About

David Glanzman is a Distinguished Professor in the Department of Integrative Biology and Physiology at UCLA, with additional appointment in the Department of Neurobiology in the David Geffen School of Medicine. His academic journey began with a B.A. in psychology from Indiana University and a Ph.D. in experimental psychology from Stanford University. He has conducted postdoctoral research in the laboratories of Frank Krasne at UCLA and Eric Kandel at Columbia University, where he started his work on learning and memory in Aplysia. His laboratory focuses on the cell biology of learning and memory in simple organisms, utilizing the marine snail Aplysia californica and zebrafish (Danio rerio) as model systems. In Aplysia, his research investigates the cellular mechanisms underlying simple forms of learning such as habituation, sensitization, and classical conditioning, with a particular emphasis on the persistence of memory and the role of RNA-induced nuclear and epigenetic changes. His work has demonstrated that long-term memory can be reinstated after certain manipulations and transferred via RNA, challenging traditional models of synaptic plasticity. In zebrafish, his research explores the neural basis of nonassociative and olfactory-based associative learning, leveraging the organism's genetic tractability, transparent larvae, and suitability for advanced imaging techniques. Glanzman's work aims to deepen understanding of the cellular and molecular mechanisms underlying learning and memory, contributing valuable insights into how memories are stored and maintained in the brain.

Research topics

• Psychology
• Cognitive psychology
• Neuroscience
• Biology
• Chemistry
• Genetics

Selected publications

• Habituation and sensitization learning in adult solitary ascidians

  Scientific Reports · 2026-03-14

  articleOpen access

  The ability of organisms to learn and remember is crucial for their survival. This cognitive ability spans a wide range of organisms, including both vertebrates and invertebrates, yet has remained little studied in many taxonomical groups. Ascidians (sea squirts), are basal chordates closely related to vertebrates, making them a valuable model for investigating the evolutionary origins of learning and memory. Despite their phylogenetic significance as a sister group to vertebrates, the learning and memory abilities of adult solitary ascidians have remained largely unexplored. In this study, we investigated, for the first time, the ability for nonassociative learning in the solitary ascidian Polycarpa mytiligera, focusing on habituation and sensitization. We employed controlled mechanical and electrical stimuli to assess these two forms of learning, specifically examining short-term sensitization and long-term habituation. Our findings demonstrate that P. mytiligera exhibits both types of learning and retains memory over both short- and long-term periods, thereby establishing P. mytiligera as a promising model for investigating learning and memory mechanisms in chordates.

  PublisherOA PDFDOI

• <em>In Vivo</em> Confocal Fluorescence Imaging of Neural Activity Induced by Sensory Stimulation in Partially Restrained Larval Zebrafish

  Journal of Visualized Experiments · 2025-04-18

  articleSenior author

  Zebrafish larvae are a promising vertebrate model system for studying the neural mechanisms of behavior. Their translucence and relatively simple neural circuitry facilitate the use of optogenetic techniques in cellular analyses of behavior. Fluorescent indicators of in vivo neural activity, such as GCaMP6s, have been widely used to study the neural activity associated with simple behaviors in larval zebrafish. Here, we present a protocol for detecting sensory-induced activity in semi-restrained zebrafish larvae using the transgenic line Tg(elav3:GCaMP6s). In particular, we use the chemical agent allyl isothiocyanate to induce a robust, reproducible fluorescent response in a brain region at the border of the hindbrain and spinal cord. We discuss the potential uses of GCaMP6s for optical monitoring of neural activity during a range of behavioral paradigms and the limitations of this technique. Our protocol outlines an accessible approach for monitoring dynamic, behavior-related in vivo neural activity in the larval zebrafish brain.

