Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Simulations of the cultured granule neuron excitability
2003
Abstract We have developed a biophysical model of a cultured rat cerebellar granule neuron and simulated its excitability under different experimental conditions. The basic excitability properties of such a small neuron; the specific action potential waveforms, the overall firing patterns induced by current stimulations, and the linear frequency-current relation, are the main model constraints. Simulations show that for a one-compartmental granule neuron model, the constraints are met using six voltage- and time-dependent ion channel types and calcium dynamics linked to BK Ca ion channel function. This kind of model of a single neuron forms a solid basis for building the increasingly more c…
Early GABAergic circuitry in the cerebral cortex.
2013
In the cerebral cortex GABAergic signaling plays an important role in regulating early developmental processes, for example, neurogenesis, migration and differentiation. Transient cell populations, namely Cajal-Retzius in the marginal zone and thalamic input receiving subplate neurons, are integrated as active elements in transitory GABAergic circuits. Although immature pyramidal neurons receive GABAergic synaptic inputs already at fetal stages, they are integrated into functional GABAergic circuits only several days later. In consequence, GABAergic synaptic transmission has only a minor influence on spontaneous network activity during early corticogenesis. Concurrent with the gradual devel…
The subplate and early cortical circuits.
2010
The developing mammalian cerebral cortex contains a distinct class of cells, subplate neurons (SPns), that play an important role during early development. SPns are the first neurons to be generated in the cerebral cortex, they reside in the cortical white matter, and they are the first to mature physiologically. SPns receive thalamic and neuromodulatory inputs and project into the developing cortical plate, mostly to layer 4. Thus SPns form one of the first functional cortical circuits and are required to relay early oscillatory activity into the developing cortical plate. Pathophysiological impairment or removal of SPns profoundly affects functional cortical development. SPn removal in v…
Improving the accuracy of rainfall prediction using a regionalization approach and neural networks
2018
Spatial and temporal analysis of precipitation patterns has become an intense research topic in contemporary climatology. Increasing the accuracy of precipitation prediction can have valuable results for decision-makers in a specific region. Hence, studies about precipitation prediction on a regional scale are of great importance. Artificial Neural Networks (ANN) have been widely used in climatological applications to predict different meteorological parameters. In this study, a method is presented to increase the accuracy of neural networks in precipitation prediction in Chaharmahal and Bakhtiari Province in Iran. For this purpose, monthly precipitation data recorded at 42 rain gauges duri…
Ideal Chaotic Pattern Recognition is achievable: The Ideal-M-AdNN - its design and properties
2013
Published version of a chapter in the book: Transactions on Computational Collective Intelligence XI. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-41776-4_2 This paper deals with the relatively new field of designing a Chaotic Pattern Recognition (PR) system. The benchmark of such a system is the following: First of all, one must be able to train the system with a set of “training” patterns. Subsequently, as long as there is no testing pattern, the system must be chaotic. However, if the system is, thereafter, presented with an unknown testing pattern, the behavior must ideally be as follows. If the testing pattern is not one of the trained patterns, the system …
Inverse simulated annealing for the determination of amorphous structures
2013
We present a new and efficient optimization method to determine the structure of disordered systems in agreement with available experimental data. Our approach permits the application of accurate electronic structure calculations within the structure optimization. The new technique is demonstrated within density functional theory by the calculation of a model of amorphous carbon.
Adversarial reverse mapping of equilibrated condensed-phase molecular structures
2020
A tight and consistent link between resolutions is crucial to further expand the impact of multiscale modeling for complex materials. We herein tackle the generation of condensed molecular structures as a refinement -- backmapping -- of a coarse-grained structure. Traditional schemes start from a rough coarse-to-fine mapping and perform further energy minimization and molecular dynamics simulations to equilibrate the system. In this study we introduce DeepBackmap: A deep neural network based approach to directly predict equilibrated molecular structures for condensed-phase systems. We use generative adversarial networks to learn the Boltzmann distribution from training data and realize reve…
Non-exponential relaxation in disordered materials: Phenomenological correlations and spectrally selective experiments
1998
Abstract In most glass-forming materials external perturbations are relaxed in a non-exponential fashion. It is shown that the degree of non-exponentiality is phenomenologically correlated with the departure from simple thermally activated behavior as measured by the fragility index m. In model glass formers such as the Ge-As-Se ternary alloy, and to some degree for amorphous materials in general, the correlations with these properties are observed also for other characteristic features. These include the specific heat step and the aging kinetics in the glass transformation range. While phenomenological correlations have proven very useful for rationalizing the properties of many glass form…
Artificial neural network for quantitative determination of total protein in yogurt by infrared spectrometry
2009
Abstract A method has been introduced for quantitative determination of protein content in yogurt samples based on the characteristic absorbance of protein in 1800–1500 cm− 1 spectral region by mid-FTIR spectroscopy and chemometrics. Successive Projection Algorithm (SPA) wavelength selection procedure, coupled with feed forward Back-Propagation Artificial Neural Network (BP-ANN) model was the benefited chemometric technique. Relative Error of Prediction (REP) in BP-ANN and SPA-BP-ANN methods for training set was 7.25 and 3.70 respectively. Considering the complexity of the sample, the ANN model was found to be reliable, while the proposed method is rapid and simple, without any sample prepa…
Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion
2020
Miniaturized hyperspectral imaging techniques have developed rapidly in recent years and have become widely available for different applications. Combining calibrated hyperspectral imagery with inverse physically based reflectance models is an interesting approach for estimating chlorophyll concentrations that are good indicators of vegetation health. The objective of this study was to develop a novel approach for retrieving chlorophyll a and b values from remotely sensed data by inverting the stochastic model of leaf optical properties using a one-dimensional convolutional neural network. The inversion results and retrieved values are validated in two ways: A classical machine learning val…