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Los Alamos National Laboratory

Los Alamos National Laboratory

Delivering science and technology to protect our nation and promote world stability

Neuro, Cognitive Science

Los Alamos scientists are harnessing expertise in biology, chemistry, physics, computer science, mathematics, and even linguistics and psychology to better understand the nature of the human brain and how it works.

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How does the human brain work? And is it possible that humans in the future can program computers or other advanced machines to “think” like the human brain?

Is it possible that humans in the future can program computers or other advanced machines to “think” like the human brain?

How does the human brain work? And is it possible that humans in the future can program computers or other advanced machines to “think” like the human brain?

Los Alamos scientists are harnessing expertise in biology, chemistry, physics, computer science, mathematics, and even linguistics and psychology to better understand the nature of the human brain and how it works, particularly in how the brain represents, processes, and transforms information.

By better understanding such functions, it may be possible to design computers and machines that perform human-like tasks, such as visually recognizing objects.


Noninvasive functional neuroimaging
Analysis and source localization
Biomedical imaging
Advanced electroneural prosthesis
Modeling and simulation of large neural systems
Neuromimetic applications
Project Description

Neuro and cognitive sciences have the potential to reshape the scientific and technological landscape of the world, just as quantum physics, the human genome, and digital computing redefined the last. By applying disciplines in biology, chemistry, physics, computer science, and linguistics and psychology, researchers can advance the understanding of the architecture and dynamic function of the human brain...more...

Such understanding, combined with advances in materials and computer hardware and software, can provide a design path toward bio-cyber hybrid systems and neuromorphic systems. Such unbelievable advances could have applications that range from biomedicine to enforcing national security.

By better understanding the brain and how it works, scientists one day will be able to better diagnose, monitor, and treat debilitating conditions such as strokes, neurological and degenerative diseases, mental illnesses, and traumatic brain injuries. It may also be possible to ultimately mimic the function of neural systems for use in machines.

