Active Learning Pipeline for Brain Mapping in a High Performance Computing Environment

This paper describes a scalable active learning pipeline prototype for large-scale brain mapping that leverages high performance computing power. It enables high-throughput evaluation of algorithm results, which, after human review, are used for iterative machine learning model training. Image processing and machine learning are performed in a batch layer. Benchmark testing of image processing using pMATLAB shows that a 100$\times$ increase in throughput (10,000%) can be achieved while total processing time only increases by 9% on Xeon-G6 CPUs and by 22% on Xeon-E5 CPUs, indicating robust scalability. The images and algorithm results are provided through a serving layer to a browser-based user interface for interactive review. This pipeline has the potential to greatly reduce the manual annotation burden and improve the overall performance of machine learning-based brain mapping.Comment: 6 pages, 5 figures, submitted to IEEE HPEC 2020 proceeding

Neuron segmentation using incomplete and noisy labels via adaptive learning with structure priors

Department of Computer Science and EngineeringRecent advances in machine learning have demonstrated significant success in biomedical image segmentation. Most existing high-quality segmentation algorithms rely on supervised learning with full training labels. However, segmentation is more susceptible to label qualitynotably, generating accurate labels in biomedical data is a labor- and time-intensive task.Especially, structure neuronal images are hard to obtain full annotation because of the entangled shape of each structure. In this thesis, a neuron structure semantic segmentation algorithm is proposed on a noise label.I assume that the label has noise and propose two new novel loss functions. Adaptive loss is applied to noise pixels in different labels with prediction in partially annotated labels. These fluorescence images may have confidence that can leverage prior knowledge when each pixel has intensity. Reconstruction loss is suggested that can be regularized of neuronal cell structures to reduce false segmentation near noisy labels. Additionally, This study is aimed to verify that our method preserves the connectivity of linear structure through a novel evaluation matrix.ope

Anatomical and functional characterization of neocortical circuits involved in transforming whisker sensory processing into goal-directed licking

The choice of an action upon perception of an external stimulus, arriving at a sensory organ of an animal, depends on previous experiences and outcomes throughout its life. In the rodent brain, the underlying mechanisms involved in simple sensorimotor transformations, such as the detection of a whisker stimulus through goal-directed licking, still remain largely unknown. In this thesis, using as a model the mouse somatosensory system, I explored the anatomical and functional properties of neuronal circuits at different stages of this cortical processing. To start with, using state-of-the-art viral tracing techniques, I investigated the thalamocortical circuits relaying sensory signals to the primary and secondary whisker somatosensory cortices (wS1, wS2). Challenging the "classical" views, the results indicated two streams of information carrying whisker-selective tactile signals. The principal trigeminal nucleus (Pr5) innervates the ventral posterior medial nucleus of the thalamus (VPM) and finally reaching layer 4 of wS1 while the spinal trigeminal nucleus (Sp5) through the rostral part of the posterior medial (POm) thalamus drives the layer 4 of wS2. Finally, a caudal part of the POm, which does not receive brainstem input, innervates layer 1 and layer 5A. Apart from their anatomical differences, those pathways conveyed distinct whisker sensory signals during goal-directed behaviors. Afterwards, I studied the cortical control of jaw and tongue movements during licking for rewards, using multisensory and multimotor whisker detection tasks. The data revealed a frontal tongue-jaw primary motor area (tjM1) which is necessary and encodes for directional licking, independently of the sensory stimulus type, shedding light on how the neocortex orchestrates the main motor output of the animal. Subsequently, I focused on changes in the L2/3 neuronal networks of wS1 after learning of a whisker stimulus. Using as a benchmark a novel "fast" learning and reward-dependent whisker detection task, I carried out inactivations of wS1 during different stages of learning and chronic two-photon (2P) calcium imaging in the L2/3 of the C2 barrel column. The inactivation results indicated that wS1 is indispensable for the acquisition of the novel stimulus and the execution of the task at expert levels. Moreover, the neural data suggested a learning-induced and "long-lasting" enhancement in the whisker sensory responses even when animals were unmotivated to lick. At a network level, a re-organization of the neuronal circuits was observed at different timescales with some of the alterations accompanying the rapid changes in the animal behavior. Additionally, the changes in the whisker sensory responses of neurons in wS1, after learning, were projection-pathway specific with wS2-projecting neurons showing higher whisker responses than whisker primary motor cortex (wM1)-projecting ones. In the final part, acknowledging the importance of a better characterization of the cortical-cortical communication of wS1, I described recent technical advancements in neuronal reconstructions. In vivo single-cell electroporation combined with 2P tomography and registration to a digital atlas, demonstrated the diversity of the projection targets of neurons in the L2/3 of wS1. Overall, I presented different results which contribute to a pre-existing body of research and help to decipher fundamentals and yet highly complex neural computations of the mammalian brain