  PublisherDOI

• Scalable prediction of symmetric protein complex structures

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-15

  preprint

  Abstract All life relies on proteins to function, yet accurately modeling protein structures that exceed ≈ 10, 000 amino acids or have higher-order geometries remains difficult. Existing solutions are limited to specific scenarios, require considerable computational resources, or are otherwise unscalable. Consequently, many large, disease-relevant protein complexes in the human proteome, as well as nearly all viruses and numerous other classes, are impractical to model with high fidelity for drug development. To modulate these protein complexes and viruses, structural information is eminently valuable, and often essential. In the last two years, machine learning based-tools that can generate binders to a given target structure with high hit rates have emerged. Combined with high-throughput screening, these technologies can far outpace traditional drug discovery. However, they cannot function well without accurate models of their target structures. Thus, to unlock the full power of AI-driven drug discovery, a scalable method must be developed to predict large protein complex structures. To overcome this bottleneck, we introduce Plica-1, a physics-based method to rapidly and accurately predict the structure of arbitrarily large, symmetric protein complexes. Validated across 4 major symmetry classes (icosahedral, tetrahedral, octahedral, and cyclic), the method consistently achieves near-experimental levels of accuracy, i.e., RMSD < 5Å. In test cases, the method runs in < 5 minutes on consumer hardware, 10 3 -10 5 times faster than the closest comparable software. The largest structure currently built, at ≈40,000 amino acids, is > 8 times the limit of existing machine learning methods. The results demonstrate that protein complexes can be modeled at significantly improved speeds and scales, making Plica-1 a promising tool for protein engineering and drug development.

  PublisherDOI

• Review for "Reductionism in Engram Neuroscience"

  2025-04-29

  peer-review1st authorCorresponding
  PublisherDOI

• Review for "Reductionism in Engram Neuroscience"

  2024-08-30

  peer-review1st authorCorresponding
  PublisherDOI

• Review for "Reductionism in Engram Neuroscience"

  2024-12-25

  peer-review1st authorCorresponding
  PublisherDOI

• Potential Contribution of Retrotransposons to Learning and Memory

  2024-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Faculty Opinions recommendation of Uncovering long-term existence of a silent short-term memory trace.

  Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · 2022-06-02

  dataset1st authorCorresponding
  PublisherDOI

• XBERT: Xilinx Logical-Level Bitstream Embedded RAM Transfusion

  2021-05-01 · 2 citations

  article

  XBERT is an API and design toolset for zero-cost access to the on-chip SRAM blocks on Xilinx architectures using the device's configuration path. The XBERT API is high-level, allowing developers to specify DMA-like data transfers of memory contents in terms of the logical memories in the application source code and thus is applicable to essentially any design targeting Xilinx devices. XBERT is broadly accessible to application developers, hiding the low-level details of physical mapping and bitstream encoding. XBERT is efficient, consuming zero reconfigurable resources with no impact on Fmax. XBERT achieves a bandwidth of 3-14 megabytes per second (MB/s) and complete readback and translation of a memory in an isolated 36Kb block RAM in less than 0.5 ms on a Xilinx UltraScale+ MPSoC Zynq.

  PublisherDOI

• The central importance of nuclear mechanisms in the storage of memory

  Biochemical and Biophysical Research Communications · 2021 · 29 citations

  Senior authorCorresponding
  • Neuroscience
  • Biology
  • Psychology
  PublisherDOI

Recent grants

• Plasticity of Long-Term Plasticity of a Single Sensorimotor Synapse

  NSF · $867k · 2013–2019

• NIH Grant R37NS029563

  NIH · $2.9M · 2013

• Neurobiology of Simple Forms of Learning in the Zebrafish

  NSF · $450k · 2009–2012

• NIH Grant R01MH096120

  NIH · $1.9M · 2018

• NIH Grant R01NS029563

  NIH · $4.0M · 2019

Frequent coauthors

• Samuel Schacher

  New York State Psychiatric Institute

  22 shared

• Eric R. Kandel

  22 shared

• Diancai Cai

  18 shared

• Wayne S. Sossin

  Montreal Neurological Institute and Hospital

  16 shared

• Shanping Chen

  Xiangtan University

  14 shared

• Kaycey Pearce

  13 shared

• Eric R. Kandel

  Columbia University

  10 shared

• Robert D. Hawkins

  9 shared

Education

• Ph.D., Molecular, Cellular, and Developmental Biology

  University of California, Los Angeles

  1997

• B.S., Molecular, Cellular, and Developmental Biology

  University of California, Los Angeles

  1992