James Aimone

Cognitive & Emerging Computing

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Cognitive & Emerging Computing

jbaimon@sandia.gov

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(505) 249-1762

Sandia National Laboratories, New Mexico
 P.O. Box 5800
 Albuquerque, NM 87185-1327

Biography

Dr. Brad Aimone is a Distinguished Member of Technical Staff in the Center for Computing Research at Sandia National Laboratories, where he is a lead researcher in leveraging computational neuroscience to advance artificial intelligence and in using neuromorphic computing platforms for future scientific computing applications. Brad currently leads several research efforts on designing neural algorithms for scientific computing applications and neuromorphic machine learning implementations.

Brad has published over seventy peer-reviewed journal and conference articles in venues such as Advanced Materials, Neuron, Nature Machine Intelligence, Nature Neuroscience, Nature Electronics, Communications of the ACM, and PNAS and he is one of the co-founders of the Neuro-Inspired Computational Elements, or NICE, conference, and he led the team that won the 2023 Misha Mahowald Prize in Neuromorphic Engineering. Prior to joining the technical staff at Sandia in 2011, Dr. Aimone was a postdoctoral research associate at the Salk Institute for Biological Studies, with a Ph.D. in computational neuroscience from the University of California, San Diego and Bachelor’s and Master’s degrees in chemical engineering from Rice University. 

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Education

Ph.D. Computational Neuroscience, University of California, San Diego
Thesis title: “Computational modeling of adult neurogenesis in the dentate gyrus”		2009
Masters of Chemical Engineering, Rice University, Houston2002
BS in Chemical Engineering, Rice University, Houston2001

Sandia Publications (post 2011)

