Search results for "neural"

A CNN Adaptive Model to Estimate PM10 Monitoring

2006

In this work we introduce a model for studying the distribution and control of atmospheric pollution from PM10. The model is based on the use of a cellular neural network (CNN) and more precisely on the integration of the mass-balance equation; at the same time it simulates the scenario regarding a planar grid describing the whole studied area (the city of Palermo) by means of a CNN and a set of Bayesian networks. The CNN allows us to define a grid system whose dynamic evolution is a redefinition of the diffusion equation that considers contributions coming from near cells for each element of the grid. Dynamics of each cell is influenced by meteorological effects and by parameters related t…

Can Oscillatory Alpha-Gamma Phase-Amplitude Coupling be Used to Understand and Enhance TMS Effects?

2019

Recent applications of simultaneous scalp electroencephalography (EEG) and transcranial magnetic stimulation (TMS) suggest that adapting stimulation to underlying brain states may enhance neuroplastic effects of TMS. It is often assumed that longer-lasting effects of TMS on brain function may be mediated by phasic interactions between TMS pulses and endogenous cortical oscillatory dynamics. The mechanisms by which TMS exerts its neuromodulatory effects, however, remain unknown. Here, we discuss evidence concerning the functional effects on synaptic plasticity of oscillatory cross-frequency coupling in cortical networks as a potential framework for understanding the neuromodulatory effects o…

Slight variations in components ratio affect odor pleasantness of a blending mixture

2009

International audience; Odors rely mainly on the perception of odorants mixtures but are commonly perceived as single undivided entities; nevertheless, the processes involved remain poorly explored. It has been reported that perceptual blending, based on configural olfactory processing, can lead odorant mixtures to give rise to an emergent odor quality not present in the components. Furthermore, very slight variations (just noticeable differences, jnd) in components concentrations were shown to be sufficient to modify the odor quality of a blending mixture. In the present study, we set out to examine whether jnd in components concentrations could also affect the odor pleasantness of a blend…

Power Line Monitoring through Data Integrity Analysis with Q-Learning Based Data Analysis Network

2022

To monitor and handle big data obtained from electrical, electronic, electro-mechanical, and other equipment linked to the power grid effectively and efficiently, it is important to monitor them continually to gather information on power line integrity. We propose that data transmission analysis and data collection from tools like digital power meters may be used to undertake predictive maintenance on power lines without the need for specialized hardware like power line modems and synthetic data streams. Neural network models such as deep learning may be used for power line integrity analysis systems effectively, safely, and reliably. We adopt Q-learning based data analysis network for anal…

Adaptive Methodology for Designing a Predictive Model of Cardiac Arrhythmia Symptoms Based on Cubic Neural Unit

2017

A cubic neural unit is a kind of a higher-order neural unit which can be used for prediction tasks among others, in the medical field. The example of the tasks includes monitoring cardiac behavior in real-time either for preemptive treatment, or for supporting a doctor to reach a more accurate diagnosis. We propose a predictive model which has been developed as an application in open source code with the aim to make it publicly accessible for research community and medical professionals and also to decrease the implementation cost. The proposed model uses sample-by-sample adaptation of the gradient descent method with error backpropagation. This paper presents an application of a cubic neur…

Correlati neurali del processo schizofrenico.

2011

PROPAGATING INTERFACES IN A TWO-LAYER BISTABLE NEURAL NETWORK

2006

The dynamics of propagating interfaces in a bistable neural network is investigated. We consider the network composed of two coupled 1D lattices and assume that they interact in a local spatial point (pin contact). The network unit is modeled by the FitzHugh–Nagumo-like system in a bistable oscillator mode. The interfaces describe the transition of the network units from the rest (unexcited) state to the excited state where each unit exhibits periodic sequences of excitation pulses or action potentials. We show how the localized inter-layer interaction provides an "excitatory" or "inhibitory" action to the oscillatory activity. In particular, we describe the interface propagation failure a…

Pinning of a kink in a nonlinear diffusive medium with a geometrical bifurcation: Theory and experiments

2004

International audience; We study the dynamics of a kink propagating in a Nagumo chain presenting a geometrical bifurcation. In the case of weak couplings, we define analytically and numerically the coupling conditions leading to the pinning of the kink at the bifurcation site. Moreover, real experiments using a nonlinear electrical lattice confirm the theoretical and numerical predictions.

Does predictability matter? Effects of cue predictability on neurocognitive mechanisms underlying prospective memory

2015

Prospective memory (PM) represents the ability to successfully realize intentions when the appropriate moment or cue occurs. In this study, we used event-related potentials (ERPs) to explore the impact of cue predictability on the cognitive and neural mechanisms supporting PM. Participants performed an ongoing task and, simultaneously, had to remember to execute a pre-specified action when they encountered the PM cues. The occurrence of the PM cues was predictable (being signalled by a warning cue) for some participants and was completely unpredictable for others. In the predictable cue condition, the behavioural and ERP correlates of strategic monitoring were observed mainly in the ongoing…

Causality-Aware Convolutional Neural Networks for Advanced Image Classification and Generation

2023

Smart manufacturing uses emerging deep learning models, and particularly Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), for different industrial diagnostics tasks, e.g., classification, detection, recognition, prediction, synthetic data generation, security, etc., on the basis of image data. In spite of being efficient for these objectives, the majority of current deep learning models lack interpretability and explainability. They can discover features hidden within input data together with their mutual co-occurrence. However, they are weak at discovering and making explicit hidden causalities between the features, which could be the reason behind the parti…