Technologies/Applications: Emerging, Developed, Potential
  • Pioneered ultra-low field magnetic resonance imaging, which enabled Los Alamos to be the first researchers ever to perform simultaneous magnetoencephalography (MEG) and ultra-low field magnetic resonance imaging of the human brain.
  • Used superconducting quantum interference devices (SQUIDs) to perform whole-head MEG and identify regions of the brain where neural activity is present. This whole-head sensor array incorporated a structure known as a Superconducting Imaging Surface (SIS). SIS is an effective magnetic shielding system within the instrument itself that enables powerful adaptive noise-cancellation strategies that greatly reduce performance requirements for external magnetic shielding.
  • Created PetaVision, a functional model of the visual cortex that enables petascale simulation of mammalian vision. The PetaVision team used its massively parallel simulation on the Roadrunner supercomputer—in the process, the team set a world speed record for scientific computation of 1.144 petaflops.
  • Invented MagViz, a product based on a new form of magnetic resonance imaging that uses ultralow magnetic fields. Applications for this technology include (1) detecting liquid explosives in airport carry-on luggage, (2) medical imaging for patients for whom traditional MRI is unsafe (such as people with active medical implants or pregnant women), and (3) quality control on production lines. This technology received an R&D100 Award in 2009.
  • Developed MRIVIEW, an interactive computational tool designed to help scientists investigate brain structure and function. Key features of the software include semi-automated segmentation of volumetric head data, as well as an interactive coordinate reconciliation method that uses surface visualization.
  • Developed MEGAN, a software package for MEG (mapping brain activity by recording magnetic fields produced by electrical currents that occur naturally in the brain) and electroencephalography (recording electrical activity along the scalp) analysis and visualization.
  • Collaborated with Princeton and Indiana universities to develop a novel methodology designed to “see” the world through the eyes of a common fly and partially decode the insect’s reactions to changes in the world around it. This work fundamentally altered earlier beliefs about how neural networks function and could provide the basis for intelligent computers that mimic biological processes.
  • Collaborated with Harvard to pioneer the feasibility of functional Magnetic Resonance Imaging (fMRI) using conventional clinical imagers. Such measurements are based on changes in blood flow and blood oxygenation that accompany brain function and are orders of magnitude shorter than the underlying currents that occur on the timescale of tens of hundreds of milliseconds. Los Alamos researchers were also among the first to recognize the possibility of direct measurements of neural currents by MRI, and this work continues to this day.
  • Pioneered the application of optical methods to monitor neural function, including noninvasive spectroscope methods designed to measure blood flow, methods to perform fluorescence imaging of cytoplasmic calcium changes associated with activation in neurons, and demonstrating the feasibility of imaging fast intrinsic optical responses associated with neural activation.
  • Created NeuralViz, computer software designed to mimic the brain’s ability to interpret what the eyes can see. This technology has applications in security, safety, and inspection areas; aerial/satellite remote sensing for defense; intelligence applications; and “vision” for autonomous robots.
  • Collaborated with Chatham and Emory universities to “teach” computers to recognize objects. Researchers created a computer model based on human neural structure and function to simulate what human eyes and brains can do, and possibly such a model can perform such recognition faster and better.
LANL Facilities
  • The Superconductivity Center: Scientists at this facility perform research, development, and technology transfer related to high-temperature superconductivity. Work includes powder synthesis, tape/coil processing, thin/thick film deposition, power cryogenic engineering, and prototype devices. In the biomedical field, superconductivity will one day play a role in MRI (a noninvasive method for seeing inside the body without using ionizing radiation) and Magnetic Source Imaging, or MSI (where SQUIDs play a key role—SQUIDs are ideal for detecting small magnetic fields). Key work in this area involves the development of better high-temperature superconductor tapes.
  • The Center for Nonlinear Studies. Formed in October 1980, this center is designed for researchers to conduct work related to nonlinear and complex systems phenomena. Biological research includes neural computation, determining the information-processing properties of the brain, and the dynamics of complex networks.
Key Personnel
  • John Compton Mosher: Neuroscience and magnetoencephalography
  • Michelle A. Espy: SQUIDs, MEG, and neuroscience
  • Garrett Kenyon: Neurophysics and visual cortex
  • Steven P. Brumby and John Stevens George: computer vision
  • Douglas M. Ranken: Computer-driven brain mapping
Sponsors, Funding Sources, or Agencies
  • Department of Energy’s Office of Science
  • National Institutes of Health
  • 2009 R&D100 Award for MagViz.
  • 2009 R&D100 Award for the Artificial Retina Project.
2011 - Ruilian Wu, Pete Silks, John C. Gordon, Ryszard Michalczyk, and Cliff Unkefer

“Catalyzed conversion of non-food biomass to fuels and chemicals: Use of algal and carbohydrate feedstocks,”

ACS National Meeting Book of Abstracts.

2012 - Yosuke Kakisaka, Zhong I. Wang, John C. Mosher, Anne-Sophie Dubarry, Andreas V. Alexopoulos, Rei Enatsu, Prakash Kotagal, and Richard C. Burgess, “Clinical evidence for the utility of movement compensation algorithm in magnetoencephalography: Successful localization during focal seizure,” Epilepsy Research 101(1-2), 191–196 (2012).

2012 - Z.I. Wang, S.E. Jones, A.J. Ristic, C. Wong, Y. Kakisaka, K. Jin, F. Schneider, J.A. Gonzalez-Martinez, J.C. Mosher, and D. Nair, et al., “Voxel-based morphometric MRI post-processing in MRI-negative focal cortical dysplasia followed by simultaneously recorded MEG and stereo EEG,” Epilepsy Research 100(1-2), 188–193 (2012).

2012 - Felix Schneider, Andreas V. Alexopoulos, Zhong Wang, Salah Almubarak, Yosuke Kakisaka, Kazutaka Jin, Dileep Nair, John C. Mosher, Imad M. Najm, and Richard C. Burgess, “Magnetic source imaging in non-lesional neocortical epilepsy: Additional value and comparison with ICEEG,” Epilepsy and Behavior 24(2), 234–240 (2012).

