Bio


Alex Gonzalez is a Scientific Project Manager for the Wu Tsai Human Performance Alliance. In this role, Alex contributes to the management of external institutional and industry partners and the Agility Grants program. On the research side, Alex is part of the Digital Athlete moonshot and provides data science and statistical expertise across multiple scientific projects related to neuroscience, human performance, health, and basic physiology. Specifically, Alex develops scalable data infrastructures and algorithms to analyze large data sets to uncover biological principles of human physiology. Current projects include (1): using data from wearable technology to characterize training loads and the relationship to performance and injury; (2) characterization and computational models of female physiology through the menstrual cycle and lifespan, (3) development of computational models that can describe between the interaction of sleep, physical activity and the menstrual cycle.

Alex's academic degrees are in Electrical Engineering, with a concentration in Signal Processing and Machine Learning. In his research, he applied the technical aspects of engineering to neuroscience. In his PhD studies at Stanford, Alex studied how invasive and non-invasive cortical electrical signals related to human memory. In his postdoctoral work, he switched to study rodents, in which he studied signal encoding of navigational circuits. Additional major prior projects include: development for online arrhythmia detection (industry), electrode sensor testing for brain recordings, instrumentation development for stem-cell differentiation into cardiomyocytes.

Current Role at Stanford


Scientific Project Manager for the Wu Tsai Human Performance Alliance

Academic Appointments


Honors & Awards


  • Ruth L. Kirschstein National Research Service Award (NRSA), NIH - NIMH (2019-2022)
  • DARE Fellowship, Stanford (2015)
  • Electrical Engineering Teaching Fellow, Stanford (2015)
  • Psychology Department Pilot Research Grant - Transcranial Current Stimulation Experiments, Stanford (2014)
  • NIH Training Grant in Biotechnology (T32), Stanford - NIH (2012-2015)
  • Alfred P. Sloan PhD Graduate Fellow, Alfred P. Sloan Foundation (2011-2013)
  • Neuro Ventures Research Seed Funding (Co-PI) - EEG-fMRI Studies of Memory, Stanford (2011-2013)
  • NSF Graduate Research Fellowship, NSF (2010-2013)

Professional Education


  • BS, University of Puerto Rico - Mayaguez, Electrical Engineering (2010)
  • PhD, Stanford, Electrical Engineering (2017)
  • Postdoc, Stanford, Neurobiology (2022)

All Publications


  • NeuroRoots, a bio-inspired, seamless brain machine interface for long-term recording in delicate brain regions. AIP advances Ferro, M. D., Proctor, C. M., Gonzalez, A., Jayabal, S., Zhao, E., Gagnon, M., Slézia, A., Pas, J., Dijk, G., Donahue, M. J., Williamson, A., Raymond, J., Malliaras, G. G., Giocomo, L., Melosh, N. A. 2024; 14 (8): 085109

    Abstract

    Scalable electronic brain implants with long-term stability and low biological perturbation are crucial technologies for high-quality brain-machine interfaces that can seamlessly access delicate and hard-to-reach regions of the brain. Here, we created "NeuroRoots," a biomimetic multi-channel implant with similar dimensions (7 μm wide and 1.5 μm thick), mechanical compliance, and spatial distribution as axons in the brain. Unlike planar shank implants, these devices consist of a number of individual electrode "roots," each tendril independent from the other. A simple microscale delivery approach based on commercially available apparatus minimally perturbs existing neural architectures during surgery. NeuroRoots enables high density single unit recording from the cerebellum in vitro and in vivo. NeuroRoots also reliably recorded action potentials in various brain regions for at least 7 weeks during behavioral experiments in freely-moving rats, without adjustment of electrode position. This minimally invasive axon-like implant design is an important step toward improving the integration and stability of brain-machine interfacing.

    View details for DOI 10.1063/5.0216979

    View details for PubMedID 39130131

    View details for PubMedCentralID PMC11309783

  • Parahippocampal neurons encode task-relevant information for goal-directed navigation. eLife Gonzalez, A., Giocomo, L. M. 2024; 12

    Abstract

    A behavioral strategy crucial to survival is directed navigation to a goal, such as a food or home location. One potential neural substrate for supporting goal-directed navigation is the parahippocampus, which contains neurons that represent an animal's position, orientation, and movement through the world, and that change their firing activity to encode behaviorally relevant variables such as reward. However, little prior work on the parahippocampus has considered how neurons encode variables during goal-directed navigation in environments that dynamically change. Here, we recorded single units from rat parahippocampal cortex while subjects performed a goal-directed task. The maze dynamically changed goal-locations via a visual cue on a trial-to-trial basis, requiring subjects to use cue-location associations to receive reward. We observed a mismatch-like signal, with elevated neural activity on incorrect trials, leading to rate-remapping. The strength of this remapping correlated with task performance. Recordings during open-field foraging allowed us to functionally define navigational coding for a subset of the neurons recorded in the maze. This approach revealed that head-direction coding units remapped more than other functional-defined units. Taken together, this work thus raises the possibility that during goal-directed navigation, parahippocampal neurons encode error information reflective of an animal's behavioral performance.

