Jeffrey L. Krichmar
Professor, Cognitive Sciences
School of Social Sciences
School of Social Sciences
Ph.D., George Mason University, 1997, Computational Sciences and Informatics
University of California, Irvine
2328 Social and Behavioral Sciences Gateway
Mail Code: 5100
Irvine, CA 92697
2328 Social and Behavioral Sciences Gateway
Mail Code: 5100
Irvine, CA 92697
Research Interests
computational neuroscience, robotics
Research Abstract
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
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
Available Technologies
Publications
Krichmar, J.L. (2018). Neurorobotics—A Thriving Community and a Promising Pathway Toward Intelligent Cognitive Robots. Frontiers in neurorobotics 12.
Hwu, T., Wang, A.Y., Oros, N., and Krichmar, J.L. (2018). Adaptive Robot Path Planning Using a Spiking Neuron Algorithm With Axonal Delays. IEEE Transactions on Cognitive and Developmental Systems 10, 126-137.
Rounds, E.L., Alexander, A.S., Nitz, D.A., and Krichmar, J.L. (2018). Conjunctive coding in an evolved spiking model of retrosplenial cortex. Behav Neuroscience. DOI: 10.1037/bne0000236.
Tang, H., Huang, T., Krichmar, J.L., Orchard, G., and Basu, A. (2018). Guest Editorial Special Issue on Neuromorphic Computing and Cognitive Systems. IEEE Transactions on Cognitive and Developmental Systems 10, 122-125.
Venkadesh, S., Komendantov, A.O., Listopad, S., Scott, E.O., De Jong, K., Krichmar, J.L., and Ascoli, G.A. (2018). Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types. Frontiers in Neuroinformatics 12.
Das, A., Pradhapan, P., Groenendaal, W., Adiraju, P., Rajan, R.T., Catthoor, F., Schaafsma, S., Krichmar, J.L., Dutt, N., and Van Hoof, C. (2018). Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout. Neural Netw 99, 134-147.
Avery, M.C., and Krichmar, J.L. (2017). Neuromodulatory Systems and Their Interactions: A Review of Models, Theories, and Experiments. Frontiers in Neural Circuits 11.
Beyeler, M., Rounds, E.L., Carlson, K.D., Dutt, N., and Krichmar, J.L. (2017). Sparse coding and dimensionality reduction in cortex. bioRxiv, DOI: 10.1101/149880.
Craig, A.B., Grossman, E., and Krichmar, J.L. (2017). Investigation of autistic traits through strategic decision-making in games with adaptive agents. Scientific Reports 7, 5533, DOI:10.1038/s41598-017-05933-6.
Oess, T., Krichmar, J.L., and Röhrbein, F. (2017). A Computational Model for Spatial Navigation Based on Reference Frames in the Hippocampus, Retrosplenial Cortex and Posterior Parietal Cortex. Frontiers in neurorobotics.
Hwu, T., Isbell, J., Oros, N., and Krichmar, J. (2016). A Self-Driving Robot Using Deep Convolutional Neural Networks on Neuromorphic Hardware. arXiv arXiv:1611.01235 [cs.NE].
Beyeler, M., Dutt, N., and Krichmar, J.L. (2016). 3D Visual Response Properties of MSTd Emerge from an Efficient, Sparse Population Code. The Journal of Neuroscience 36, 8399-8415.
Craig, A.B., Phillips, M.E., Zaldivar, A., Bhattacharyya, R., and Krichmar, J.L. (2016). Investigation of biases and compensatory strategies using a probabilistic variant of the Wisconsin Card Sorting Test. Frontiers in Psychology 7:17.
Krichmar, J.L., Conradt, J., and Asada, M. (2015). Neurobiologically Inspired Robotics: Enhanced Autonomy through Neuromorphic Cognition. Neural Networks, 72, 1-2.
Beyeler, M., Oros, N., Dutt, N., and Krichmar, J.L. (2015). A GPU-accelerated cortical neural network model for visually guided robot navigation. Neural Networks, 72, 75-87.
Asher, D.E., Oros, N., and Krichmar, J.L. (2015). The Importance of Lateral Connections in the Parietal Cortex for Generating Motor Plans. PLoS ONE 10, e0134669.
Chou, T.-S., Bucci, L.D., and Krichmar, J.L. (2015). Learning Touch Preferences with a Tactile Robot Using Dopamine Modulated STDP in a Model of Insular Cortex. Frontiers in Neurorobotics 9.
Avery, M., and Krichmar, J.L. (2015). Improper activation of D1 and D2 receptors leads to excess noise in prefrontal cortex. Frontiers in Computational Neuroscience Vol. 9, Article 31, 1-15.
Krichmar, J.L., Coussy, P., and Dutt, N. (2015). Large-Scale Spiking Neural Networks using Neuromorphic Hardware Compatible Models. ACM Journal on Emerging Technologies in Computing Systems, Vol. 11, No. 4, Article 36, 1-18.
Zaldivar, A., and Krichmar, J.L. (2014). Allen Brain Atlas-Driven Visualizations: A Web-Based Gene Expression Energy Visualization Tool. Frontiers in Neuroinformatics 8.
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.