2012 - Yosuke Kakisaka, Zhong I. Wang, John C. Mosher, Dileep R. Nair, Andreas V. Alexopoulos, and Richard C. Burgess, “Magnetoencephalography’s higher sensitivity to epileptic spikes may elucidate the profile of electroencephalographically negative epileptic seizures,” Epilepsy and Behavior 23(2), 171–173 (2012).

2012 - Yosuke Kakisaka, Norman K. So, Stephen E. Jones, Zhong I. Wang, John C. Mosher, Andreas V. Alexopoulos, and Richard C. Burgess, “Intractable focal epilepsy contralateral to the side of facial atrophy in Parry-Romberg syndrome,” Neurological Sciences 33(1), 165–168 (2012).

2012 - Rei Enatsu, Zhe Piao, Timothy O’Connor, Karl Horning, John Mosher, Richard Burgess, William Bingaman, and Dileep Nair, “Cortical excitability varies upon ictal onset patterns in neocortical epilepsy: A cortico-cortical evoked potential study,” Clinical Neurophysiology 123(2), 252–260 (2012).

2012 - Rei Enatsu, Yuichi Kubota, Yosuke Kakisaka, Juan Bulacio, Zhe Piao, Timothy O’Connor, Karl Horning, John Mosher, Richard C. Burgess, and William Bingaman, et al.,
”Reorganization of posterior language area in temporal lobe epilepsy: A cortico-cortical evoked potential study,” Epilepsy Research (2012).

2012 - Yosuke Kakisaka, Masaki Iwasaki, Andreas V. Alexopoulos, Rei Enatsu, Kazutaka Jin, Zhong I. Wang, John C. Mosher, Anne-Sophie Dubarry, Dileep R. Nair, and Richard C. Burgess, “Magnetoencephalography in fronto-parietal opercular epilepsy,” Epilepsy Research (2012).

2012 - Yosuke Kakisaka, John C. Mosher, Zhong I. Wang, Kazutaka Jin, Anne-Sophie Dubarry, Andreas V. Alexopoulos, and Richard C. Burgess, “Utility of temporally-extended signal space separation algorithm for magnetic noise from vagal nerve stimulators,” Clinical Neurophysiology (2012).

2012 - Yosuke Kakisaka, Ajay Gupta, Zhong I. Wang, Anne-Sophie Dubarry, Andreas V. Alexopoulos, John C. Mosher, and Richard C. Burgess, “Different cortical involvement pattern of generalized and localized spasms: A magnetoencephalography study,” Epilepsy and Behavior 22(3), 599–601 (2012).

2011 - Yosuke Kakisaka, Andreas V. Alexopoulos, Ajay Gupta, Zhong I. Wang, John C. Mosher, Masaki Iwasaki, and Richard C. Burgess, “Generalized 3-Hz spike-and-wave complexes emanating from focal epileptic activity in pediatric patients,” Epilepsy and Behavior 20(1), 103–106 (2011).

2011 - Syed Ashrafulla, Dimitrios Pantazis, John Mosher, Matti Hämäläinen, Brent Liu, and Richard M. Leahy, “Viability of sharing MEG data using minimum-norm imaging,” in Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 7967 (2011).

2011 - Franois Tadel, Sylvain Baillet, John C. Mosher, Dimitrios Pantazis, and Richard M. Leahy, “Brainstorm: A user-friendly application for MEG/EEG analysis,” Computational Intelligence and Neuroscience (2011).

2010 - S. Baillet, F. Tadel, R.M. Leahy, J.C. Mosher, A. Delorme, S. Makeig, R. Oostenveld, M. Hämäläinen, S.S. Dalal, and J. Zumer, et al., “Academic software toolboxes for the analysis of MEG data,” IFMBE Proceedings 28, 101–104 (2010).

2012 - Gennady P. Berman, Michelle A. Espy, Vyacheslav N. Gorshkov, Vladimir I. Tsifrinovich, and Petr L. Volegov, “Radiation damping for speeding up NMR applications,” Concepts in Magnetic Resonance Part A: Bridging Education and Research 40 A(4), 179–185 (2012).