    View details for DOI 10.7554/eLife.85646

    View details for PubMedID 38363198

  • From Rats to Humans: how novel behavioral paradigms and reinforcement learning can bridge the gap in translation. Lab animal Gonzalez, A., Giocomo, L. M. 2022

    View details for DOI 10.1038/s41684-022-01077-x

    View details for PubMedID 36258040

  • Neuromatch Academy: a 3-week, online summer school in computational neuroscience Journal of Open Source Education 't Hart, B. M., et al 2022

    View details for DOI 10.21105/jose.00118

  • Electrocorticography reveals the temporal dynamics of posterior parietal cortical activity during recognition memory decisions. Proceedings of the National Academy of Sciences of the United States of America Gonzalez, A., Hutchinson, J. B., Uncapher, M. R., Chen, J., LaRocque, K. F., Foster, B. L., Rangarajan, V., Parvizi, J., Wagner, A. D. 2015; 112 (35): 11066-11071

    Abstract

    Theories of the neurobiology of episodic memory predominantly focus on the contributions of medial temporal lobe structures, based on extensive lesion, electrophysiological, and imaging evidence. Against this backdrop, functional neuroimaging data have unexpectedly implicated left posterior parietal cortex (PPC) in episodic retrieval, revealing distinct activation patterns in PPC subregions as humans make memory-related decisions. To date, theorizing about the functional contributions of PPC has been hampered by the absence of information about the temporal dynamics of PPC activity as retrieval unfolds. Here, we leveraged electrocorticography to examine the temporal profile of high gamma power (HGP) in dorsal PPC subregions as participants made old/new recognition memory decisions. A double dissociation in memory-related HGP was observed, with activity in left intraparietal sulcus (IPS) and left superior parietal lobule (SPL) differing in time and sign for recognized old items (Hits) and correctly rejected novel items (CRs). Specifically, HGP in left IPS increased for Hits 300-700 ms poststimulus onset, and decayed to baseline ∼200 ms preresponse. By contrast, HGP in left SPL increased for CRs early after stimulus onset (200-300 ms) and late in the memory decision (from 700 ms to response). These memory-related effects were unique to left PPC, as they were not observed in right PPC. Finally, memory-related HGP in left IPS and SPL was sufficiently reliable to enable brain-based decoding of the participant's memory state at the single-trial level, using multivariate pattern classification. Collectively, these data provide insights into left PPC temporal dynamics as humans make recognition memory decisions.

    View details for DOI 10.1073/pnas.1510749112

    View details for PubMedID 26283375

  • TEACHING DIGITAL SIGNAL PROCESSING WITH STANFORD'S LAB-IN-A-BOX Mujica, F. A., Esposito, W. J., Gonzalez, A., Qi, C. R., Vassos, C., Wieman, M., Wilcox, R., Kovacs, G. A., Schafer, R. W., IEEE IEEE. 2015: 307–12
  • Integrated strain array for cellular mechanobiology studies. Journal of micromechanics and microengineering : structures, devices, and systems Simmons, C. S., Sim, J. Y., Baechtold, P., Gonzalez, A., Chung, C., Borghi, N., Pruitt, B. L. 2011; 21 (5): 54016-54025

    Abstract

    We have developed an integrated strain array for cell culture enabling high-throughput mechano-transduction studies. Biocompatible cell culture chambers were integrated with an acrylic pneumatic compartment and microprocessor-based control system. Each element of the array consists of a deformable membrane supported by a cylindrical pillar within a well. For user-prescribed waveforms, the annular region of the deformable membrane is pulled into the well around the pillar under vacuum, causing the pillar-supported region with cultured cells to be stretched biaxially. The optically clear device and pillar-based mechanism of operation enables imaging on standard laboratory microscopes. Straightforward fabrication utilizes off-the-shelf components, soft lithography techniques in polydimethylsiloxane, and laser ablation of acrylic sheets. Proof of compatibility with basic biological assays and standard imaging equipment were accomplished by straining C2C12 skeletal myoblast cells on the device for 6 hours. At higher strains, cells and actin stress fibers realign with a circumferential preference.

    View details for DOI 10.1088/0960-1317/21/5/054016

    View details for PubMedID 21857773

    View details for PubMedCentralID PMC3156674