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., and Krichmar, J.L. (2014). Learning to ignore: A modeling study of a decremental cholinergic pathway and its influence on attention and learning. Learning & Memory 21, 105-118.
Hwu, T., Wang, A.Y., Oros, N., and Krichmar, J.L. (2018). Adaptive Robot Path Planning Using a Spiking Neuron Algorithm With Axonal Delays. IEEE Transactions on Cognitive and Developmental Systems 10, 126-137.
Rounds, E.L., Alexander, A.S., Nitz, D.A., and Krichmar, J.L. (2018). Conjunctive coding in an evolved spiking model of retrosplenial cortex. Behav Neuroscience. DOI: 10.1037/bne0000236.
Tang, H., Huang, T., Krichmar, J.L., Orchard, G., and Basu, A. (2018). Guest Editorial Special Issue on Neuromorphic Computing and Cognitive Systems. IEEE Transactions on Cognitive and Developmental Systems 10, 122-125.
Venkadesh, S., Komendantov, A.O., Listopad, S., Scott, E.O., De Jong, K., Krichmar, J.L., and Ascoli, G.A. (2018). Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types. Frontiers in Neuroinformatics 12.
Das, A., Pradhapan, P., Groenendaal, W., Adiraju, P., Rajan, R.T., Catthoor, F., Schaafsma, S., Krichmar, J.L., Dutt, N., and Van Hoof, C. (2018). Unsupervised heart-rate estimation in wearables with Liquid states and a probabilistic readout. Neural Netw 99, 134-147.
Avery, M.C., and Krichmar, J.L. (2017). Neuromodulatory Systems and Their Interactions: A Review of Models, Theories, and Experiments. Frontiers in Neural Circuits 11.
Beyeler, M., Rounds, E.L., Carlson, K.D., Dutt, N., and Krichmar, J.L. (2017). Sparse coding and dimensionality reduction in cortex. bioRxiv, DOI: 10.1101/149880.
Craig, A.B., Grossman, E., and Krichmar, J.L. (2017). Investigation of autistic traits through strategic decision-making in games with adaptive agents. Scientific Reports 7, 5533, DOI:10.1038/s41598-017-05933-6.
Oess, T., Krichmar, J.L., and Röhrbein, F. (2017). A Computational Model for Spatial Navigation Based on Reference Frames in the Hippocampus, Retrosplenial Cortex and Posterior Parietal Cortex. Frontiers in neurorobotics.
Hwu, T., Isbell, J., Oros, N., and Krichmar, J. (2016). A Self-Driving Robot Using Deep Convolutional Neural Networks on Neuromorphic Hardware. arXiv arXiv:1611.01235 [cs.NE].
Beyeler, M., Dutt, N., and Krichmar, J.L. (2016). 3D Visual Response Properties of MSTd Emerge from an Efficient, Sparse Population Code. The Journal of Neuroscience 36, 8399-8415.
Craig, A.B., Phillips, M.E., Zaldivar, A., Bhattacharyya, R., and Krichmar, J.L. (2016). Investigation of biases and compensatory strategies using a probabilistic variant of the Wisconsin Card Sorting Test. Frontiers in Psychology 7:17.
Krichmar, J.L., Conradt, J., and Asada, M. (2015). Neurobiologically Inspired Robotics: Enhanced Autonomy through Neuromorphic Cognition. Neural Networks, 72, 1-2.
Beyeler, M., Oros, N., Dutt, N., and Krichmar, J.L. (2015). A GPU-accelerated cortical neural network model for visually guided robot navigation. Neural Networks, 72, 75-87.
Asher, D.E., Oros, N., and Krichmar, J.L. (2015). The Importance of Lateral Connections in the Parietal Cortex for Generating Motor Plans. PLoS ONE 10, e0134669.
Chou, T.-S., Bucci, L.D., and Krichmar, J.L. (2015). Learning Touch Preferences with a Tactile Robot Using Dopamine Modulated STDP in a Model of Insular Cortex. Frontiers in Neurorobotics 9.
Avery, M., and Krichmar, J.L. (2015). Improper activation of D1 and D2 receptors leads to excess noise in prefrontal cortex. Frontiers in Computational Neuroscience Vol. 9, Article 31, 1-15.
Krichmar, J.L., Coussy, P., and Dutt, N. (2015). Large-Scale Spiking Neural Networks using Neuromorphic Hardware Compatible Models. ACM Journal on Emerging Technologies in Computing Systems, Vol. 11, No. 4, Article 36, 1-18.
Zaldivar, A., and Krichmar, J.L. (2014). Allen Brain Atlas-Driven Visualizations: A Web-Based Gene Expression Energy Visualization Tool. Frontiers in Neuroinformatics 8.
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.
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., and Krichmar, J.L. (2014). Learning to ignore: A modeling study of a decremental cholinergic pathway and its influence on attention and learning. Learning & Memory 21, 105-118.
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.
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.
Professional Societies
IEEE - Senior Member
Society For Neuroscience
Research Centers
Center for Cognitive Neuroscience and Engineering
Link to this profile
https://faculty.uci.edu/profile/?facultyId=5549
https://faculty.uci.edu/profile/?facultyId=5549
Last updated
10/16/2018
10/16/2018