Contact Information
Neurorobotics or Brain-Based Robotics
jkrichma at uci.edu or jeff.krichmar at uci.edu
https://sites.socsci.uci.edu/~jkrichma/index.html
Professor Brain Krichmar’s Research
Jeffrey L. Krichmar received a B.S. in Computer Science in 1983 from the University of Massachusetts at Amherst, a M.S. in Computer Science from The George Washington University in 1991, and a Ph.D. in Computational Sciences and Informatics from George Mason University in 1997. He spent 15 years as a software engineer on projects ranging from the PATRIOT Missile System at the Raytheon Corporation to Air Traffic Control for the Federal Systems Division of IBM. From 1999 to 2007, he was a Senior Fellow in Theoretical Neurobiology at The Neurosciences Institute. He currently is a professor in the Department of Cognitive Sciences and the Department of Computer Science at the University of California, Irvine. Krichmar has nearly 20 years experience designing adaptive algorithms, creating neurobiologically plausible network simulations, and constructing brain-based robots whose behavior is guided by neurobiologically inspired models. He has over 100 publications and holds 7 patents. His research interests include neurorobotics, embodied cognition, biologically plausible models of learning and memory, neuromorphic applications and tools, and the effect of neural architecture on neural function. He is a Senior Member of IEEE and the Society for Neuroscience.
His group has created and supports a simulation environment for developing large-scale spiking neuron networks, which is available at:
http://www.socsci.uci.edu/~jkrichma/CARLsim
His group has created an inexpensive platform for robotic control, coupling the powerful capabilities of Android smartphones with off-the-shelf robotic components. An instruction manual and software examples can be found at:
http://www.socsci.uci.edu/~jkrichma/ABR
Research
Neurorobotics or Brain-Based Robotics
– Neuromodulation as a robot controller
– Socially Assistive Robot that focuses on touch
Neuromorphic Computing
– Spiking Neural Network of Motion Perception and Visual Navigation
Goals of Neurorobotics
Understanding Through Building
– Building physical systems that demonstrate cognitive abilities could lead
to a better understanding of the neural machinery that realizes cognitive
function
Building More Intelligent Machines
– Constructing physical systems could lead to a system that demonstrates
capabilities commonly found in the animal kingdom, but rarely found in
artificial systems