Search results for "neural model"
showing 9 items of 9 documents
Neural modelling of friction material cold performance
2008
The complex and highly non-linear phenomena involved during braking are primarily caused by friction materials’ characteristics. The final friction materials' characteristics are determined by their compositions, manufacturing, and the brake's operating conditions. Analytical models of friction materials' behaviour are difficult, even impossible, to obtain for the case of different brakes' operating conditions. That is why, in this paper, all relevant influences on the friction materials' cold performance have been integrated by means of artificial neural networks. The influences of 26 input parameters, defined by the friction materials' composition (18 ingredients), manufacturing (five pa…
Modeling the insect mushroom bodies: application to a delayed match-to-sample task.
2013
Despite their small brains, insects show advanced capabilities in learning and task solving. Flies, honeybees and ants are becoming a reference point in neuroscience and a main source of inspiration for autonomous robot design issues and control algorithms. In particular, honeybees demonstrate to be able to autonomously abstract complex associations and apply them in tasks involving different sensory modalities within the insect brain. Mushroom Bodies (MBs) are worthy of primary attention for understanding memory and learning functions in insects. In fact, even if their main role regards olfactory conditioning, they are involved in many behavioral achievements and learning capabilities, as …
A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning
2016
Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies n…
Neural Modeling of Greenhouse Gas Emission from Agricultural Sector in European Union Member Countries
2018
The present paper discusses a novel methodology based on neural network to determine agriculture emission model simulations. Methane and nitrous oxide are the key pollutions among greenhouse gases being a major contribution to climate changes because of their high potential global impact. Using statistical clustering (k-means and Ward’s method), five meaningful clusters of countries with similar level of greenhouse gases emission were identified. Neural modeling using multi-layer perceptron networks was performed for countries placed in particular groups. The parameters that characterize the quality of a network are the predictive errors (mainly validation and test) and they are high (0.97–…
Modelling the insect Mushroom Bodies: Application to sequence learning
2015
Learning and reproducing temporal sequences is a fundamental ability used by living beings to adapt behaviour repertoire to environmental constraints. This paper is focused on the description of a model based on spiking neurons, able to learn and autonomously generate a sequence of events. The neural architecture is inspired by the insect Mushroom Bodies (MBs) that are a crucial centre for multimodal sensory integration and behaviour modulation. The sequence learning capability coexists, within the insect brain computational model, with all the other features already addressed like attention, expectation, learning classification and others. This is a clear example that a unique neural struc…
Computational analysis of a multi-varied biomimetic neural network
2021
In recent years, astrocytes have emerged as such elements involved in inter-neuronal communication that they are increasingly considered in the synaptic coupling connecting neurons and synapses, thus constituting tripartite synapses. Many studies have focused on this astrocytic influence. With the description of the basic mechanisms of synapses, as well as the influence of gliotransmission, we emphasize that gliotransmission to extra-synaptic areas has so far been mainly studied in vivo and in vitro. We come closer to the paradigms used in AI, by proposing an analysis of the astrocyte influence on the variations of the parameters governing synaptic and extra-synaptic plasticity.We sought to…
Electronic implementation of a non-linear oscillator subjected to noise : application to the modeling of neuronal information coding
2011
We study the nonlinear FitzHugh-Nagumo model witch describes the dynamics of excitable neural element. It is well known that this system exhibits three different possible responses. Indeed, the system can be mono-stable, oscillatory or bistable. In the oscillatory regime, the system periodically responds by generating action potential. By contrast, in the mono-stable state the system response remains constant after a transient. Under certain conditions, the system can undergo a bifurcation between the stable and the oscillatory regime via the so called Andronov-Hopf bifurcation. In this Phd thesis, we consider the FitzHugh-Nagumo model in the stable state, that is set near the Andronov-Hopf…
Analyzing the metrics of the perceptual space in a new multistage physiological colour vision model
2009
In this work, the metric of a new multistage colour vision model, ATTD05, is assessed and a new colour difference formula is suggested. Firstly, the uniformity of the ATTD05 colour space was compared with that of CIECAM02 for some Munsell samples, because if the model yields a uniform perceptual space, we will be able to implement a colour difference formula as a Euclidian distance between two points. Secondly, we developed a new space based on the perceptual descriptors of the model: brightness, hue, colourfulness, and saturation. After that, we calculated the free parameters of the space that better fit the measured and experimental data of two datasets (small-magnitude and large-magnitud…
CHANGING PERSPECTIVE ON PERCEPTION PHYSIOLOGY: CAN YOU REALLY SEE WHAT IS HAPPENING?
2018
Perception is a complex, neural mechanism that requires organization and interpretation of input meaning and it has been a key topic in medicine, neuroscience and philosophy for centuries. Gestalt psychology proposed that the underlying mechanism is a constructive process that depends on both input of stimuli and the sensory-motor state of the agent. The Bayesian Brain hypothesis reframed it as probabilistic inference of previous beliefs, which are revised to accommodate new information. The Predictive Coding Theory proposes that this process is implemented through a top-down cascade of cortical predictions of lower level input and the concurrent propagation of a bottom-up prediction error …