  • 2024
  • Wang, F., Kulkarni, S., Theilman, B.H., Rothganger, F., Schuman, C., Lim, S.H., & Aimone, J.B. (2024). Scaling neural simulations in STACS. Neuromorphic Computing and Engineering, 4(2). https://doi.org/10.1088/2634-4386/ad3be7 Publication ID: 124460
  • Theilman, B.H., Zhang, Q., Kahana, A., Cyr, E.C., Trask, N., Aimone, J.B., & Karniadakis, G.E. (2024). Spiking Physics-Informed Neural Networks on Loihi-2 [Conference Proceeding]. https://doi.org/10.1109/NICE61972.2024.10548180 Publication ID: 151436
  • 2023
  • Aimone, J.B., & Parekh, O.D. (2023). The brain’s unique take on algorithms. Nature Communications, 14(1). https://doi.org/10.1038/s41467-023-40535-z Publication ID: 122476
  • Aimone, J.B., & Misra, S. (2023). Will Stochastic Devices Play Nice With Others in Neuromorphic Hardware?: There’s More to a Probabilistic System Than Noisy Devices. IEEE Electron Devices Magazine, 1(2). https://doi.org/10.1109/med.2023.3298873 Publication ID: 123164
  • Awile, O., Knight, J.C., Nowotny, T., Aimone, J.B., Diesmann, M., & Schurmann, F. (2023). Editorial: Neuroscience, computing, performance, and benchmarks: Why it matters to neuroscience how fast we can compute. Frontiers in Neuroinformatics, 17. https://doi.org/10.3389/fninf.2023.1157418 Publication ID: 123936
  • 2022
  • Aimone, J.B., Date, P., Fonseca-Guerra, G.A., Hamilton, K.E., Henke, K., Kay, B., Kenyon, G.T., Kulkarni, S.R., Parsa, M., Schuman, C.D., Severa, W.M., & Smith, J.D. (2022). A review of non-cognitive applications for neuromorphic computing. Neuromorphic Computing and Engineering, 2(3). https://doi.org/10.1088/2634-4386/ac889c Publication ID: 106596
  • Vineyard, C.M., Cardwell, S.G., Chance, F.S., Musuvathy, S.S., Rothganger, F., Severa, W.M., Smith, J.D., Teeter, C.M., Wang, F., & Aimone, J.B. (2022). Neural Mini-Apps as a Tool for Neuromorphic Computing Insight [Conference Proceeding]. ACM International Conference Proceeding Series. https://doi.org/10.1145/3517343.3517353 Publication ID: 108276
  • Smith, J.D., Hill, A., Reeder, L.E., Franke, B.C., Lehoucq, R.B., Parekh, O.D., Severa, W.M., & Aimone, J.B. (2022). Neuromorphic scaling advantages for energy-efficient random walk computations. Nature Electronics, 5(2), pp. 102-112. https://doi.org/10.1038/s41928-021-00705-7 Publication ID: 80181
  • 2020
  • Aimone, J.B. (2020). A Roadmap for Reaching the Potential of Brain-Derived Computing. Advanced Intelligent Systems, 3(1). https://doi.org/10.1002/aisy.202000191 Publication ID: 71266
  • Chance, F.S., Aimone, J.B., Musuvathy, S.S., Smith, M.R., Vineyard, C.M., & Wang, F. (2020). Crossing the Cleft: Communication Challenges Between Neuroscience and Artificial Intelligence. Frontiers in Computational Neuroscience, 14(39). https://doi.org/10.3389/fncom.2020.00039 Publication ID: 73290
  • 2019
  • Green, S., & Aimone, J.B. (2019). Memristors learn to play. Nature Electronics, 2(3), pp. 96-97. https://doi.org/10.1038/s41928-019-0224-3 Publication ID: 67576
  • 2018
  • Quach, T.T., Agarwal, S., James, C.D., Marinella, M., & Aimone, J.B. (2018). Sparse Data Acquisition on Emerging Memory Architectures. IEEE Access, 7, pp. 1685-1693. https://doi.org/10.1109/ACCESS.2018.2886931 Publication ID: 60568
  • Wang, F., Quach, T.T., Wheeler, J., Aimone, J.B., & James, C.D. (2018). Sparse coding for N-gram feature extraction and training for file fragment classification. IEEE Transactions on Information Forensics and Security, 13(10), pp. 2553-2562. https://doi.org/10.1109/TIFS.2018.2823697 Publication ID: 61351
  • Verzi, S.J., Rothganger, F., Parekh, O.D., Quach, T.T., Miner, N.E., Vineyard, C.M., James, C.D., & Aimone, J.B. (2018). Computing with spikes: The advantage of fine-grained timing. Neural Computation, 30(10), pp. 2660-2690. https://doi.org/10.1162/neco_a_01113 Publication ID: 63758
  • 2017
  • Naegle, J.H., Suppona, R.A., Aimone, J.B., James, C.D., Follett, D.R., Townsend, D., Follett, P.L., & Karpman, G.D. (2017). Neuromorphic data microscope. ACM International Conference Proceeding Series, 2017-July. https://doi.org/10.1145/3183584.3183617 Publication ID: 58038
  • Aimone, J.B. (2017). Exponential scaling of neural algorithms – a future beyond Moore’s Law?. bioRxiv. https://www.osti.gov/biblio/1429770 Publication ID: 56135
  • Aimone, J.B. (2017). The Computing World is Ready for Brain Inspiration – But what will be the Biggest Impact?. R&D Magazine. https://www.osti.gov/biblio/1429769 Publication ID: 56134
  • Severa, W.M., Parekh, O.D., James, C.D., & Aimone, J.B. (2017). A combinatorial model for dentate gyrus sparse coding. Neural Computation, 29(1), pp. 94-117. https://doi.org/10.1162/NECO_a_00905 Publication ID: 51741
  • James, C.D., Aimone, J.B., Miner, N.E., Vineyard, C.M., Rothganger, F., Carlson, K.D., Mulder, S.A., Draelos, T.J., Faust, A., Marinella, M., Naegle, J.H., & Plimpton, S.J. (2017). A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications. Biologically Inspired Cognitive Architectures, 19(C), pp. 49-64. https://doi.org/10.1016/j.bica.2016.11.002 Publication ID: 48212
  • 2016
  • Bouchard, K.E., Aimone, J.B., Chun, M., T, D., Denker, M., Diesmann, M., Donofrio, D.D., Frank, L.M., Kasthuri, N., C, K., Ruebel, O., Simon, H.D., Sommer, F.T., & Prabhat, N. (2016). High-Performance Computing in Neuroscience for Data-Driven Discovery, Integration, and Dissemination. Neuron, 92(3), pp. 628-631. https://doi.org/10.1016/j.neuron.2016.10.035 Publication ID: 47534
  • du, H., Deng, W., Aimone, J.B., Ge, M., Parylak, S., Walch, K., Cook, J., Zhang, W., Song, H., Wang, L., Gage, F.H., & Mu, Y. (2016). Dopaminergic inputs in the dentate gyrus direct the choice of memory encoding. Proceedings of the National Academy of Sciences of the United States of America, 113(37), pp. E5501-E5510. https://doi.org/10.1073/pnas.1606951113 Publication ID: 51525
  • Dieni, C.V., Panichi, R., Aimone, J.B., Kuo, C.T., Wadiche, J.I., & Overstreet-Wadiche, L. (2016). Low excitatory innervation balances high intrinsic excitability of immature dentate neurons. Nature Communications, 7. https://doi.org/10.1038/ncomms11313 Publication ID: 49011
  • Agarwal, S., Quach, T.T., Parekh, O.D., Debenedictis, E., James, C.D., Marinella, M., & Aimone, J.B. (2016). Energy scaling advantages of resistive memory crossbar based computation and its application to sparse coding. Frontiers in Neuroscience, 9(JAN). https://doi.org/10.3389/fnins.2015.00484 Publication ID: 46410
  • 2014
  • Aimone, J.B. (2014). Regulation and Function of Adult Neurogenesis. From Genes to Cognition. Physiological Reviews, 94(4), pp. 991-1026. https://doi.org/10.1152/physrev.00004.2014 Publication ID: 36168
  • 2013
  • Aimone, J.B. (2013). A Hypothesis for Temporal Coding of Young and Mature Granule Cells. Proposed for publication in Frontiers in Neurogenesis.. https://www.osti.gov/biblio/1063462 Publication ID: 31485
  • Aimone, J.B. (2013). Perspectives for computational modeling of cell replacement for neurological disorders. Frontiers in Computational Neuroscience, 7(150). https://doi.org/10.3389/fncom.2013.00150 Publication ID: 32963
Showing 10 of 26 publications.

