Specification, Design and Development of a Pyramidal Learning Platform

The aim of this article is to design an online learning platform that allows tutoring between learners from different levels and the possibility of adding content to the fields of knowledge, using measures that assess quality. It is a versatile technological tool appropriate for the collaboration between different agents, both for acquiring new professional skills and for lifelong learning and formal regulated learning of education systems. Learning takes place through a two-way scheme: On the one hand, the learner is assisted through online tutoring by another highly qualified learner (that acts as a teacher). On the other hand, the same learner assists others from lower categories. In that way, their-skills will be consolidated by forcing reflection on the subject in order to explain it. The knowledge base is a wiki platform stratified in the same levels as the professional and training categories. This platform features evaluation and accounting modules for tutorials and contributions to the content of the wiki. The assignment of several disciples of a given level to each tutor of the superior level makes possible an exponential expansion that is of great interest for using the pyramidal school in projects of solidarity cooperation.This research was funded by the Valencian Innovation Agency (AVI) under scientific innovation unit (UCIE Ars Innovatio) of the University of Alicante. https://web.ua.es/es/ars-innovatio/unidad-cientifica-deinnovacion-ars-innovatio.html

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Bringing Open Data to Whole Slide Imaging

Supplementary information associated with Besson et al. (2019) ECDP 2019 Faced with the need to support a growing number of whole slide imaging (WSI) file formats, our team has extended a long-standing community file format (OME-TIFF) for use in digital pathology. The format makes use of the core TIFF specification to store multi-resolution (or "pyramidal") representations of a single slide in a flexible, performant manner. Here we describe the structure of this format, its performance characteristics, as well as an open-source library support for reading and writing pyramidal OME-TIFFs

System framework for autonomous data processing onboard next generation of nanosatellite

Progress within nanosatellite systems development makes niche commercial Earth observing missions feasible; however, despite advances in demonstrated data rates, these systems will remain downlink limited able to capture more data than can be returned to the ground cost-effectively in traditional raw or near-raw forms. The embedding of existing ground-based image processing algorithms into onboard systems is non-trivial especially in limited resource nanosatellites, necessitating new approaches. In addition, mission opportunities for systems beyond Earth orbit present additional challenges around relay availability and bandwidth, and delay-tolerance, leading to more autonomous approaches. This paper describes a framework for implementing autonomous data processing onboard resource-constrained nanosatellites, covering data selection, reduction, prioritization and distribution. The framework is based on high level requirements and aligned to existing off-the-shelf software and international standards. It is intended to target low-resource algorithms developed in other sectors including autonomous vehicles and commercial machine learning. Techniques such as deep learning and heuristic code optimization have been identified as both value-adding to the use cases studied and technically feasible. With the framework in place, work is now progressing within the consortium under UKSA Centre for Earth Observation and Instrument funding to deliver an initial prototype data chain implemented within a representative FPGA-based flight computer system

Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms

Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation. Neuromorphic devices offer a number of advantages over conventional computing architectures, such as high emulation speed or low power consumption, but this usually comes at the price of reduced configurability and precision. In this article, we investigate the consequences of several such factors that are common to neuromorphic devices, more specifically limited hardware resources, limited parameter configurability and parameter variations. Our final aim is to provide an array of methods for coping with such inevitable distortion mechanisms. As a platform for testing our proposed strategies, we use an executable system specification (ESS) of the BrainScaleS neuromorphic system, which has been designed as a universal emulation back-end for neuroscientific modeling. We address the most essential limitations of this device in detail and study their effects on three prototypical benchmark network models within a well-defined, systematic workflow. For each network model, we start by defining quantifiable functionality measures by which we then assess the effects of typical hardware-specific distortion mechanisms, both in idealized software simulations and on the ESS. For those effects that cause unacceptable deviations from the original network dynamics, we suggest generic compensation mechanisms and demonstrate their effectiveness. Both the suggested workflow and the investigated compensation mechanisms are largely back-end independent and do not require additional hardware configurability beyond the one required to emulate the benchmark networks in the first place. We hereby provide a generic methodological environment for configurable neuromorphic devices that are targeted at emulating large-scale, functional neural networks

Bi-directional and shared epigenomic signatures following proton and 56Fe irradiation.

The brain's response to radiation exposure is an important concern for patients undergoing cancer therapy and astronauts on long missions in deep space. We assessed whether this response is specific and prolonged and is linked to epigenetic mechanisms. We focused on the response of the hippocampus at early (2-weeks) and late (20-week) time points following whole body proton irradiation. We examined two forms of DNA methylation, cytosine methylation (5mC) and hydroxymethylation (5hmC). Impairments in object recognition, spatial memory retention, and network stability following proton irradiation were observed at the two-week time point and correlated with altered gene expression and 5hmC profiles that mapped to specific gene ontology pathways. Significant overlap was observed between DNA methylation changes at the 2 and 20-week time points demonstrating specificity and retention of changes in response to radiation. Moreover, a novel class of DNA methylation change was observed following an environmental challenge (i.e. space irradiation), characterized by both increased and decreased 5hmC levels along the entire gene body. These changes were mapped to genes encoding neuronal functions including postsynaptic gene ontology categories. Thus, the brain's response to proton irradiation is both specific and prolonged and involves novel remodeling of non-random regions of the epigenome

Roles of autism gene ARID1B in murine brain development and behavior

Autism spectrum disorder (ASD) and intellectual disability (ID) are highly prevalent neurodevelopmental disorders characterized by social and communication deficits, stereotyped behaviors, cognitive dysfunction, and deficits in adaptive behaviors. The pathogenesis underlying these disorders remains unknown, and thus no pharmacologic or genetic therapies are currently available. Recent progress in the field has shown that haploinsufficiency of the AT-rich interactive domain-containing 1B (ARID1B) gene is a genetic cause of ASD and ID. Our lab recently developed an Arid1b knockout mouse model to better study its role in the pathogenesis of these disorders. One theory regarding the cause of neurodevelopmental disorders is disruption of the excitatory/inhibitory balance in the brain. We previously showed that interneuron deficits lead to an excitatory/inhibitory imbalance in Arid1b knockout mice, playing a significant role in the observed behavioral phenotypes. Interneurons are highly heterogenous cell types in the brain; however, little is known regarding how the different subtypes modulate various behaviors. In chapter 2, we dissect the individual roles of the two most populous interneurons in the cerebral cortex, parvalbumin and somatostatin subtypes, in ASD/ID behaviors seen with ARID1B haploinsufficiency. We show that parvalbumin interneurons affect social and emotional behaviors, while somatostatin interneurons primarily affect stereotyped behaviors and cognitive function. In addition to interneuron deficits, several studies have also implicated altered neurite outgrowth of cortical projection neurons in ASD and ID. Furthermore, deficits in neurotrophic signaling, a master regulator of neurite outgrowth, is also frequently observed. In chapter 3, we examine a potential role of ARID1B in regulating neurite development of excitatory neurons during corticogenesis. We show that loss of the Arid1b gene leads to disrupted neurite outgrowth and altered development of the corpus callosum. Additionally, we suggest a likely role of ARID1B in the BDNF neurotrophic signaling pathway. Together, these studies provide insight into possible roles of ARID1B during neurogenesis, shedding further insight into the pathogenesis of ASD and ID