Cognitive Teaming Systems Jump to section
Decision Making and Conceptual Structure Jump to section
Neuroscience of Learning and Memory Jump to section

Center for Human Machine Collaboration (CHMC)

Is there a fundamental basis for human intelligence?  In the Center for Human-Machine Collaboration (CHMC) we are exploring this question through research that is focused on understanding the brain’s capability to learn, recall, and adapt to uncertainty. We apply our research by creating algorithms, tools, and systems to enhance learning, decision making, and intelligent behavior in machines.


Cognitive Teaming Systems

  • Human-Machine Symbiosis
  • Team Neurosynchrony
  • Cognitive Processing for Autonomous Systems

Current and Recent Projects

Cognitive Processing for Adaptive Decision Making

Autonomy is the key to growth and scalability of complex systems. Cognitive systems can supply the two key ingredients for this: 1) The ability to orient; that is, the ability to synthesize information observed and convert it into information that can be acted upon, creating a coherent manipulable view of the world, and 2) The ability to decide; that is, weight alternate options and select. Our objective is to research an approach to cognitive processing with a focus on hybrid technologies to create leverage toward achieving autonomous systems. The approach we developed utilizes inspiration from the brain: specifically, how the brain stores experiences in memory, generalizes semantics from memory, and reasons over it to produce decisions.

This work is funded through internal research funds.

Decision Making and Conceptual Structure

  • Neurocognitive Models of Sensemaking
  • Knowledge Representation

Current and Recent Projects

Integrated Cognitive Architectures for Understanding Sensemaking (ICArUS)

Sensemaking is a proactive form of situation awareness where noisy, sparse, and uncertain multimodal information is synthesized into explanatory and predictive hypotheses. Several collaborative modeling efforts spanned cognitive architectures using production systems and detailed neural models designed to reveal the neural principles and cognitive tradeoffs that examine human reasoning, decision-making, and cognitive bias.

Supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of the Interior (DOI) contract number D10PC20021 from December 2010 to November 2014.

Knowledge Represenation in Neural Systems (KRNS)

The KRNS program challenges researchers to provide and evaluate novel theories for knowledge representation by demonstrating their utility in interpreting neural activity to decode the concepts that generated it. In collaborations with several universities, we conducted research in widely varying semantic models as candidate representations for the brain, new spatiotemporal resolutions in fMRI BOLD neuroimaging to examine dynamic activity, and machine learning methods to extract the underlying neural representations of concepts.

Supported by the Intelligence Advanced Research Projects Activity (IARPA) via AFRL contract number FA8650-13-C-7356 from September 2013 to January 2015.

Neuroscience of Learning and Memory

  • Noninvasive, Intracranial, Neurostimulation and Recording
  • Memory Enhancement
  • Enhanced Learning of Complex Skills
  • Understanding Basis of Expertise
  • Memory Consolidation
  • Personalized Predictive Models of Behavior

Current and Recent Projects

Restoring Active Memory: Replay (RAM-REPLAY)

HRL is leading a team of 5 universities and 5 consultants to develop a man-portable, model-driven tCS technology that can accelerate skill acquisition and specific memory consolidation by closed-loop intervention during both waking and sleep. (DARPA BTO #W911NF-16-C-0018)

Transcranial Stimulation To Improve Memory (tSTIM)

HRL, with subcontractor McGill University, investigated the use of non-invasive stimulation to modulate single units and LFPs across multiple brain areas and thereby enhance memory function, in direct comparison with invasive stimulation methods, and also developed a predictive computational model of stimulation-enhanced short-term memory behavior. (DARPA BTO #N66001-14-C-4066)

Memory Enhancement by Modulation of Encoding Strength (MEMES)

HRL, with subcontractor McGill University, is investigating the effects of closed-loop multi-site non-invasive stimulation on long-term memory behavior and underlying neurophysiology in two neocortical areas and hippocampus. (DARPA BTO # N66001-16-C-4058)


Training methods to reach expert level performance is time-consuming and costly, while high physical/cognitive performance and skill mastery in workers/soldiers are in great demand. We strive to radically improve training with the use of novel neurostimulation approaches through an understanding of the neural basis of expertise in order to enable expert level performance for large fractions of the population by improving endogenous physical and cognitive processes. Our objective is to develop fundamental theories and techniques for neurostimulative/neuromodulatory interventions for cognitive and physical performance enhancement.