2012 - A.N. Matlashov, E. Burmistrov, P.E. Magnelind, L. Schultz, A.V. Urbaitis, P.L. Volegov, J. Yoder, and M.A. Espy, “SQUID-based systems for co-registration of ultra-low field nuclear magnetic resonance images and magnetoencephalography,” Physica C: Superconductivity and its Applications (2012).

2011 - I. Savukov, T. Karaulanov, A. Castro, P. Volegov, A. Matlashov, A. Urbatis, J. Gomez, and M. Espy, “Non-cryogenic anatomical imaging in ultra-low field regime: Hand MRI demonstration,” Journal of Magnetic Resonance 211(2), 101–108 (2011).

2011 - Henrik J. Sandin, Petr L. Volegov, Michelle A. Espy, Andrei N. Matlashov, Igor M. Savukov, and Larry J. Schultz, “Noise modeling from conductive shields using Kirchhoff equations,” IEEE Transactions on Applied Superconductivity 21(3 PART 1), 489–492 (2011).

2011 - Michelle Espy, Shermiyah Baguisa, David Dunkerley, Per Magnelind, Andrei Matlashov, Tuba Owens, Henrik Sandin, Igor Savukov, Larry Schultz, and Algis Urbaitis, et al., “Progress on detection of liquid explosives using ultra-low field MRI,” IEEE Transactions on Applied Superconductivity 21(3 PART 1), 530–533 (2011).

2011 - Andrei N. Matlashov, Larry J. Schultz, Michelle A. Espy, Robert H. Kraus, Igor M. Savukov, Petr L. Volegov, and Caroline J. Wurden, “SQUIDS vs. induction coils for ultra-low field nuclear magnetic resonance: Experimental and simulation comparison,” IEEE Transactions on Applied Superconductivity 21(3 PART 1), 465–468 (2011).

2011 - Per E. Magnelind, John J. Gomez, Andrei N. Matlashov, Tuba Owens, J. Henrik Sandin, Petr L. Volegov, and Michelle A. Espy, “Co-registration of interleaved MEG and ULF MRI using a 7 channel low-T c SQUID system,” IEEE Transactions on Applied Superconductivity 21(3 PART 1), 456–460 (2011).

2010 - Vadim S. Zotev, Tuba Owens, Andrei N. Matlashov, Igor M. Savukov, John J. Gomez, and Michelle A. Espy, “Microtesla MRI with dynamic nuclear polarization,” Journal of Magnetic Resonance 207(1), 78–88 (2010).

2010 - M. Espy, M. Flynn, J. Gomez, C. Hanson, R. Kraus, P. Magnelind, K. Maskaly, A. Matlashov, and S. Newman, et al., “Ultra-low-field MRI for the detection of liquid explosives,” Superconductor Science and Technology 23(3) (2010).

2012 - D.M. Paiton, S.P. Brumby, G.T. Kenyon, G.J. Kunde, K.D. Peterson, M.I. Ham, P.F. Schultz, and J.S. George, “Combining multiple visual processing streams for locating and classifying objects in video,” in Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, 49–52 (2012).

2012 - Peter F. Schultz, Luis M. Bettencourt, and Garrett T. Kenyon, “A symmetry-breaking generative model of a simple-cell/complex-cell hierarchy,” in Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, 89–92 (2012).

2012 - Wentao Huang, Zhengping Ji, Steven P. Brumby, Garrett Kenyon, and Luis M. A. Bettencourt,” Development of invariant feature maps via a computational model of simple and complex cells,” in Proceedings of the International Joint Conference on Neural Networks (2012).

2011 - Vadas Gintautas, Michael I. Ham, Benjamin Kunsberg, Shawn Barr, Steven P. Brumby, Craig Rasmussen, John S. George, Ilya Nemenman, Luís M. A. Bettencourt, and Garret T. Kenyon, “Model cortical association fields account for the time course and dependence on target complexity of human contour perception,” PLoS Computational Biology 7(10) (2011).

