Jeff William Lichtman

The paper uses connectomics to map a cerebellum-like circuit in weakly electric fish, revealing structured synaptic connectivity that supports predictive sensory processing and continual learning.

RAEM enables targeted, multi-resolution imaging of biological specimens by combining low-resolution 3D surveys with high-resolution imaging of specific regions.

The paper examines how cerebellar granule cells sample distinct mossy fiber inputs using vCLEM data from a mouse cerebellum, revealing structured, non-random sampling patterns.

FEABAS is a tool for efficiently aligning and stitching serial EM data, handling artifacts and enabling scalable vEM reconstruction.

This paper introduces CAptVE, a method for fluorescent aptamer-based labeling that enables molecular analysis in ultrastructurally reconstructable electron micrographs.

The paper presents a multimodal imaging pipeline that combines diffusion MRI and high-resolution X-ray microscopy to map the human connectome across multiple scales.

SmartEM accelerates electron microscopy image acquisition by using machine learning to intelligently allocate imaging time, achieving up to 7-fold speedup in connectomic samples.

This study shows human brain organoids can mature over 5 years in culture, transcriptionally aging and retaining memory of time spent in vitro.

This study investigates synaptic connectivity development in the mouse cerebellum, revealing sequential input-dependent wiring and individualized parallel fiber inputs to Purkinje cells.

FishExplorer integrates multimodal brain data from larval zebrafish to enable circuit analysis and hypothesis generation.

The paper presents a connectomic dataset of a larval zebrafish brain, enabling circuit analysis through synaptic and neuronal annotations.

The paper identifies a brightness and contrast processing circuit in zebrafish and shows Lrrn CAMs are essential for its assembly and function.

The paper uses correlative light and electron microscopy to identify synaptic motifs in larval zebrafish that support visual motion processing.

This paper examines the cellular and synaptic organization of the octopus vertical lobe, identifying unique structural and functional features compared to other associative networks.

EM-Compressor uses Variational Autoencoders to compress EM images for connectomics, reducing data size to 1/128th while preserving features needed for segmentation.

The paper characterizes ionocyte subtypes in zebrafish olfactory epithelium, identifying three types with distinct functions and distributions.

The paper identifies extensive soma-soma plate-like contacts (ephapses) between SCN neurons that may enable synchronization without chemical or electrical synapses.

The paper presents a connectomic analysis of a human cerebral cortex fragment, revealing structural and connectivity features of neurons and synapses.

The paper introduces sample preparation methods for volume electron microscopy in the gastropod mollusc Berghia stephanieae, addressing challenges in cryopreservation and processing time.

SegCLR, a self-supervised learning method, enables classification of cellular subcompartments and cell types from 3D electron microscopy data with minimal labeled data.

Vimo enables interactive visualization of neuronal connectivity motifs in large brain networks, allowing experts to sketch and analyze motif instances and chains in 3D.

The paper presents a vCLEM protocol to map Aβ and pTau in AD mouse hippocampus connectomics data, revealing new molecular and structural alterations.

The paper introduces a 12-step protocol called ODeCO to infiltrate osmium in whole mouse brains, preserving ultrastructure for connectome mapping.

mEMbrain provides an interactive MATLAB tool for connectomic segmentation, enabling user-friendly analysis of EM datasets with pre-trained networks and ground truth annotations.

The paper analyzes the connectome of the Octopus vulgaris vertical lobe, identifying novel synaptic structures and functional roles in memory acquisition.

The paper introduces a vCLEM method using fluorescent scFvs to combine molecular labeling with ssEM, revealing new cell types and subcellular features in mouse cerebellar cortex.

The paper uses 3D reconstruction and deep learning to reveal intercellular connections between developing granule cells in the cerebellar EGL, showing a continuum of proliferative and migratory events.

The paper reconstructs the post-embryonic maturation of the C. elegans motor circuit using electron microscopy, showing how structural rewiring maintains functional output during development.

This study investigates how zebrafish larvae distinguish self-generated reafferent mechanosensory signals from exafferent stimuli using lateral line hair cells and neural circuits.

The paper traces neuromuscular connectivity in mice across development, revealing that synaptic pruning follows a global activity-based ordering rule.