Reinforcement Learning May Demystify the Limited Human Motor Learning Efficacy Due to Visual-Proprioceptive Mismatch.

Vision and proprioception have fundamental sensory mismatches in delivering locational information, and such mismatches are critical factors limiting the efficacy of motor learning. However, it is still not clear how and to what extent this mismatch limits motor learning outcomes. To further the understanding of the effect of sensory mismatch on motor learning outcomes, a reinforcement learning algorithm and the simplified biomechanical elbow joint model were employed to mimic the motor learning process in a computational environment.

Proportional sway-based electrotactile feedback improves lateral standing balance.

Introduction: Plantar cutaneous augmentation is a promising approach in balance rehabilitation by enhancing motion-dependent sensory feedback. The effect of plantar cutaneous augmentation on balance has been mainly investigated in its passive form (e.g.

AdjointBackMapV2: Precise reconstruction of arbitrary CNN unit's activation via adjoint operators.

Adjoint operators have been found to be effective in the exploration of CNN's inner workings (Wan and Choe, 2022). However, the previous no-bias assumption restricted its generalization. We overcome the restriction via embedding input images into an extended normed space that includes bias in all CNN layers as part of the extended space and propose an adjoint-operator-based algorithm that maps high-level weights back to the extended input space for reconstructing an effective hypersurface.

AdjointBackMap: Reconstructing effective decision hypersurfaces from CNN layers using adjoint operators.

There are several methods in the exploration of Convolutional Neural Networks' (CNNs') inner workings. However, in general, finding the inverse of the function performed by CNNs as a whole is an ill-posed problem. In this paper, we propose a method based on adjoint operators to reconstruct, given an arbitrary unit in the CNN (except for the first convolutional layer), its effective hypersurface in the input space.

A Queryable Graph Representation of Vascular Connectivity in the Whole Mouse Brain.

We construct a graph representation for the topology and geometry of the vasculature presenting across the whole mouse brain (dataset: Knife-Edge Scanning Microscope Brain Atlas India Ink). We use our graph representation to calculate preliminary estimates of the average radius as 4:8 μm, total vascular volume as 1:1000 mm, total vascular surface area as 6:5511 cm, and total vascular length of 2866:6567 cm. We then isolate a posterior cerebral region, derive its graph representation, and then import that representation to a Neo4j graph database.

Data-Driven Synthetic Cerebrovascular Models For Validation Of Segmentation Algorithms.

We introduce a novel method to generate biologically grounded synthetic cerebrovasculature models in a datadriven fashion. First, the centerlines of vascular filaments embedded in an acquired imaging volume are obtained by a segmentation algorithm. That imaging volume is reconstructed from a graph encoding of the centerline (i.

Towards An Open-Source Framework For The Analysis Of Cerebrovasculature Structure.

The use of graphs to analyze cerebrovascular networks is quite common in studies of the microcirculation. While we have learned a lot from studies utilizing graphs as a tool for the analysis of microvessels, most methodologies for these procedures have only been described in brief and most are not publicly accessible. In this work, we introduce the foundation for an anticipated open-source framework that we hope will streamline the analysis of cerebrovascular structure.

Tracing and analysis of the whole mouse brain vasculature with systematic cleaning to remove and consolidate erroneous images.

Whole mouse brain microvascular images at submicrometer scale can be obtained by Knife-Edge Scanning Microscopy (KESM). However, due to the large size of the image dataset and the noise from the serial sectioning process of the KESM, whole mouse brain vascular reconstruction and analysis with submicrometer resolution have not been achieved yet, while several previous studies demonstrated manually selected small noise-free portion of the KESM dataset. In addition to the KESM dataset, there have been studies for vessel reconstruction and analysis of the whole mouse brain at lower resolution or of partial brain regions at submicrometer resolution.

Mapping the full vascular network in the mouse brain at submicrometer resolution.

Mapping the microvascular networks in the brain can lead to significant scientific and clinical insights. We developed a serial sectioning microscopy technique called the Knife-Edge Scanning Microscopy (KESM) to section and image the entire mouse brain at submicrometer resolution. In our effort to map the entire vascular network in the mouse brain, we perfused the vessels with India ink, and used KESM to image the prepared brain.

Automated neurovascular tracing and analysis of the knife-edge scanning microscope Rat Nissl data set using a computing cluster.

We present a novel, parallelizable algorithm capable of automatically reconstructing and calculating anatomical statistics of cerebral vascular networks embedded in large volumes of Rat Nissl-stained data. In this paper, we report the results of our method using Rattus somatosensory cortical data acquired using Knife-Edge Scanning Microscopy. Our algorithm performs the reconstruction task with averaged precision, recall, and F2-score of 0.

