A5016876498- Cell Image Analysis Techniques
- Advanced Electron Microscopy Techniques and Applications
- Advanced Fluorescence Microscopy Techniques
- Motor Control and Adaptation
- Electron and X-Ray Spectroscopy Techniques
- Functional Brain Connectivity Studies
- Cephalopods and Marine Biology
- Advanced Neural Network Applications
- Neurobiology and Insect Physiology Research
- Genetics, Aging, and Longevity in Model Organisms
- Robot Manipulation and Learning
- Neural dynamics and brain function
- Machine Learning in Materials Science
- Visual Attention and Saliency Detection
- Neuroscience and Neural Engineering
- Action Observation and Synchronization
- Single-cell and spatial transcriptomics
- Physiological and biochemical adaptations
- Advanced Memory and Neural Computing
- Photoreceptor and optogenetics research
- Balance, Gait, and Falls Prevention
- Biotin and Related Studies
- Reinforcement Learning in Robotics
- Teleoperation and Haptic Systems
- Memory and Neural Mechanisms
Harvard University
2020-2025
Harvard University Press
2019-2024
Massachusetts Institute of Technology
2016-2020
Weizmann Institute of Science
2012-2017
Here, we present the first analysis of connectome a small volume
The short-lasting attenuation of brain oscillations is termed event-related desynchronization (ERD). It frequently found in the alpha and beta bands humans during generation, observation, imagery movement considered to reflect cortical motor activity action-perception coupling. shared information driving ERD all these motor-related behaviors unknown. We investigated whether particular laws governing production perception curved may account for rhythms. Human appears be governed by relatively...
Abstract From birth to adulthood, an animal’s nervous system changes as its body grows and behaviours mature. The form extent of circuit remodelling across the connectome is unknown. We used serial-section electron microscopy reconstruct full brain eight isogenic C. elegans individuals postnatal stages learn how it with age. overall geometry preserved from adulthood. Upon this constant scaffold, substantial in chemical synaptic connectivity emerge. Comparing connectomes among reveals...
Analysis of brain structure, connectivity, and molecular diversity relies on effective tissue fixation. Conventional fixation causes extracellular space (ECS) loss, complicating the segmentation cellular objects from electron microscopy datasets. Previous techniques for preserving ECS in mammalian brains utilizing high-pressure perfusion can give inconsistent results owing to variations hydrostatic pressure within vasculature. A more reliable protocol that uniformly preserves throughout...
The 'connectome', a comprehensive wiring diagram of synaptic connectivity, is achieved through volume electron microscopy (vEM) analysis an entire nervous system and all associated non-neuronal tissues. White et al. (1986) pioneered the fully manual reconstruction connectome using C. elegans. Recent advances in vEM allow mapping new elegans connectomes with increased throughput, reduced subjectivity. Current studies aim to not only fill remaining gaps original connectome, but also address...
Pixel-accurate tracking of objects is a key element in many computer vision applications, often solved by iterated individual object or instance segmentation followed matching. Here we introduce cross-classification clustering (3C), technique that simultaneously tracks complex, interrelated an image stack. The idea to efficiently turn problem into classification running logarithmic number independent classifications per image, letting the cross-labeling these uniquely classify each pixel...
In human motor control studies, end-effector (e.g., hand) trajectories have been successfully modeled using optimization principles. Yet, it remains unclear how such are updated when the or task goals perturbed. Here, we present an approach to and robotic task-level trajectory planning modification geometrical invariance optimization, allowing adapt learned movements a priori unknown boundary conditions. The criterion represents tradeoff between smoothness (minimum jerk) accuracy...
Summary Understanding memory formation and its influence on behavior is a central challenge in neuroscience. Associative learning networks, including the mushroom body insects, cerebellum mammals, vertical lobe (VL) cephalopods, typically exhibit 3-layered architecture, characterized by divergence (fan-out) followed convergence (fan-in), facilitating sparse sensory coding (Babadi Sompolinsky, 2014; Lin et al., Litwin-Kumar 2017; Turchetti-Maia 2017). Previously, using volumetric electron...
