Search results for "Network models"

The role of network connectivity on epileptiform activity.

2021

AbstractA number of potentially important mechanisms have been identified as key players to generate epileptiform activity, such as genetic mutations, activity-dependent alteration of synaptic functions, and functional network reorganization at the macroscopic level. Here we study how network connectivity at cellular level can affect the onset of epileptiform activity, using computational model networks with different wiring properties. The model suggests that networks connected as in real brain circuits are more resistant to generate seizure-like activity. The results suggest new experimentally testable predictions on the cellular network connectivity in epileptic individuals, and highligh…

A performance based approach for the analysis of urban water distribution systems under drought conditions

2009

The present study proposes some performance indicators for analysing urban water distribution system characterised by intermittent supply service. Specific indicators have been proposed for investigating the reduction of quality of water service under resources scarcity conditions. A modelling procedure has been implemented in order to allow for analysing complex network schemes in which users self adapt to scarcity conditions. The procedure has been applied to analyse the performance of an existing supply system in Palermo (Italy). The network and users’ behaviour in intermittent distribution conditions have been monitored in order to calibrate the model and verify the reliability of the p…

Analysis and simulation of creativity learning by means of artificial neural networks

2007

The paper presents a new neural network approach for analysis and simulation of creative behavior. The used concept of Dynamically Controlled Neural Gas (DyCoNG) entails a combination of Dynamically Controlled Network [Perl, J. (2004a). A neural network approach to movement pattern analysis. Human Movement Science,23, 605-620] and Growing Neural Gas (Fritzke, 1995) by quality neurons. A quality neuron reflects the rareness of a piece of information and therefore can measure the originality of a recorded activity that was assigned to the neuron during the network training. The DyCoNG approach was validated using data from a longitudinal field-based study. The creative behavior of 42 particip…

Dynamic Pattern Recognition in Sport by Means of Artificial Neural Networks

2008

Behavioural processes like those in sports, motor activities or rehabilitation are often the object of optimization methods. Such processes are often characterized by a complex structure. Measurements considering them may produce a huge amount of data. It is an interesting challenge not only to store these data, but also to transform them into useful information. Artificial Neural Networks turn out to be an appropriate tool to transform abstract numbers into informative patterns that help to understand complex behavioural phenomena. The contribution presents some basic ideas of neural network approaches and several examples of application. The aim is to give an impression of how neural meth…

Introduction

1998

Energy and Network Models for the Intelligent Control of Distributed Systems

2014

This paper presents detailed energy and network models for the intelligent control of a distributed system which also comprises power generation from renewables. The two models provide interactive energy monitoring, intelligent control and power demand balancing at home, block and neighbor level. Particularly, it is here described how the energy model works and also details of how it has been validated are here given. Moreover, it is shown how the energy model simulation results are transferred to the network simulation software SynerGEE, set up by GL Noble Denton Industrial Services Ltd (UK), in order to fully perform network simulations.

On the structural connectivity of large-scale models of brain networks at cellular level

2021

AbstractThe brain’s structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model’s connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the …

Neural networks for animal science applications: Two case studies

2006

Abstract Artificial neural networks have shown to be a powerful tool for system modelling in a wide range of applications. In this paper, we focus on neural network applications to intelligent data analysis in the field of animal science. Two classical applications of neural networks are proposed: time series prediction and clustering. The first task is related to the prediction of weekly milk production in goat flocks, which includes a knowledge discovery stage in order to analyse the relative relevance of the different variables. The second task is the clustering of goat flocks; it is used to analyse different livestock surveys by using self-organizing maps and the adaptive resonance theo…