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  1. Pubblicazioni

JOURNAL OF NEUROSCIENCE METHODS

Rivista
Codice:
E092924
ISSN:
0165-0270
  • Dati Generali

Dati Generali

Pubblicazioni (49)

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A Cellular Neural Network methodology for the automated segmentation of multiple sclerosis lesions
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A Wrapped Multi-label Classifier for the Automatic Diagnosis and Prognosis of Alzheimer's Disease.
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A brain slice experimental model to study the generation and the propagation of focally-induced epileptiform activity
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A laser interferometer for sub-nanometre measurements in the cochlea.
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A low-cost open-architecture taste delivery system for gustatory fMRI and BCI experiments
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A reliable strategy for single-cell RNA sequencing analysis using cryoconserved primary cortical cells.
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AN ALGORITHM COMPARING THE 2 MONONUCLEAR CURVES OF CHOICE REACTION-TIMES IN PIGEONS
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Advances in modelling alpha-synuclein-induced Parkinson's diseases in rodents: Virus-based models versus inoculation of exogenous preformed toxic species
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Aging effect on head motion: A Machine Learning study on resting state fMRI data
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An unexpected target of spinal direct current stimulation: Interhemispheric connectivity in humans
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Automated extraction and classification of dynamic metrical features of morphological development in dissociated Purkinje neurons
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Automated extraction and classification of dynamic metrical features of morphological development in dissociated Purkinje neurons
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Bayesian multi-dipole modelling in the frequency domain
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Cross-correlation analysis of intracellularly recorded synaptic activities: an evaluation of the method through computer simulation
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Editorial on Special Issue: Machine learning on MCI.
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Frequency-based approach to the study of semantic brain networks connectivity
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Genetically encoded sensors for Chloride concentration
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Imaging neuronal calcium fluorescence at high spatio-temporal resolution
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In vivo autofluorescence spectrofluorometry of central serotonin.
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Integration of confocal and atomic force microscopy images
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Latency correction in sparse neuronal spike trains
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Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy
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Machine learning on Parkinson's disease? Let's translate into clinical practice.
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Machine learning on Parkinson's disease? Let's translate into clinical practice.
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Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy
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Machine-Learning neuroimaging challenge for automated diagnosis of mild cognitive impairment: Lessons learnt.
Articolo
Mapping dependencies of BOLD signal change to end-tidal CO2: Linear and nonlinear modeling, and effect of physiological noise correction
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Measures of spike train synchrony for data with multiple time scales
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Measuring multiple spike train synchrony
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Measuring spike train synchrony
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Measuring spike train synchrony
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Mediterranean Neuroscience Methods 2017
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Micropatterned surface electrode for massive selective stimulation of intraepidermal nociceptive fibres
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Personality biomarkers of pathological gambling: A machine learning study.
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Pseudo painting/air bubble technique for planar lipid bilayers
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Segmentation of MR brain images through discriminant analysis
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Segmentation of magnetic resonance brain images through discriminant analysis
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Segmentation of magnetic resonance brain images through discriminant analysis
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Single particle tracking of acyl carrier protein (ACP)-tagged TrkA receptors in PC12nnr5 cells
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Source modeling of ElectroCorticoGraphy (ECoG) data: Stability analysis and spatial filtering
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Superparamagnetic iron oxide nanocolloids in MRI studies of neuroinflammation
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The densitometric physical fractionator for counting neuronal populations: application to a mouse model of familial amyotrophic lateral sclerosis
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Time-resolved and time-scale adaptive measures of spike train synchrony
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Tractography in Amyotrophic Lateral Sclerosis using a novel probabilistic tool: a study with tract-based reconstruction compared to voxel-based approach
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Two-dimensional gel electrophoresis of peripheral nerve proteins: Optimized sample preparation.
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Using spike train distances to identify the most discriminative neuronal subpopulation
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Verrillo L, Mangano E, Drongitis D, Merelli I, Pischedda F, Piccoli G, Consolandi C, Bordoni R and Miano MG.
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What can spike train distances tell us about the neural code?
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Which spike train distance is most suitable for distinguishing rate and temporal coding?
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