About

Jeff Lichtman is the Jeremy R. Knowles Professor of Molecular and Cellular Biology and the Santiago Ramón y Cajal Professor of Arts and Sciences at Harvard University. His research focuses on the mechanisms underlying synaptic competition between neurons that innervate the same target cell. These competitive interactions are responsible for sharpening neural connection patterns during development and may also play a role in learning and memory formation. His laboratory studies synaptic rearrangements by visualizing synaptic competition directly in living animals using modern optical imaging techniques. The research concentrates on neuromuscular junctions in accessible mouse neck muscles, utilizing transgenic animals and labeling strategies to monitor individual nerve terminals and postsynaptic specializations over hours or months. Lichtman's work has contributed significantly to understanding synaptic structure and function, with numerous publications in the field.

Research topics

• Biology
• Neuroscience
• Computer Science
• Evolutionary biology
• Optics
• Physics
• Medicine
• Psychology
• Anatomy
• Cell biology
• Pathology
• Chemistry
• Genetics
• Computational biology
• Materials science

Selected publications

• Paired and solitary ionocytes in the zebrafish olfactory epithelium

  Chemical Senses · 2025-01-01 · 1 citations

  articleOpen access

  The sense of smell is generated by electrical currents that are influenced by the concentration of ions in olfactory sensory neurons and mucus. In contrast to the extensive morphological and molecular characterization of sensory neurons, there has been little description of the cells that control ion concentrations in the zebrafish olfactory system. Here, we report the molecular and ultrastructural characterization of zebrafish olfactory ionocytes. Transcriptome analysis suggests that the zebrafish olfactory epithelium contains at least three different ionocyte types, which resemble Na+/K+-ATPase-rich (NaR), H+-ATPase-rich (HR), and Na+/Cl- cotransporter (NCC) cells, responsible for calcium, pH, and chloride regulation, respectively, in the zebrafish skin. In the olfactory epithelium, NaR-like and HR-like ionocytes are usually adjacent to one another, whereas NCC-like cells are usually solitary. The distinct subtypes are differentially distributed: NaR-like/HR-like cell pairs are found broadly within the olfactory epithelium, whereas NCC-like cells reside within the peripheral non-sensory multiciliated cell zone. Comparison of gene expression and serial-section electron microscopy analysis indicates that the NaR-like cells wrap around the HR-like cells and are connected to them by shallow tight junctions. The development of olfactory ionocyte subtypes is also differentially regulated, as pharmacological Notch inhibition leads to a loss of NaR-like and HR-like cells, but does not affect NCC-like ionocyte number. These results provide a molecular and anatomical characterization of olfactory ionocytes in a stenohaline freshwater teleost. The paired ionocytes suggest that both transcellular and paracellular transport regulate ion concentrations in the olfactory epithelium, while the solitary ionocytes may enable independent regulation of ciliary beating.

  PublisherDOI

• Fishexplorer: A multimodal cellular atlas platform for neuronal circuit dissection in larval zebrafish

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-15 · 1 citations

  preprintOpen access

  Understanding how neural circuits give rise to behavior requires comprehensive knowledge of neuronal morphology, connectivity, and function. Atlas platforms play a critical role in enabling the visualization, exploration, and dissemination of such information. Here, we present FishExplorer, an interactive and expandable community platform designed to integrate and analyze multimodal brain data from larval zebrafish. FishExplorer supports datasets acquired through light microscopy (LM), electron microscopy (EM), and X-ray imaging, all co-registered within a unified spatial coordinate system which enables seamless comparison of neuronal morphologies and synaptic connections. To further assist circuit analysis, FishExplorer includes a suite of tools for querying and visualizing connectivity at the whole-brain scale. By integrating data from recent large-scale EM reconstructions (presented in companion studies), FishExplorer enables researchers to validate circuit models, explore wiring principles, and generate new hypotheses. As a continuously evolving resource, FishExplorer is designed to facilitate collaborative discovery and serve the growing needs of the teleost neuroscience community.

