Shanka Subhra Mondal
A5029266882- Neural dynamics and brain function
- Neuroscience and Neuropharmacology Research
- Cell Image Analysis Techniques
- Advanced Electron Microscopy Techniques and Applications
- Memory and Neural Mechanisms
- Functional Brain Connectivity Studies
- Advanced Fluorescence Microscopy Techniques
- Cloud Computing and Resource Management
- Neuroinflammation and Neurodegeneration Mechanisms
- Multimodal Machine Learning Applications
- Advanced Software Engineering Methodologies
- Advanced Image and Video Retrieval Techniques
- Energy Load and Power Forecasting
- Computability, Logic, AI Algorithms
- Embedded Systems Design Techniques
- Forecasting Techniques and Applications
- Visual perception and processing mechanisms
- Neurobiology and Insect Physiology Research
- Stock Market Forecasting Methods
- Human Pose and Action Recognition
- Advanced Memory and Neural Computing
- Modular Robots and Swarm Intelligence
- IoT and Edge/Fog Computing
- Constraint Satisfaction and Optimization
- Data Visualization and Analytics
Princeton University
2021-2025
University of Oxford
2023-2024
Neuroscience Institute
2021-2024
University of Pennsylvania
2023-2024
Baylor College of Medicine
2023
Laboratoire d'Informatique de Paris-Nord
2021-2023
Indian Institute of Technology Kharagpur
2019
Adobe Systems (United States)
2019
Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how type participates in the cortical circuit, but mapping rules at resolution distinct types remains difficult. Here, we used millimeter-scale volumetric electron microscopy 1 to investigate all inhibitory neurons across densely-segmented population 1352 cells spanning layers mouse visual cortex, producing wiring...
Abstract Understanding the brain requires understanding neurons’ functional responses to circuit architecture shaping them. Here we introduce MICrONS connectomics dataset with dense calcium imaging of around 75,000 neurons in primary visual cortex (VISp) and higher areas (VISrl, VISal VISlm) an awake mouse that is viewing natural synthetic stimuli. These data are co-registered electron microscopy reconstruction containing more than 200,000 cells 0.5 billion synapses. Proofreading a subset...
Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties1. Synaptic connectivity shapes how type participates in the cortical circuit, but mapping rules at resolution distinct types remains difficult. Here we used millimetre-scale volumetric electron microscopy2 to investigate all inhibitory neurons across densely segmented population 1,352 cells spanning layers mouse visual cortex, producing wiring diagram...
Abstract Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using variety molecular, electrophysiological and morphological approaches 1–4 . Each modality offers new perspectives on the variation biological processes underlying cell-type specialization. Cellular-scale electron microscopy provides dense ultrastructural examination an unbiased perspective subcellular organization brain cells, including their synaptic connectivity...
Abstract Understanding the relationship between circuit connectivity and function is crucial for uncovering how brain computes. In mouse primary visual cortex, excitatory neurons with similar response properties are more likely to be synaptically connected 1–8 ; however, broader rules remain unknown. Here we leverage millimetre-scale MICrONS dataset analyse synaptic functional of across cortical layers areas. Our results reveal that preferentially within areas—including feedback...
Neural circuit function is shaped both by the cell types that comprise and connections between them1. have previously been defined morphology2,3, electrophysiology4, transcriptomic expression5,6, connectivity7-9 or a combination of such modalities10-12. The Patch-seq technique enables characterization morphology, electrophysiology properties from individual cells13-15. These were integrated to define 28 inhibitory, morpho-electric-transcriptomic (MET) in mouse visual cortex16, which do not...
We are in the era of millimetre-scale electron microscopy volumes collected at nanometre resolution1,2. Dense reconstruction cellular compartments these has been enabled by recent advances machine learning3-6. Automated segmentation methods produce exceptionally accurate reconstructions cells, but post hoc proofreading is still required to generate large connectomes that free merge and split errors. The elaborate 3D meshes neurons contain detailed morphological information multiple scales,...
Understanding the relationship between circuit connectivity and function is crucial for uncovering how brain implements computation. In mouse primary visual cortex (V1), excitatory neurons with similar response properties are more likely to be synaptically connected, but previous studies have been limited within V1, leaving much unknown about broader rules. this study, we leverage millimeter-scale MICrONS dataset analyze synaptic functional of individual across cortical layers areas. Our...
Abstract Advances in electron microscopy, image segmentation and computational infrastructure have given rise to large-scale richly annotated connectomic datasets, which are increasingly shared across communities. To enable collaboration, users need be able concurrently create annotations correct errors the automated by proofreading. In large every proofreading edit relabels cell identities of millions voxels thousands like synapses. For analysis, require immediate reproducible access this...
