Jeffrey L. Krichmar

Professor, Cognitive Sciences
School of Social Sciences

Ph.D., George Mason University, 1997, Computational Sciences and Informatics

Phone: (949) 824-5888
Email: jkrichma@uci.edu

University of California, Irvine
2328 Social and Behavioral Sciences Gateway
Mail Code: 5100
Irvine, CA 92697

picture of Jeffrey L. Krichmar

Research
Interests
Neurorobotics, embodied cognition, biologically plausible models of learning and memory, and the effect of neural architecture on neural function.
   
URLs Krichmar's homepage
   
Center for Cognitive Neuroscience and Engineering (CENCE)
   
Research
Abstract
In the Cognitive Anteater Robotics Laboratory (CARL) at the University of California, Irvine, we are designing robotic systems whose behaviors are guided by large-scale simulations of the mammalian brain. Because these simulated nervous systems are embodied on a robot, they provide a powerful tool for studying brain function. Moreover, because these cognitive robots are embedded in the real-world, the system's behavior and function can be tested similarly to that of an animal under experimental conditions. We have studied perception, operant conditioning, episodic and spatial memory, and motor control through the simulation of brain regions such as the visual cortex, the hippocampus, the cerebellum, and the neuromodulatory systems. The behavior and neuronal dynamics of these systems were directly compared with empirical data from experimental psychology and neuroscience experiments.
   
Available Technologies
Publications Zaldivar, A., and Krichmar, J.L. (2014). Allen Brain Atlas-Driven Visualizations: A Web-Based Gene Expression Energy Visualization Tool. Frontiers in Neuroinformatics 8.

Bucci, L.D., Chou, T.-s., and Krichmar, J.L. (2014). Tactile Sensory Decoding in a Neuromorphic Interactive Robot. Paper presented at: 2014 IEEE Conference on Robotics & Automation (Hong Kong).

Asher, D.E., Krichmar, J.L., and Oros, N. (2014). Evolution of Biological Plausible Neural Networks Performing a Visually Guided Reaching Task. Paper presented at: Genetic and Evolutionary Computation Conference (GECCO) (Vancouver: ACM).

Carlson, K.D., Nageswaran, J.M., Dutt, N., and Krichmar, J.L. (2014). An efficient automated parameter tuning framework for spiking neural networks. Frontiers in Neuroscience 8(10).

Beyeler, M., Richert, M., Dutt, N.D., and Krichmar, J.L. (2014). Efficient spiking neural network model of pattern motion selectivity in visual cortex. Neuroinformatics.

Avery, M.C., Dutt, N., and Krichmar, J.L. (2014). Mechanisms underlying the basal forebrain enhancement of top-down and bottom-up attention. The European journal of neuroscience 39, 852-865.

Oros, N., Chiba, A.A., Nitz, D.A., Krichmar, J.L. (2014). Learning to ignore - A modeling study of the decremental cholinergic pathway and its influence on attention and learning. Learning and Memory, 21: 105-118.

Carlson, K.D., Beyeler, M., Dutt, N., and Krichmar, J.L. (2014). GPGPU Accelerated Simulation and Parameter Tuning for Neuromorphic Applications. Proceedings of the 19th Asia and South Pacific Design Automation Conference (ASP-DAC'14).

Asher*, D.A., Craig*, A.B., Zaldivar*, A., Brewer, A.A., and Krichmar, J.L. (2013). A dynamic, embodied paradigm to investigate the role of serotonin in cost and decision-making. Frontiers in Integrative Neuroscience 7(78). (*co-first authors)

Zaldivar, A., and Krichmar, J. (2013). Interactions between the neuromodulatory systems and the amygdala: exploratory survey using the Allen Mouse Brain Atlas. Brain Structure and Function, 218, 1513-1530.

Carlson, K.D., Richert, M., Dutt, N., and Krichmar, J.L. (2013). Biologically Plausible Models of Homeostasis and STDP: Stability and Learning in Spiking Neural Networks. Paper presented at: International Joint Conference on Neural Networks (Dallas, TX: IEEE Explore).

Krichmar, J.L., and Rohrbein, F. (2013). Value and Reward Based Learning in Neurorobots. Frontiers in neurorobotics 7.

Beyeler, M., Dutt, N.D., and Krichmar, J.L. (2013). Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule. Neural Networks 48, 109-124.

Craig, A.B., Asher, D.E., Oros, N., Brewer, A.A., and Krichmar, J.L. (2013). Social contracts and human-computer interaction with simulated adapting agents. Adaptive Behavior 21, 371-387.

Krichmar, J.L. (2013). A neurorobotic platform to test the influence of neuromodulatory signaling on anxious and curious behavior. Frontiers in neurorobotics 7, 1-17.
   
Research Center Center for Cognitive Neuroscience and Engineering
   
   
Link to this profile http://www.faculty.uci.edu/profile.cfm?faculty_id=5549
   
Last updated 08/15/2014