  PublisherOA PDFDOI

• Cellular and synaptic organization of the Octopus vertical lobe

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-01-29

  preprintOpen access

  Understanding memory formation and its influence on behavior is a central challenge in neuroscience. Associative learning networks, including the mushroom body in insects, the cerebellum in mammals, and the vertical lobe (VL) in cephalopods, typically exhibit a 3-layered architecture, characterized by divergence (fan-out) followed by convergence (fan-in), facilitating sparse sensory coding (Babadi and Sompolinsky, 2014; Lin et al., 2014; Litwin-Kumar et al., 2017; Turchetti-Maia et al., 2017). Previously, using volumetric electron microscopy, we showed that the VL uniquely comprises 22 million simple amacrine (SAM) interneurons, each receiving a singular input subject to activity-dependent long-term potentiation, contrasting with typical middle-layer interneurons (Bidel, Meirovitch et al., 2023). We also demonstrated that these SAMs provide excitatory feedforward input to the output cell layer, balanced by approximately 400,000 inhibitory complex amacrines (CAM), which are morphologically diverse and integrate numerous inputs (Bidel, Meirovitch et al., 2023). Here, we leverage the same digital tissue to explore the CAMs' morphological diversity, identifying correlations between structure, postsynaptic site density, and synaptic input proportions, which led to the classification of CAMs into distinct groups. Further analysis of the input layer in the VL revealed a meticulous structural and synaptic compartmentalization, with distinct synaptic bouton types forming three zones that integrate different inputs towards CAMs. Additionally, we identify the potential presence of a neurogenic niche in the VL, hinting at parallels with neurogenic processes in other species and warranting further investigation, particularly in the context of learning and memory. This study deepens our understanding of the VL's cellular and synaptic architecture, revealing both shared and unique features compared to other associative networks, and highlighting the intricate interplay of structural and functional elements in memory formation.

  PublisherOA PDFDOI

• SmartEM: machine learning-guided electron microscopy

  Nature Methods · 2025-12-29 · 2 citations

  articleOpen access
  PublisherOA PDFDOI

• Cellular and synaptic organization of the Octopus vertical lobe

  eLife · 2025-05-13

  preprintOpen access

  Summary Understanding memory formation and its influence on behavior is a central challenge in neuroscience. Associative learning networks, including the mushroom body in insects, the cerebellum in mammals, and the vertical lobe (VL) in cephalopods, typically exhibit a 3-layered architecture, characterized by divergence (fan-out) followed by convergence (fan-in), facilitating sparse sensory coding (Babadi and Sompolinsky, 2014; Lin et al., 2014; Litwin-Kumar et al., 2017; Turchetti-Maia et al., 2017). Previously, using volumetric electron microscopy, we showed that the VL uniquely comprises 22 million simple amacrine (SAM) interneurons, each receiving a singular input subject to activity-dependent long-term potentiation, contrasting with typical middle-layer interneurons (Bidel, Meirovitch et al., 2023). We also demonstrated that these SAMs provide excitatory feedforward input to the output cell layer, balanced by approximately 400,000 inhibitory complex amacrines (CAM), which are morphologically diverse and integrate numerous inputs (Bidel, Meirovitch et al., 2023). Here, we leverage the same digital tissue to explore the CAMs’ morphological diversity, identifying correlations between structure, postsynaptic site density, and synaptic input proportions, which led to the classification of CAMs into distinct groups. Further analysis of the input layer in the VL revealed a meticulous structural and synaptic compartmentalization, with distinct synaptic bouton types forming three zones that integrate different inputs towards CAMs. Additionally, we identify the potential presence of a neurogenic niche in the VL, hinting at parallels with neurogenic processes in other species and warranting further investigation, particularly in the context of learning and memory. This study deepens our understanding of the VL’s cellular and synaptic architecture, revealing both shared and unique features compared to other associative networks, and highlighting the intricate interplay of structural and functional elements in memory formation.