2011 - P.N. Loxley, L.M. Bettencourt, and G.T. Kenyon, “Ultra-fast detection of salient contours through horizontal connections in the primary visual cortex,” EPL 93(6) (2011).

2011 - Zhengping Ji, Wentao Huang, Garrett Kenyon, and Luis M.A. Bettencourt, “Hierarchical discriminative sparse coding via bidirectional connections,” in Proceedings of the International Joint Conference on Neural Networks, 2844–2851 (2011).

2012 - Garrett T. Kenyon, “Extreme synergy: Spatiotemporal correlations enable rapid image reconstruction from computer-generated spike trains,” Journal of Vision 10(3), 1–27 (2010).

2010 - Craig Rasmussen, Garrett Kenyon, and Matthew Sottile, “Visual language recognition with a feed-forward network of spiking neurons,” Proc. of the IADIS Int. Conf. Intelligent Systems and Agents 2010, Proc. of the IADIS European Conference on Data Mining 2010, Part of the MCCSIS, 103–108 (2010).

2012 - Zhengping Ji, Wentao Huang, and Steven P. Brumby, “Learning sparse representation via a nonlinear shrinkage encoder and a linear sparse decoder,” in Proceedings of the International Joint Conference on Neural Networks (2012).

2011 - Steven P. Brumby, “Image fusion for remote sensing using fast, large-scale neuroscience models,” in Proceedings of SPIE - The International Society for Optical Engineering, 8064 (2011).

2009 - Leon Heller, Benjamin E. Barrowes, and John S. George, “Modeling direct effects of neural current on MRI,” Human Brain Mapping 30(1), 1–12 (2009).

2012 - C.J. Aine, L. Sanfratello, D. Ranken, E. Best, J.A. MacArthur, T. Wallace, K. Gilliam, C.H. Donahue, R. Montana, and J.E. Bryant, et al., “MEG-SIM: A web portal for testing MEG analysis methods using realistic simulated and empirical data,” Neuroinformatics 10(2), 141–158 (2012).

2011 - Sanja Josef Golubic, Ana Susac, Veljko Grilj, Douglas Ranken, Ralph Huonker, Jens Haueisen, and Selma Supek, “Size matters: MEG empirical and simulation study on source localization of the earliest visual activity in the occipital cortex,” Medical and Biological Engineering and Computing 49(5), 545–554 (2011).

2011 - Ana Susac, Risto J. Ilmoniemi, Doug Ranken, and Selma Supek, “Face activated neurodynamic cortical networks,” Medical and Biological Engineering and Computing 49(5), 531–543 (2011).

2010 - Ana Susac, Risto J. Ilmoniemi, Elina Pihko, Doug Ranken, and Selma Supek, “Early cortical responses are sensitive to changes in face stimuli,” Brain Research 1346, 155-164 (2010).

2010 - C.J. Aine, J.E. Bryant, J.E. Knoefel, J.C. Adair, B. Hart, C.H. Donahue, R. Montaño, R. Hayek, C. Qualls, and D. Ranken, et al., “Different strategies for auditory word recognition in healthy versus normal aging,” NeuroImage 49(4), 3319–3330 (2010).

2010 - Julia M. Stephen, Rebecca Montaño, Christopher H. Donahue, John C. Adair, Janice Knoefel, Clifford Qualls, Blaine Hart, Doug Ranken, and Cheryl J. Aine, “Somatosensory responses in normal aging, mild cognitive impairment, and Alzheimer’s disease,” Journal of Neural Transmission 117(2), 217–225 (2010).

2010 - Lori Sanfratello, Julia M. Stephen, Douglas Ranken, Elaine Best, Theodore Wallace, Jason MacArthur, Katie Gilliam, and Cheryl J. Aine, “MEG-SIM portal: Reconstructions from realistic simulations of sensory and cognitive processing,” IFMBE Proceedings 28, 132–135 (2010).


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