We are also looking into automated synthesis of software for complex cryptographic protocols.


Rajan Bhattacharyya

Jaehoon Choe

Vincent De Sapio

Mike Howard

Ryan Hubbard

Nick Ketz

Iman Mohammadrezazadeh

Aashish Patel

Praveen Pilly

Ruggero Scorcioni

Steven Skorheim

Kenji Yamada

eCortex, Inc.
Prof. Mark Schnitzer, Stanford University
Drs. Christian Lebiere, Prof. John R. Anderson and James Staszeqski, Carnegie Mellon University
Dr. Randall O'Reilly, University of Colorado Boulder
Profs. Stephen Grossberg and Gail Carpenter, Boston University
Prof. Jeff Krichmar, University of California Irvine
Prof. Giorgio Ascoli, George Mason University
Professor Vince Clark, University of New Mexico
Professor Chris Pack, McGill University


Click to show/hide the list of Papers

Authors Title Publication Year
Patel AN, Howard MD, Roach SM, Jones AP, Bryant NB, Robinson C, Clark V, Pilly PK Mental state assessment and validation using personalized physiological biometrics Frontiers in Human Neuroscience, 2018;12:221 June 2018
Ryan J. Hubbard, Nicholas A. Ketz, Aaron P. Jones, Bradley Robert, Natalie B. Bryant, Steven W. Skorheim, Shane Roach, Vincent P. Clark, Praveen K. Pilly Memory Reactivation with Neurostimulation during Sleep Elicits Electrophysiological Responses that Predict Behavioral Changes Presented at the 25th Annual Meeting of the Cognitive Neuroscience Society March 2018
S. M. Salas, R. P. Patrick, S. Roach, M. E. Phillips, N. D. Stepp, J. Cruz-Albrecht, V. De Sapio, T-C. Lu, V. Sritapan Neuromorphic and Early Warning Behavior- Based Authentication in Common Theft Scenarios In Proceedings of the 2017 IEEE International Symposium on Technologies for Homeland Security (HST) April 2017
Krause, M. R., Zanos, T. P., Csorba, B. A., Pilly, P. K., Choe, J., Phillips, M. E., Pack, C. C. Transcranial direct current stimulation facilitates associative learning and alters functional connectivity in the primate brain Current Biology, 27(20), 3086-3096 2017
M. E. Phillips, N. D. Stepp, J. Cruz-Albrecht, V. De Sapio, T-C. Lu, and V Sritapan Neuromorphic and early warning behavior-based authentication for mobile devices In Proceedings of the 2016 IEEE International Symposium on Technologies for Homeland Security (HST) May 2016
V. De Sapio, M. Howard, D. Korchev, R. Green, R. Gardner, and L. Bruchal Demographic specific musculoskeletal models of factory worker performance, fatigue, and injury In Proceedings of the 2016 IEEE International Aerospace Conference March 2016
Datta, M. R. Krause, P. K. Pilly, J. Choe, T. P. Zanos, C. Thomas, and C. C. Pack On comparing in vivo intracranial recordings in non-human primates to predictions of optimized transcranial electrical stimulation Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Orlando, FL. 2016
Choe, J., Coffman, B. A., Bergstedt, D. T., Ziegler, M. D., Phillips, M. E. Transcranial Direct Current Stimulation Modulates Neuronal Activity and Learning in Pilot Training Frontiers in human neuroscience, 10 2016
Phillips, M. E., Stepp, N. D., Cruz-Albrecht, J., De Sapio, V., Lu, T. C., Sritapan, V. Neuromorphic and Early Warning Behavior-based Authentication for Mobile Devices Technologies for Homeland Security (HST), 2016 IEEE Symposium on (pp. 1-5) IEEE May 2016
V. De Sapio, M. Howard, D. Korchev, R. Green, R. Gardner, L. Bruchal Demographic Specific Musculoskeletal Models of Factory Worker Performance, Fatigue, and Injury Proceedings of IEEE Aerospace Conference March 2016
Bhattacharyya R, Coffman BA, Choe J, Phillips ME Human Augmentation: Does Neurotechnology Make a Better Brain? IEEE Computer 2016
Vu, A. T., Phillips, J. S., Kay, K., Phillips, M. E., Johnson, M. R., Shinkareva, S. V., Bhattacharyya, R. Using Precise Word Timing Information Improves Decoding Accuracy in a Multiband-accelerated Multimodal Reading Experiment Cognitive Neuropsychology, 33(3-4), 265-275 2016
