Cell-type specific cholinergic modulation of the cortex

Thesis (Ph. D.)--Massachusetts Institute of Technology, Computational and Systems Biology Program, 2013.Cataloged from PDF version of thesis. "September 2013." Page 126 blank.Includes bibliographical references.The cholinergic innervation of the neocortex by afferent fibers originating in the nucleus basalis (NB) of the basal forebrain is implicated in modulating diverse neocortical functions including information processing, cortical plasticity, arousal and attention. To understand the physiological basis of these brain functions during cholinergic modulation, it is critical to identify the cortical circuit elements involved and define how their interactions contribute to cortical network dynamics. In this thesis, I present evidence showing how specific neuronal and glial cell types can be differentially modulated by acetylcholine (Ach), resulting in dynamic functional interactions during ACh-modulated information processing and cortical plasticity. Chapter 2 identifies somatostatin-expressing neurons as a dominant player in driving decorrelation and information processing through its intimate interactions with parvalbumin-expressing and pyramidal neurons. Chapter 3 highlights astrocytes and their interactions with pyramidal neurons as important drives for stimulus-specific cortical plasticity during cholinergic modulation. This is one of the earliest works that has mapped the functional role of distinct cell-types and their interactions to specific brain functions modulated by ACh, thereby setting the foundation for future studies to manipulate these specific functional interactions in both normal and diseased brains.by Naiyan Chen.Ph.D

Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild

Tracking and reconstructing 3D objects from cluttered scenes are the key components for computer vision, robotics and autonomous driving systems. While recent progress in implicit function has shown encouraging results on high-quality 3D shape reconstruction, it is still very challenging to generalize to cluttered and partially observable LiDAR data. In this paper, we propose to leverage the continuity in video data. We introduce a novel and unified framework which utilizes a neural implicit function to simultaneously track and reconstruct 3D objects in the wild. Our approach adapts the DeepSDF model (i.e., an instantiation of the implicit function) in the video online, iteratively improving the shape reconstruction while in return improving the tracking, and vice versa. We experiment with both Waymo and KITTI datasets and show significant improvements over state-of-the-art methods for both tracking and shape reconstruction tasks. Our project page is at https://jianglongye.com/implicit-tracking .Comment: Accepted to RA-L 2022 & IROS 2022. Project page: https://jianglongye.com/implicit-trackin

Sequence Level Semantics Aggregation for Video Object Detection

Video objection detection (VID) has been a rising research direction in recent years. A central issue of VID is the appearance degradation of video frames caused by fast motion. This problem is essentially ill-posed for a single frame. Therefore, aggregating features from other frames becomes a natural choice. Existing methods rely heavily on optical flow or recurrent neural networks for feature aggregation. However, these methods emphasize more on the temporally nearby frames. In this work, we argue that aggregating features in the full-sequence level will lead to more discriminative and robust features for video object detection. To achieve this goal, we devise a novel Sequence Level Semantics Aggregation (SELSA) module. We further demonstrate the close relationship between the proposed method and the classic spectral clustering method, providing a novel view for understanding the VID problem. We test the proposed method on the ImageNet VID and the EPIC KITCHENS dataset and achieve new state-of-the-art results. Our method does not need complicated postprocessing methods such as Seq-NMS or Tubelet rescoring, which keeps the pipeline simple and clean.Comment: ICCV 2019 camera read

3D Video Object Detection with Learnable Object-Centric Global Optimization

We explore long-term temporal visual correspondence-based optimization for 3D video object detection in this work. Visual correspondence refers to one-to-one mappings for pixels across multiple images. Correspondence-based optimization is the cornerstone for 3D scene reconstruction but is less studied in 3D video object detection, because moving objects violate multi-view geometry constraints and are treated as outliers during scene reconstruction. We address this issue by treating objects as first-class citizens during correspondence-based optimization. In this work, we propose BA-Det, an end-to-end optimizable object detector with object-centric temporal correspondence learning and featuremetric object bundle adjustment. Empirically, we verify the effectiveness and efficiency of BA-Det for multiple baseline 3D detectors under various setups. Our BA-Det achieves SOTA performance on the large-scale Waymo Open Dataset (WOD) with only marginal computation cost. Our code is available at https://github.com/jiaweihe1996/BA-Det.Comment: CVPR202