The field of connectomics faces unprecedented "big data" challenges. To reconstruct neuronal connectivity, automated pixel-level segmentation is required for petabytes streaming electron microscopy data. Existing algorithms provide relatively good accuracy but are unacceptably slow, and would require years to extract connectivity graphs from even a single cubic millimeter neural tissue. Here we present viable real-time solution, multi-pass pipeline optimized shared-memory multicore systems,...
Abstract Mapping neuronal networks is a central focus in neuroscience. While volume electron microscopy (vEM) can reveal the fine structure of (connectomics), it does not provide molecular information to identify cell types or functions. We developed an approach that uses fluorescent single-chain variable fragments (scFvs) perform multiplexed detergent-free immunolabeling and volumetric-correlated-light-and-electron-microscopy on same sample. generated eight scFvs targeting brain markers....
Abstract This paper introduces a benchmark framework to evaluate the performance of reaching motion generation approaches that learn from demonstrated examples. The system implements ten different measures for typical generalization tasks in robotics using open source MATLAB software. Systematic comparisons are based on default training data set human motions, which specify respective ground truth. In technical terms, an evaluated method needs compute velocities, given state provided by...
Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on one hand, have benefited ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures advanced machine algorithms. On other field neuroscience at large, image processing particular, has manifested a need for user-friendly open...
Abstract Mapping the complete synaptic connectivity of a mammalian brain would be transformative, revealing pathways underlying perception, behavior, and memory. Serial section electron microscopy, via membrane staining using osmium tetroxide, is ideal for visualizing cells connections but, in whole samples, faces significant challenges related to chemical treatment volume changes. These issues can adversely affect both ultrastructural quality macroscopic tissue integrity. By leveraging...
Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology. This leads to a high incidence of erroneously merged objects. Humans, by contrast, can easily detect such errors acquiring intuition the correct morphology Biological neurons have complicated and variable shapes, which are challenging learn, merge take multitude different forms. We present an algorithm, MergeNet, that shows 3D ConvNets can, in fact, from high-level neuronal MergeNet...
Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of (connectomics), it does not provide molecular information helps identify cell types or their functional properties. Volumetric correlated light and (vCLEM) combines ssEM volumetric fluorescence to incorporate labeling into datasets. We developed an approach uses small fluorescent single-chain variable fragment (scFv)...
ORIGINAL RESEARCH article Front. Comput. Neurosci., 27 May 2013 Volume 7 - | https://doi.org/10.3389/fncom.2013.00068
The current design trend in large scale machine learning is to use distributed clusters of CPUs and GPUs with MapReduce-style programming. Some have been led believe that this type horizontal scaling can reduce or even eliminate the need for traditional algorithm development, careful parallelization, performance engineering. This paper a case study showing contrary: benefits algorithms, engineering, sometimes be so vast it possible solve "cluster-scale" problems on single commodity multicore machine.
Comprehensive, synapse-resolution imaging of the brain will be crucial for understanding neuronal computations and function. In connectomics, this has been sole purview volume electron microscopy (EM), which entails an excruciatingly difficult process because it requires cutting tissue into many thin, fragile slices that then need to imaged, aligned, reconstructed. Unlike EM, hard X-ray is compatible with thick tissues, eliminating thin sectioning, delivering fast acquisition, intrinsic...
Connectomics provides essential nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access the high-throughput electron microscopes necessary generate enough data for whole circuit or reconstruction. To date, machine-learning methods been used after collection images by microscopy (EM) accelerate improve neuronal segmentation, synapse reconstruction other analysis. With computational improvements in processing...
Connectomics is an emerging field in neuroscience that aims to reconstruct the 3-dimensional morphology of neurons from electron microscopy (EM) images. Recent studies have successfully demonstrated use convolutional neural networks (ConvNets) for segmenting cell membranes individuate neurons. However, there has been comparatively little success high-throughput identification intercellular synaptic connections required deriving connectivity graphs. In this study, we take a compositional...