Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Diffusion processes with ultrametric jumps
2007
Abstract In the theory of spin glasses the relaxation processes are modelled by random jumps in ultrametric spaces. One may argue that at the border of glassy and nonglassy phases the processes combining diffusion and jumps may be relevant. Using the Dirichlet form technique we construct a model of diffusion on the real line with jumps on the Cantor set. The jumps preserve the ultrametric feature of a random process on unit ball of 2-adic numbers.
Spatio-temporal dynamics of oscillatory network activity in the neonatal mouse cerebral cortex
2007
We used a 60-channel microelectrode array to study in thick (600-1000 microm) somatosensory cortical slices from postnatal day (P)0-P3 mice the spatio-temporal properties of early network oscillations. We recorded local non-propagating as well as large-scale propagating spontaneous oscillatory activity. Both types of activity patterns could never be observed in neocortical slices of conventional thickness (400 microm). Local non-propagating spontaneous oscillations with an average peak frequency of 15.6 Hz, duration of 1.7 s and maximal amplitude of 66.8 microV were highly synchronized in a network of approximately 200 microm in diameter. Spontaneous oscillations of lower frequency (10.4 Hz…
GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation
2018
In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac center-of-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results).…
Review on Higher-Order Neural Units to Monitor Cardiac Arrhythmia Patterns
2017
An electrocardiogram (ECG) is a non-invasive technique that checks for problems with the electrical activity of a patient’s heart. ECG is economical and extremely versatile. Some of its characteristics make it a very useful tool to detect cardiac pathologies. The ECG records a series of characteristic waves called PQRST; however, the QRS complex analysis enables the detection of a type of arrhythmia in an ECG. Technological developments enable the storage of a large amount of data, from which knowledge extraction is impossible without a powerful data processing tool; in particular, an adequate signal processing tool, whose output provides reliable parameters as a basis to make a precise cli…
New fitting scheme to obtain effective potential from Car-Parrinello molecular dynamics simulations: Application to silica
2008
A fitting scheme is proposed to obtain effective potentials from Car-Parrinello molecular dynamics (CPMD) simulations. It is used to parameterize a new pair potential for silica. MD simulations with this new potential are done to determine structural and dynamic properties and to compare these properties to those obtained from CPMD and a MD simulation using the so-called BKS potential. The new potential reproduces accurately the liquid structure generated by the CPMD trajectories, the experimental activation energies for the self-diffusion constants and the experimental density of amorphous silica. Also lattice parameters and elastic constants of alpha-quartz are well-reproduced, showing th…
Ordered networks of rat hippocampal neurons attached to silicon oxide surfaces.
2001
The control of neuronal cell position and outgrowth is of fundamental interest in the development of applications ranging from cellular biosensors to tissue engineering. We have produced rectangular networks of functional rat hippocampal neurons on silicon oxide surfaces. Attachment and network formation of neurons was guided by a geometrical grid pattern of the adhesion peptide PA22-2 which matches in sequence a part of the A-chain of laminin. PA22-2 was applied by contact printing onto the functionalised silicon oxide surface and was immobilised by hetero-bifunctional cross-linking with sulfo-GMBS. Geometric pattern matching was achieved by microcontact printing using a polydimethylsiloxa…
Spot compliant neuronal networks by structure optimized micro-contact printing
2001
Neuronal cell growth in vitro can be controlled with micropatterned structures of extracellular matrix proteins such as laminin. This technique is a powerful tool for studying neuronal cell function in order to increase experimental reproducibility and to specifically design innovative experimental setups. In this paper the correlation between the structural dimensions of the ECM pattern and the shape of the resulting cellular network is analyzed. The aim of the present study was to position neuronal cell bodies as precisely as possible and to induce directed cell differentiation. PCC7-MzN cells were cultured on laminin patterns. The line width, node size and gap size in-between cell adhesi…
Polysialic acid is required for dopamine D2 receptor-mediated plasticity involving inhibitory circuits of the rat medial prefrontal cortex.
2011
Decreased expression of dopamine D2 receptors (D2R), dysfunction of inhibitory neurotransmission and impairments in the structure and connectivity of neurons in the medial prefrontal cortex (mPFC) are involved in the pathogenesis of schizophrenia and major depression, but the relationship between these changes remains unclear. The polysialylated form of the neural cell adhesion molecule (PSA-NCAM), a plasticity-related molecule, may serve as a link. This molecule is expressed in cortical interneurons and dopamine, via D2R, modulates its expression in parallel to that of proteins related to synapses and inhibitory neurotransmission, suggesting that D2R-targeted antipsychotics/antidepressants…
Computation of inverse functions in a model of cerebellar and reflex pathways allows to control a mobile mechanical segment.
2003
Abstract The command and control of limb movements by the cerebellar and reflex pathways are modeled by means of a circuit whose structure is deduced from functional constraints. One constraint is that fast limb movements must be accurate although they cannot be continuously controlled in closed loop by use of sensory signals. Thus, the pathways which process the motor orders must contain approximate inverse functions of the bio-mechanical functions of the limb and of the muscles. This can be achieved by means of parallel feedback loops, whose pattern turns out to be comparable to the anatomy of the cerebellar pathways. They contain neural networks able to anticipate the motor consequences …
Cerebellar learning of bio-mechanical functions of extra-ocular muscles: modeling by artificial neural networks
2003
A control circuit is proposed to model the command of saccadic eye movements. Its wiring is deduced from a mathematical constraint, i.e. the necessity, for motor orders processing, to compute an approximate inverse function of the bio-mechanical function of the moving plant, here the bio-mechanics of the eye. This wiring is comparable to the anatomy of the cerebellar pathways. A predicting element, necessary for inversion and thus for movement accuracy, is modeled by an artificial neural network whose structure, deduced from physical constraints expressing the mechanics of the eye, is similar to the cell connectivity of the cerebellar cortex. Its functioning is set by supervised reinforceme…