  PublisherDOI

• Frenet–Serret Frame-Based Decomposition for Part Segmentation of 3-D Curvilinear Structures

  IEEE Transactions on Medical Imaging · 2025-07-16 · 1 citations

  articleOpen access

  Accurate segmentation of anatomical substructures within 3D curvilinear structures in medical imaging remains challenging due to their complex geometry and the scarcity of diverse, large-scale datasets for algorithm development and evaluation. In this paper, we use dendritic spine segmentation as a case study and address these challenges by introducing a novel Frenet-Serret Frame-based Decomposition, which decomposes 3D curvilinear structures into a globally smooth continuous curve that captures the overall shape, and a cylindrical primitive that encodes local geometric properties. This approach leverages Frenet-Serret Frames and arc length parameterization to preserve essential geometric features while reducing representational complexity, facilitating data-efficient learning, improved segmentation accuracy, and generalization on 3D curvilinear structures. To rigorously evaluate our method, we introduce two datasets: CurviSeg, a synthetic dataset for 3D curvilinear structure segmentation that validates our method's key properties, and DenSpineEM, a benchmark for dendritic spine segmentation, which comprises 4,476 manually annotated spines from 70 dendrites across three public electron microscopy datasets, covering multiple brain regions and species. Our experiments on DenSpineEM demonstrate exceptional cross-region and cross-species generalization: models trained on the mouse somatosensory cortex subset achieve 94.43% Dice, maintaining strong performance in zero-shot segmentation on both mouse visual cortex (95.61% Dice) and human frontal lobe (86.63% Dice) subsets. Moreover, we test the generalizability of our method on the IntrA dataset, where it achieves 77.08% Dice (5.29% higher than prior arts) on intracranial aneurysm segmentation from entire artery models. These findings demonstrate the potential of our approach for accurately analyzing complex curvilinear structures across diverse medical imaging fields. Our dataset, code, and models are available at https://github.com/VCG/FFD4DenSpineEM to support future research.

  PublisherOA PDFDOI

• Correlative light and electron microscopy reveals the fine circuit structure underlying evidence accumulation in larval zebrafish

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-15 · 9 citations

  preprintOpen access

  Evidence accumulation is a fundamental neural computation essential for adaptive behavior, yet its synaptic implementation remains unclear. Addressing this challenge critically depends on linking neural dynamics to circuit structure within the same brain. Here, we combine functional calcium imaging with large-scale ultrastructural electron microscopy (EM) to uncover the wiring logic of visual evidence accumulation in larval zebrafish. In a functionally imaged EM dataset of the anterior hindbrain, we identify conserved morphological cell types whose activity patterns define distinct computational roles. Bilateral inhibition, disinhibition, and recurrent connectivity emerge as key circuit motifs shaping these dynamics. To generalize our findings across animals, we develop a photoconversion-based pipeline to label and reconstruct functionally characterized neurons, enabling us to train a classifier that predicts functional identity from morphology alone. Applying this classifier to a second, whole-brain EM dataset lacking functional data reveals matching connectivity patterns, significantly augmenting its applicability for detailed circuit dissections. Based on these results, we develop and constrain a biophysically realistic neural network model that captures observed dynamics and yields predictions we tested and confirmed experimentally. Our work illustrates how hypothesis-driven connectomics can uncover the synaptic basis of sensory-motor computations and establishes a novel framework for cross-animal circuit dissection in the vertebrate brain.

  PublisherOA PDFDOI

• Connectome analysis of a cerebellum-like circuit for sensory prediction

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-03

  preprintOpen access

  Stable and accurate perception involves comparing incoming sensory input with internally- generated predictions 1–3 . A mechanistic understanding of this process has been elusive due to the size and complexity of the relevant brain regions in mammals. Here we leverage connectomics to comprehensively map the cell types and synaptic connections underlying a well-characterized and ecologically relevant form of predictive sensory processing in the cerebellum-like electrosensory lobe (ELL) of weakly electric fish 4,5 . Connectome analysis reveals highly-structured feedforward and recurrent synaptic connectivity mediating the cancellation of predictable electrosensory input. A computational model constrained by prior electrophysiological recordings shows how this connectivity supports the formation of predictions at multiple sites within the network and how the ELL solves a continual learning problem by maintaining fast and accurate predictions despite noise and changes in environmental context. Overall, these findings provide a blueprint for using connectomics to elucidate learning in vertebrate nervous systems.