Knife-edge scanning microscopy for in silico study of cerebral blood flow: From biological imaging data to flow simulations.

Knife-edge scanning microscopy provides the capability to image whole-brain cerebral microvasculature of small organisms, such as mice, at sub-micron resolution, providing a feasible foundation for the reconstruction of circulatory pathways from the systemic to cellular scale. In this paper, we illustrate the feasibility of using this data to model cerebral blood flow using numerical simulations. Starting with a small vascular element in microcirculation of interest, we present its segmentation from the imaging-data volume, construction of its triangular surface mesh, assembly of its tetrahedral volumetric mesh from the surface, and then conclude with Stokes flow simulation of plasma through the microvascular vessel.

Fast submicrometer-scale imaging of whole zebrafish using the knife-edge scanning microscope.

Advances in high-resolution 3D microscopy have enabled the investigation of subcellular microstructures in biological specimen. For a full understanding of the organism's structure and function, it is mandatory to obtain data from the whole animal, not just parts of it. In this paper, we present our work with the Knife-Edge Scanning Microscope (KESM) for imaging a Nissl-stained whole zebrafish larva.

Learning to distinguish cerebral vasculature data from mechanical chatter in India-ink images acquired using knife-edge scanning microscopy.

We introduce a simple, yet effective, procedure for accurate classification of connected components embedded in biological images. In our method, a training set is generated from user-delineated features of manually-labeled examples; we subsequently train a classifier using the resultant training set. The overall process is described using imaging data acquired from an India-ink perfused C57BL/6J mouse brain using Knife Edge Scanning Microscopy.

A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition.

This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-large-scale-integration (VLSI)-based machine learning applications. To the best of the authors' knowledge, this is the first work that employs a bioinspired spike-based learning algorithm for the LSM. With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression.

Predictable internal brain dynamics in EEG and its relation to conscious states.

Consciousness is a complex and multi-faceted phenomenon defying scientific explanation. Part of the reason why this is the case is due to its subjective nature. In our previous computational experiments, to avoid such a subjective trap, we took a strategy to investigate objective necessary conditions of consciousness.

A microchip for quantitative analysis of CNS axon growth under localized biomolecular treatments.

Growth capability of neurons is an essential factor in axon regeneration. To better understand how microenvironments influence axon growth, methods that allow spatial control of cellular microenvironments and easy quantification of axon growth are critically needed. Here, we present a microchip capable of physically guiding the growth directions of axons while providing physical and fluidic isolation from neuronal somata/dendrites that enables localized biomolecular treatments and linear axon growth.

Parameter learning for alpha integration.

In pattern recognition, data integration is an important issue, and when properly done, it can lead to improved performance. Also, data integration can be used to help model and understand multimodal processing in the brain. Amari proposed α-integration as a principled way of blending multiple positive measures (e.

Multiscale exploration of mouse brain microstructures using the knife-edge scanning microscope brain atlas.

Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM).

Specimen preparation, imaging, and analysis protocols for knife-edge scanning microscopy.

Major advances in high-throughput, high-resolution, 3D microscopy techniques have enabled the acquisition of large volumes of neuroanatomical data at submicrometer resolution. One of the first such instruments producing whole-brain-scale data is the Knife-Edge Scanning Microscope (KESM), developed and hosted in the authors' lab. KESM has been used to section and image whole mouse brains at submicrometer resolution, revealing the intricate details of the neuronal networks (Golgi), vascular networks (India ink), and cell body distribution (Nissl).

Fast macro-scale transmission imaging of microvascular networks using KESM.

Accurate microvascular morphometric information has significant implications in several fields, including the quantification of angiogenesis in cancer research, understanding the immune response for neural prosthetics, and predicting the nature of blood flow as it relates to stroke. We report imaging of the whole mouse brain microvascular system at resolutions sufficient to perform accurate morphometry. Imaging was performed using Knife-Edge Scanning Microscopy (KESM) and is the first example of this technique that can be directly applied to clinical research.

Facilitating neural dynamics for delay compensation: a road to predictive neural dynamics?

Goal-directed behavior is a hallmark of cognition. An important prerequisite to goal-directed behavior is that of prediction. In order to establish a goal and devise a plan, one needs to see into the future and predict possible future events.

Extrapolative delay compensation through facilitating synapses and its relation to the flash-lag effect.

Neural conduction delay is a serious issue for organisms that need to act in real time. Various forms of flash-lag effect (FLE) suggest that the nervous system may perform extrapolation to compensate for delay. For example, in motion FLE, the position of a moving object is perceived to be ahead of a brief flash when they are actually colocalized.