Pre-Sandia Publications (pre 2011)

  • Li Y, Stam FJ, Aimone JB, Goulding M, Callaway EM, and Gage FH – “Molecular layer perforant path-associated cells contribute to feed-forward inhibition in the adult dentate gyrus” PNAS. 110(22), May 2013
  • Li Y*, Aimone JB*, Xu X, Callaway EM, and Gage FH – “Development of GABAergic inputs controls the contribution of maturing neurons to the adult hippocampal network” PNAS. 109(11), March 2012.
  • Aimone JB, Deng W, and Gage FH – “Resolving New Memories: A Critical Look at the Dentate Gyrus, Adult Neurogenesis, and Pattern Separation” Neuron. 70(4), May 2011. (ISI Web of Knowledge Highly Cited Paper)
  • Aimone JB and Gage FH – “Modeling new neuron function: a history of using computational neuroscience to study adult neurogenesis” European Journal of Neuroscience. 33(6), March 2011.
  • Aimone JB, Deng W, and Gage FH – “Put Them Out to Pasture?  What Are Old Granule Cells Good for, Anyway…?” Hippocampus. 20(10), October 2010.
  • Aimone JB*, Deng W*, and Gage FH – “Adult neurogenesis: integrating theories and separating functions” Featured Review for Trends in Cognitive Sciences. 14(7), July 2010 (Cover Article; ISI Web of Knowledge Highly Cited Paper).
  • Deng W*, Aimone JB*, and Gage FH – “New neurons and new memories: How does adult hippocampal neurogenesis affect learning and memory?” Nature Reviews Neuroscience. 11(5), May 2010. (ISI Web of Knowledge Highly Cited Paper)
  • Aimone JB, Wiles J, and Gage FH – “Computational Influence of Adult Neurogenesis on Memory Encoding” Neuron, 61(2), January 2009.  (Faculty of 1000 Biology)
  • Smrt RD, Eaves-Egenes J, Barkho BZ, Santistevan NJ, Zhao C, Aimone JB, Gage FH, and Zhao X – “Mecp2 deficiency leads to delayed maturation and altered gene expression in hippocampal neurons” Neurobiology of Disease, 27(1), April 2007. (Faculty of 1000 Biology; ISI Web of Knowledge Highly Cited Paper)
  • Aimone JB, Wiles J, and Gage FH – “Potential Role for Adult Neurogenesis in the Encoding of Time in New Memories.” Nature Neuroscience, 9(6), June 2006. (Faculty of 1000 Biology; ISI Web of Knowledge Highly Cited Paper)
  • Barkho BZ, Song H, Aimone JB, Smrt RD, Kuwabara T, Nakashima K, Gage FH, and Zhao X – “Identification of astrocyte-expressed factors that modulate neural stem/progenitor cell differentiation.” Stem Cell and Development, 15(3), June 2006.
  • Myers CP, Lewcock JW, Hanson MG, Gosgnach S, Aimone JB, Gage FH, Lee KF, Landmesser LT, and Pfaff SL – “Cholinergic Input is Required during Embryonic Development to Mediate Proper Assemby of Spinal Locomotor Circuits.” Neuron, 46(1), April 2005.
  • Aimone JB*, Leasure JL*, Perreau VM*, Thallmair M* and the Christopher Reeve Paralysis Foundation – “Spatial and Temporal Gene Expression Profiling of the Contused Rat Spinal Cord” Experimental Neurology, 189(2), October 2004 (Cover Article).
  • Aimone JB and Gage FH,– “Unbiased Characterization of High-densisty Oligonucleotide Microarrays Using Probe-Level Statistics” Journal of Neuroscience Methods, 135(1-2), May 2004.
  • Coffer JL, Montchamp JL, Aimone JB, and Weis RP – “Routes to Calcified Porous Silicon: Implications for Drug Delivery and Biosensing” Physica. Status. Solidi. (a) 197, No.2.  2003.