested in a brain-based device.  Proc. Natl. Acad. Sci. USA 103(28):10799-10804.


A.K. Seth, J.L. McKinstry, G.M. Edelman, and J.L. Krichmar (2004). Visual binding through reentrant connectivity and dynamic sychronization in a brain-based device. Cerebral Cortex 14(11): 1185-99.


See vita for full list of publications



Y. Chen and J.L. McKinstry (2013). "Mental imagery and reentry in a visuomotor reaching system using large-scale spiking neuronal models" Society for Neuroscience Annual Meeting, San Diego, November 2013.


J.L. McKinstry (2013). "Temporal sequence learning in reentrantly coupled winner-take-all networks of spiking neurons." Twenty Second Annual Computational Neuroscience Meeting, Paris, France, July, 2013.


Y. Chen, J.L. McKinstry, and G.M. Edelman (2011). "A winner-take-all network for map formation and pattern learning using spiking neural models." Society for Neuroscience Annual Meeting, Washington D.C., November 2011.


Y. Choe, L.C. Abbott, G. Ponte, J. Keyser, J. Kwon, D. Mayerich, D. Miller, D. Han, A.M. Grimaldi, G. Fiorito, D.B. Edelman, and J.L. McKinstry (2010). Charting out the octopus connectome at submicron resolution using the knife-edge scanning microscope. BMC Neuroscience. Nineteenth Annual Computational Neuroscience Meeting: CNS*2010(in press).


J. L. McKinstry “A cerebellar model for predictive motor control tested in a brain-based device.”  19th Annual Spring Brain Conference, Palm Springs, California, March 12-15, 2008.


C. Marcarelli, J.L. McKinstry. “Testing for machine consciousness using insight learning.” Assoc. for the Advancement of Artificial Intelligence 2007 Fall Symposium, AI and Consciousness: Theoretical Foundations and Current Approaches, Washington, D.C., November 8–11, 2007