  PublisherOA PDFDOI

• Developmental Connectomics of the Mouse Cerebellum

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-16

  preprintOpen accessSenior authorCorresponding

  Abstract To uncover the developmental processes that establish the precise patterns of synaptic connectivity in the CNS, we employed a connectomic approach in the mouse cerebellar cortex between birth and 2 weeks of age. There were dramatic quantitative and qualitative changes in the structure and connectivity of cerebellar cells. Parallel fiber synapses onto Purkinje cells increased ∼500-fold, with the most rapid growth taking place a week after birth. To support this profound synaptogenesis, Purkinje cells generated thousands of transient parallel fiber-oriented filopodia that received nascent synapses from parallel fibers. Importantly, we find that granule cells initiate synaptic output onto Purkinje cells only after receiving mossy fiber input, revealing a sequential, input-dependent logic for circuit assembly. In sharp contrast to the concurrent pruning of climbing fiber inputs, parallel fiber connectivity expanded and became highly individualized during development. Despite anatomical overlap, neighboring Purkinje cells share significantly fewer parallel fiber inputs than expected by chance. Moreover, parallel fibers themselves diverged spatially, further enforcing selective input allocation and resulting in highly specific parallel fiber cohorts for each Purkinje cell. Our findings uncover a mechanistic sequence in which early afferent activity and transient cellular structures guide the selective wiring and expansion of parallel fiber input to Purkinje cells, establishing developmental principles that ensure functional specificity in the mature brain.

  PublisherOA PDFDOI

• Sparse Graph Reconstruction and Seriation for Large-Scale Image Stacks

  ArXiv.org · 2025-09-27

  preprintOpen accessSenior author

  We study recovering a 1D order from a noisy, locally sampled pairwise comparison matrix under a tight query budget. We recast the task as reconstructing a sparse, noisy line graph and present, to our knowledge, the first method that provably builds a sparse graph containing all edges needed for exact seriation using only O(N(log N + K)) oracle queries, which is near-linear in N for fixed window K. The approach is parallelizable and supports both binary and bounded-noise distance oracles. Our five-stage pipeline consists of: (i) a random-hook Boruvka step to connect components via short-range edges in O(N log N) queries; (ii) iterative condensation to bound graph diameter; (iii) a double-sweep BFS to obtain a provisional global order; (iv) fixed-window densification around that order; and (v) a greedy SuperChain that assembles the final permutation. Under a simple top-1 margin and bounded relative noise we prove exact recovery; empirically, SuperChain still succeeds when only about 2N/3 of true adjacencies are present. On wafer-scale serial-section EM, our method outperforms spectral, MST, and TSP baselines with far fewer comparisons, and is applicable to other locally structured sequencing tasks such as temporal snapshot ordering, archaeological seriation, and playlist/tour construction.

  PublisherOA PDFDOI

Recent grants

• A Tool for Synapse-level Circuit Analysis of Human Cerebral Cortex Specimens.

  NIH · $2.4M · 2021–2026

• A Facility to Generate Connectomics Information

  NIH · $6.5M · 2018–2025

• NIH Grant R01NS076467

  NIH · $9.4M · 2016

• Developmental origins of mental illness: evolution and reversibility

  NIH · $38.3M · 2011–2024

• NIH Grant R01EB016411

  NIH · $806k · 2015

Frequent coauthors

• Hanspeter Pfister

  Harvard University

  97 shared

• Richard Schalek

  Harvard University

  71 shared

• Daniel R. Berger

  51 shared

• Joshua R. Sanes

  Harvard University Press

  48 shared

• Markus Hadwiger

  47 shared

• Johanna Beyer

  47 shared

• Won-Ki Jeong

  45 shared

• Narayanan Kasthuri

  University of Chicago

  38 shared

Labs

• Jeff LichtmanPI