Emergence of a stable cortical map for neuroprosthetic control.

Cortical control of neuroprosthetic devices is known to require neuronal adaptations. It remains unclear whether a stable cortical representation for prosthetic function can be stored and recalled in a manner that mimics our natural recall of motor skills. Especially in light of the mixed evidence for a stationary neuron-behavior relationship in cortical motor areas, understanding this relationship during long-term neuroprosthetic control can elucidate principles of neural plasticity as well as improve prosthetic function. Here, we paired stable recordings from ensembles of primary motor cortex neurons in macaque monkeys with a constant decoder that transforms neural activity to prosthetic movements. Proficient control was closely linked to the emergence of a surprisingly stable pattern of ensemble activity, indicating that the motor cortex can consolidate a neural representation for prosthetic control in the presence of a constant decoder. The importance of such a cortical map was evident in that small perturbations to either the size of the neural ensemble or to the decoder could reversibly disrupt function. Moreover, once a cortical map became consolidated, a second map could be learned and stored. Thus, long-term use of a neuroprosthetic device is associated with the formation of a cortical map for prosthetic function that is stable across time, readily recalled, resistant to interference, and resembles a putative memory engram

High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference

We propose a data-driven method for recovering miss-ing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based on multi-view depth information provided as part of the input. It also includes a 3D fully convolutional (3DFCN) module that further enriches the global structure representation according to volumetric information in the input. Under the guidance of the global structure network, the local geometry refinement network takes as input lo-cal 3D patches around missing regions, and progressively produces a high-resolution, complete surface through a volumetric encoder-decoder architecture. Our method jointly trains the global structure inference and local geometry refinement networks in an end-to-end manner. We perform qualitative and quantitative evaluations on six object categories, demonstrating that our method outperforms existing state-of-the-art work on shape completion.Comment: 8 pages paper, 11 pages supplementary material, ICCV spotlight pape

SPIRE Point Source Catalog Explanatory Supplement

The Spectral and Photometric Imaging Receiver (SPIRE) was launched as one of the scientific instruments on board of the space observatory Herschel. The SPIRE photometer opened up an entirely new window in the Submillimeter domain for large scale mapping, that up to then was very difficult to observe. There are already several catalogs that were produced by individual Herschel science projects. Yet, we estimate that the objects of only a fraction of these maps will ever be systematically extracted and published by the science teams that originally proposed the observations. The SPIRE instrument performed its standard photometric observations in an optically very stable configuration, only moving the telescope across the sky, with variations in its configuration parameters limited to scan speed and sampling rate. This and the scarcity of features in the data that require special processing steps made this dataset very attractive for producing an expert reduced catalog of point sources that is being described in this document. The Catalog was extracted from a total of 6878 unmodified SPIRE scan map observations. The photometry was obtained by a systematic and homogeneous source extraction procedure, followed by a rigorous quality check that emphasized reliability over completeness. Having to exclude regions affected by strong Galactic emission, that pushed the limits of the four source extraction methods that were used, this catalog is aimed primarily at the extragalactic community. The result can serve as a pathfinder for ALMA and other Submillimeter and Far-Infrared facilities. 1,693,718 sources are included in the final catalog, splitting into 950688, 524734, 218296 objects for the 250\mu m, 350\mu m, and 500\mu m bands, respectively. The catalog comes with well characterized environments, reliability, completeness, and accuracies, that single programs typically cannot provide

Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks

Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial for making treatment decisions, but can be challenging even for experienced radiologists. The diagnostic procedure is based on the detection and recognition of the different ILD pathologies in thoracic CT scans, yet their manifestation often appears similar. In this study, we propose the use of a deep purely convolutional neural network for the semantic segmentation of ILD patterns, as the basic component of a computer aided diagnosis (CAD) system for ILDs. The proposed CNN, which consists of convolutional layers with dilated filters, takes as input a lung CT image of arbitrary size and outputs the corresponding label map. We trained and tested the network on a dataset of 172 sparsely annotated CT scans, within a cross-validation scheme. The training was performed in an end-to-end and semi-supervised fashion, utilizing both labeled and non-labeled image regions. The experimental results show significant performance improvement with respect to the state of the art

Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization

Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability to learn from continuous streams of information is referred to as lifelong learning and represents a long-standing challenge for neural network models due to catastrophic forgetting. Computational models of lifelong learning typically alleviate catastrophic forgetting in experimental scenarios with given datasets of static images and limited complexity, thereby differing significantly from the conditions artificial agents are exposed to. In more natural settings, sequential information may become progressively available over time and access to previous experience may be restricted. In this paper, we propose a dual-memory self-organizing architecture for lifelong learning scenarios. The architecture comprises two growing recurrent networks with the complementary tasks of learning object instances (episodic memory) and categories (semantic memory). Both growing networks can expand in response to novel sensory experience: the episodic memory learns fine-grained spatiotemporal representations of object instances in an unsupervised fashion while the semantic memory uses task-relevant signals to regulate structural plasticity levels and develop more compact representations from episodic experience. For the consolidation of knowledge in the absence of external sensory input, the episodic memory periodically replays trajectories of neural reactivations. We evaluate the proposed model on the CORe50 benchmark dataset for continuous object recognition, showing that we significantly outperform current methods of lifelong learning in three different incremental learning scenario

Nuclear Physics for Cultural Heritage

Nuclear physics applications in medicine and energy are well known and widely reported. Less well known are the many important nuclear and related techniques used for the study, characterization, assessment and preservation of cultural heritage. There has been enormous progress in this field in recent years and the current review aims to provide the public with a popular and accessible account of this work. The Nuclear Physics Division of the EPS represents scientists from all branches of nuclear physics across Europe. One of its aims is the dissemination of knowledge about nuclear physics and its applications. This review is led by Division board member Anna Macková, Head of the Tandetron Laboratory at the Nuclear Physics Institute of the Czech Academy of Sciences, and the review committee includes four other members of the nuclear physics board interested in this area: Faiçal Azaiez, Johan Nyberg, Eli Piasetzky and Douglas MacGregor. To create a truly authoritative account, the Scientific Editors have invited contributions from leading experts across Europe, and this publication is the combined result of their work. The review is extensively illustrated with important discoveries and examples from archaeology, pre-history, history, geography, culture, religion and curation, which underline the breadth and importance of this field. The large number of groups and laboratories working in the study and preservation of cultural heritage across Europe indicate the enormous effort and importance attached by society to this activity

Analyzing Modular CNN Architectures for Joint Depth Prediction and Semantic Segmentation

This paper addresses the task of designing a modular neural network architecture that jointly solves different tasks. As an example we use the tasks of depth estimation and semantic segmentation given a single RGB image. The main focus of this work is to analyze the cross-modality influence between depth and semantic prediction maps on their joint refinement. While most previous works solely focus on measuring improvements in accuracy, we propose a way to quantify the cross-modality influence. We show that there is a relationship between final accuracy and cross-modality influence, although not a simple linear one. Hence a larger cross-modality influence does not necessarily translate into an improved accuracy. We find that a beneficial balance between the cross-modality influences can be achieved by network architecture and conjecture that this relationship can be utilized to understand different network design choices. Towards this end we propose a Convolutional Neural Network (CNN) architecture that fuses the state of the state-of-the-art results for depth estimation and semantic labeling. By balancing the cross-modality influences between depth and semantic prediction, we achieve improved results for both tasks using the NYU-Depth v2 benchmark.Comment: Accepted to ICRA 201

Temporally Graded Activation of Neocortical Regions in Response to Memories of Different Ages

The temporally graded memory impairment seen in many neurobehavioral disorders implies different neuroanatomical pathways and/or cognitive mechanisms involved in storage and retrieval of memories of different ages. A dynamic interaction between medial-temporal and neocortical brain regions has been proposed to account for memory\u27s greater permanence with time. Despite considerable debate concerning its time-dependent role in memory retrieval, medial-temporal lobe activity has been well studied. However, the relative participation of neocortical regions in recent and remote memory retrieval has received much less attention. Using functional magnetic resonance imaging, we demonstrate robust, temporally graded signal differences in posterior cingulate, right middle frontal, right fusiform, and left middle temporal regions in healthy older adults during famous name identification from two disparate time epochs. Importantly, no neocortical regions demonstrated greater response to older than to recent stimuli. Our results suggest a possible role of these neocortical regions in temporally dating items in memory and in establishing and maintaining memory traces throughout the lifespan. Theoretical implications of these findings for the two dominant models of remote memory functioning (Consolidation Theory and Multiple Trace Theory) are discussed