Neurons in the neocortex exhibit astonishing morphological diversity, which is critical for properly wiring neural circuits and giving neurons their functional properties. However, organizational principles underlying this diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain low-dimensional "bar code" describing more than 30,000 excitatory mouse visual areas V1, AL, RL that were reconstructed from millimeter scale...
Abstract To understand the brain we must relate neurons’ functional responses to circuit architecture that shapes them. Here, present a large connectomics dataset with dense calcium imaging of millimeter scale volume. We recorded activity from approximately 75,000 neurons in primary visual cortex (VISp) and three higher areas (VISrl, VISal VISlm) an awake mouse viewing natural movies synthetic stimuli. The data were co-registered volumetric electron microscopy (EM) reconstruction containing...
Abstract 3D electron microscopy (EM) has been successful at mapping invertebrate nervous systems, but the approach limited to small chunks of mammalian brains. To scale up larger volumes, we have built a computational pipeline for processing petascale image datasets acquired by serial section EM, popular form EM. The employs convolutional nets compute nonsmooth transformations required align images sections containing numerous cracks and folds, detect neuronal boundaries, label voxels as...
Abstract Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need be able concurrently create new annotations correct errors the automated by proofreading. In large datasets, every proofreading edit relabels cell identities of millions voxels thousands like synapses. For analysis, require immediate reproducible...
Resource usage of production workloads running on shared compute clusters often fluctuate significantly across time. While simultaneous spike in the resource between two same machine can create performance degradation, unused resources a results wastage and undesirable operational characteristics for cluster. Prior works did not consider such temporal fluctuations or their alignment scheduling decisions. Due to variety time-varying workloads, complex characteristics, it is challenging design...
We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium 2021). Dense reconstruction cellular compartments these EM has been enabled by recent advances Machine Learning (ML) (Lee 2017; Wu Lu Macrina Automated segmentation methods produce exceptionally accurate reconstructions cells, but post-hoc proofreading is still required to generate large connectomes free merge and split errors. The elaborate 3-D...
Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using variety molecular, electrophysiological and morphological approaches. Each modality offers new perspectives on the variation biological processes underlying type specialization. Cellular scale electron microscopy (EM) provides dense ultrastructural examination an unbiased perspective into subcellular organization brain cells, including their synaptic connectivity nanometer...
Recognizing Families In the Wild (RFIW) is a large-scale kinship recognition challenge based on FIW dataset. This dataset largest databases for recognition, consisting of more than 13,000 family photos and 1,000 families. The number members in each range from 4 to 38. One tasks database is, given two individuals, predict whether they have any kin relationship or not. this paper, we present deep learning approach using Siamese Convolutional Neural Network Architecture quantify similarity...
A core component of human intelligence is the ability to identify abstract patterns inherent in complex, high-dimensional perceptual data, as exemplified by visual reasoning tasks such Raven's Progressive Matrices (RPM). Motivated goal designing AI systems with this capacity, recent work has focused on evaluating whether neural networks can learn solve RPM-like problems. Previous generally found that strong performance these problems requires incorporation inductive biases are specific RPM...
<title>Abstract</title> The neocortex is composed of microcircuits built from distinct cell types. Despite significant progress in characterizing these types, understanding the full synaptic connections individual excitatory cells remains elusive. This study investigates connectivity arguably most well recognized neuron neocortex: thick tufted layer 5 pyramidal also known as extra telencephalic (ET) neurons using a 1 mm3 publicly available electron microscopy dataset. analysis reveals that...
Human visual reasoning is characterized by an ability to identify abstract patterns from only a small number of examples, and systematically generalize those novel inputs. This capacity depends in large part on our represent complex inputs terms both objects relations. Recent work computer vision has introduced models with the extract object-centric representations, leading process multi-object inputs, but falling short systematic generalization displayed human reasoning. Other recent have...
Surgical workflow analysis is of importance for understanding onset and persistence surgical phases individual tool usage across surgery in each phase. It beneficial clinical quality control to hospital administrators planning. Video acquired during typically can be leveraged this task. Currently, a combination convolutional neural network (CNN) recurrent networks (RNN) are popularly used video general, not only being restricted videos. In paper, we propose multi-task learning framework...
Summary The neocortex is one of the most critical structures that makes us human, and it involved in a variety cognitive functions from perception to sensory integration motor control. Composed repeated modules, or microcircuits, relies on distinct cell types as its fundamental building blocks. Despite significant progress characterizing these 1–5 , an understanding complete synaptic partners associated with individual excitatory remain elusive. Here, we investigate connectivity arguably...
Deep neural networks have made tremendous gains in emulating human-like intelligence, and been used increasingly as ways of understanding how the brain may solve complex computational problems on which this relies. However, these still fall short of, therefore fail to provide insight into supports strong forms generalization humans are capable. One such case is out-of-distribution (OOD) – successful performance test examples that lie outside distribution training set. Here, we identify...