Craig, A. B., Phillips, M. E., Zaldivar, A., Bhattacharyya, R., Krichmar, J. L. Investigation of Biases and Compensatory Strategies Using a Probabilistic Variant of the Wisconsin Card Sorting Test Frontiers in psychology, 7 2016
Sun, Y., O’Reilly, R. C., Bhattacharyya, R., Smith, J. W., Liu, X., Wang, H. Latent Structure in Random Sequences Drives neural learning toward a rational bias Proceedings of the National Academy of Sciences, 112(12), 3788-3792 2015
Phillips ME, Phillips J, Tubridy SM, Johnson MR, Shinkareva SV, Millin R, Vu AT, Yacoub E, Gureckis T, Grossman M, Bhattacharyya R A neurosemantic behavioral feature model predicts conceptual representation in the brain Cognitive Neuroscience Society 2015 (Abstract accepted for full paper submission to Special Issue of Cognitive Neuropsychology) 2015
Ni, K., Benvenuto, J., Bhattacharyya, R., Millin, R. Feature Transformation of Neural Activity with Sparse and Low-Rank Decomposition Accepted for SPIE Proceedings 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging 2015
Vu AT, Phillips J, Kay K, Phillips ME, Johnson MR, Shinkareva SV, Millin R, Grossman M, Bhattacharyya R, Yacoub E.., Extracting sentence word order and timing information from a natural reading experiment using multiband accelerated fMRI OHBM 2015
Chelian, S. E., Paik, J., Pirolli, P., Lebiere, C., Bhattacharyya, R. Reinforcement learning and instance-based learning approaches to modeling human decision making in a prognostic foraging task Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (pp. 116-122) IEEE August 2015
M. Mansouri, V. De Sapio, and J. Reinbolt Prioritized task-based control of movement with supporting contacts using OpenSim and Matlab In Abstracts for the XV International Symposium on Computer Simulation in Biomechanics July 2015
Howard, M. D., Bhattacharyya, R., Chelian, S. E., Phillips, M. E., Pilly, P. K., Ziegler, M. D., Wang, H. The neural basis of decision-making during sensemaking: implications for human-system interaction IEEE Aerospace Conference (pp. 1-16) IEEE March 2015
V. De Sapio, N. Srinivasa A methodology for controlling motion and constraint forces in holonomically constrained systems Multibody System Dynamics, 33(2):179– 204 February 2015
V. De Sapio, D. Earl, R. Green, K. Saul Human factors simulation using demographically tuned biomechanical models In Proceedings of the 2014 International Annual Meeting of the Human Factors and Ergonomics Society, volume 58, pages 944– 948 October 2014
S. Goldfarb, D. Earl, V. De Sapio, M. Mansouri, J. Reinbolt An approach and implementation for coupling neurocognitive and neuromechanical models In Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics, pages 406–413 October 2014
S. Goldfarb, R. Bhattacharyya, V. De Sapio Coupled models of cognition and action: Behavioral phenotypes in the collective In Poster Session for Collective Intelligence 2014, MIT June 2014
V. De Sapio An approach for goal-oriented neuromuscular control of digital humans International Journal of Human Factors Modelling and Simulation, 4(2):121– 144 2014
O’Brien, M. J., Keegan, M. S., Goldstein, T., Millin, R., Benvenuto, J., Kay, K., Bhattacharyya, R. Sparse Atomic Feature Learning via Gradient Regularization: With Applications to Finding Sparse Representations of fMRI Activity Patterns IEEE Signal Processing in Medicine and Biology Symposium (SPMB) doi:10.1109/SPMB.2014.7002972 2014
Chelian, S. E., Ziegler, M. D., Pirolli, P., Bhattacharyya, R. Learning to prognostically forage in a neural network model of the interactions between neuromodulators and prefrontal cortex Procedia Computer Science, 41, 32-39 2014
Phillips, M., Ziegler, M. A neurostimulation-based advanced training system for human performance augmentation Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, 7(2), e11-e12 2014
Pilly, P. K. and Grossberg, S. How does the modular organization of entorhinal grid cells develop? Frontiers in Human Neuroscience, 8, 337 2014

