VIOLA - A multi-purpose and web-based visualization tool for neuronal-network simulation output

Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity and measured activity in cortical tissue. Spatiotemporal patterns of activity propagating across the cortical surface as observed experimentally can for example be described by neuronal network models with layered geometry and distance-dependent connectivity. The interpretation of the resulting stream of multi-modal and multi-dimensional simulation data calls for integrating interactive visualization steps into existing simulation-analysis workflows. Here, we present a set of interactive visualization concepts called views for the visual analysis of activity data in topological network models, and a corresponding reference implementation VIOLA (VIsualization Of Layer Activity). The software is a lightweight, open-source, web-based and platform-independent application combining and adapting modern interactive visualization paradigms, such as coordinated multiple views, for massively parallel neurophysiological data. For a use-case demonstration we consider spiking activity data of a two-population, layered point-neuron network model subject to a spatially confined excitation originating from an external population. With the multiple coordinated views, an explorative and qualitative assessment of the spatiotemporal features of neuronal activity can be performed upfront of a detailed quantitative data analysis of specific aspects of the data. Furthermore, ongoing efforts including the European Human Brain Project aim at providing online user portals for integrated model development, simulation, analysis and provenance tracking, wherein interactive visual analysis tools are one component. Browser-compatible, web-technology based solutions are therefore required. Within this scope, with VIOLA we provide a first prototype.Comment: 38 pages, 10 figures, 3 table

Mathematical statistics vs machine learning: towards an intelligent modeling framework for soil and plant growth processes

Mestrado de dupla diplomação com a Kuban State Agrarian UniversityThe work described in this dissertation focuses on the methods for analyzing MS and ML that are used in PF. The purpose of the work is to investigate these methods on their practical application to a specific set of data. In the course of the work, the following tasks were completed: the current state of affairs in the field of PF was investigated, the theoretical foundations of the methods of MS and ML were investigated, which were subjected to practical tests on a specific set of data. Conclusions were drawn about the advantages and disadvantages of these methods. A selection of works of scientists engaged in research on the introduction of a specific set of nutrients into the soil was also investigated. The most important contributions to this work are the practical application of various methods of analysis, as well as the design of a DST designed to help farmers integrate PF into their pilot training farms.O trabalho descrito nesta dissertação versa sobre métodos e técnicas no âmbito da Estatística Matemática e de ML usados para efeitos de previsão de colheitas e tratamento de solos em agricultura de precisão. O objetivo do trabalho é investigar esses métodos em sua aplicação prática a um conjunto específico de dados. No decorrer do trabalho, foram realizadas as seguintes tarefas: investigou-se a situação atual no campo da agricultura de precisão, investigaram-se os fundamentos teóricos dos métodos e técnicas da estatística matemática e de ML. Estes métodos e técnicas foram submetidos a testes práticos em um conjunto específico de dados. Foram tiradas conclusões sobre as vantagens e desvantagens desses métodos: Uma seslção de trabalhos científicos relacionados com a investigação sobre a introdução de um conjunto específico de nutrientes no solo foram também investigados. As contribuições mais importantes para este trabalho são a aplicação prática de vários métodos de análise, bem como o projeto de uma ferramenta de apoio à decisão projetada para ajudar os agricultores a integrar a agricultura de precisão nas suas propriedades agrícolas