Click to show/hide the list of Papers (Under Review)

Authors Title Publication Year
Jones AP, Choe J, Bryant NB, Robinson CSH, Ketz NA, Skorheim SW, Combs A, Lamphere ML, Robert B, Gill HA Heinrich MD, Howard MD, Clark VP, Pilly PK Closed-Loop tACS Delivered During Slow-Wave Sleep Enhances Consolidation of Generalized Information Submitted to Journal of Cognitive Neuroscience 2018
Ketz NA, Choe J, Jones AP, Bryant NB, Clark VP, Pilly PK Closed-loop slow wave tACS improves sleep dependent long term memory generalization by modulating endogenous oscillations Submitted Journal of Neuroscience Jan 2018
Pilly PK, Howard MD, Bhattacharyya R Contextual modulation of memory associations in the hippocampus Submitted to PLOS One 2018

Click to show/hide the list of Patents

Authors Title Patent # Date
V. De Sapio and D. .J. Earl Method and system for tuning a musculoskeletal model US 9,858,391 January 2, 2018
H. Hoffmann, D. W. Payton, V. De Sapio Method for tele-robotic operations over time-delayed communication links US 9,776,325 October 3, 2017
V. De Sapio, M. Howard, R. Green Quantifying muscle and tendon fatigue during physical exertion US 9,610,036 April 4, 2017
V. De Sapio, M. D. Howard, R. F. Green Quantifying muscle and tendon fatigue during physical exertion US 9,610,036 March 4, 2017
V. De Sapio, N. Srinivasa System for controlling brain machine interfaces and neural prosthetic systems US 9,566,174 February 14, 2017
M. Howard System and Method to discover and encode indirect associations in associative memory US 9,558,825 January 31, 2017
V. De Sapio, H. Hoffmann System for controlling motion and constraint forces in a robotic system US 9,364,951 June 14, 2016
M. Daily, M. Howard, Y. Chen, D. Payton, R. Sundareswara Recall system using spiking neuron networks US 9,020,870 April 28, 2015
M. Howard, R. Bhattacharyya System and Method for Adaptive Recall US 9,002,762 April 7, 2015
M. Howard, R. Bhattacharyya Framework for flexible cognitive perception and action selection US 8,990,139 March 24, 2015
M. Daily, M. Howard, Y. Chen, R. Sundareswara, D. Payton System for representing, storing, and reconstructing an input signal US 8,756,183 June 17, 2014



Upcoming Events

Workshop on Human Machine Systems

August 23-24, 2018 | HRL Laboratories - Malibu, CA

Past Events

October 11, 2017 | HRL Laboratories - Malibu, CA

Workshop on Memory Consolidation, Restoration, and Augmentation

Current Openings for CHMC

|Scientist IV – Autonomous Systems
|Scientist IV – Machine Learning and Cognitive Processing for Autonomous Systems
|Scientist IV Post Doc – Machine Learning and Cognitive Processing for Autonomous Systems


Email: rajan[at]

Dr. Rajan Bhattacharyya
Senior Research Engineer
Leader, Center for Human Machine Collaboration

Information and Systems Sciences Lab
HRL Laboratories, LLC
3011 Malibu Canyon Road
Malibu, CA 90265