Alberto Arturo Vergani

Alberto Arturo Vergani
Scuola Superiore Sant'Anna | SSSUP · The Biorobotics Insititute

PhD in Computer Science and Mathematics

About

19
Publications
998
Reads
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17
Citations
Introduction
Alberto Arturo Vergani is a postdoctoral researcher in computational neuroscience working on modelling predictive biomarkers of early onset of dementia, at Computational Neuroengineering Lab, The BioRobotics Institute, Scuola Superiore Sant'Anna di Pisa, Polo Valdera, Pontedera, Italy.

Publications

Publications (19)
Poster
Full-text available
The main results are that by tuning synaptic delay in a general model of V1 cortex, we found an incremental pre-activation of the topographic map arising in relation to medium and long moving dot trajectories, while cells fire without anticipation across the short trajectory and in relation to control conditions (fixed delay and flash). These resul...
Poster
Full-text available
In the poster is presented a model of primates primary visual cortex (V1) which tries to mimic recent results showing a differential neural processing of a moving stimulus with respect to its trajectory. We found that by using large excitatory and small inhibitory connections, inhibitory cells show a observable pre-activation with the long trajecto...
Poster
Full-text available
A bump attractor network is a model that implements a competitive neuronal process emerging from a spike pattern related to an input source. Since the bump network could behave in many ways, this paper explores some critical limits of the parameter space using various positive and negative weights and an increasing size of the input spike sources T...
Article
Full-text available
Networks of spiking neurons can have persistently firing stable bump attractors to represent continuous spaces (like temperature). This can be done with a topology with local excitatory synapses and local surround inhibitory synapses. Activating large ranges in the attractor can lead to multiple bumps, that show repeller and attractor dynamics; how...
Poster
Full-text available
Networks of spiking neurons can have persistently firing stable bump attractors to represent continuous spaces (like temperature). This can be done with a topology with local excitatory synapses and local surround inhibitory synapses. Activating large ranges in the attractor can lead to multiple bumps, that show repeller and attractor dynamics; how...
Preprint
Full-text available
A bump attractor network is a model that implements a competitive neuronal process emerging from a spike pattern related to an input source. Since the bump network could behave in many ways, this paper explores some critical limits of the parameter space using various positive and negative weights and an increasing size of the input spike sources T...
Poster
Full-text available
The poster represents a RS-fMRI analysis of one subject by using fuzzy c-means clustering algorithm. The goal was to establish the optimal number of temporal classes, that range from 2 to 5.
Preprint
Full-text available
This work aims to compute the number of temporal classes within a resting state fMRI examination. Usually, during a resting state experiment the subject is scanned with eyes open or closed without behaving a particular task (it is a passive paradigm). The brain scans are a collection of volumes in temporal succession. Each volume is a whole functio...
Chapter
In computational neuroimaging, the analysis of functional Magnetic Resonance Images (fMRIs) using fuzzy clustering methods is a promising data driven approach to explore brain functional connectivity. In this complex domain, accurate evaluation procedures based on suitable indexes, able to identify optimal clustering results, are of great values st...
Article
Full-text available
RS-fMRI data analysis for functional connectivity explorations is a challenging topic in computational neuroimaging. Several approaches have been investigated to discover whole-brain data features. Among these, clustering techniques based on Competitive Learning (CL) and Spectral Methods (SM) have been shown effective in providing useful informatio...
Chapter
We used model-free methods to explore the brain’s functional properties adopting a partitioning procedure based on cross-clustering. We selected Fuzzy C-Means (FCM) and Neural Gas (NG) algorithms to find spatial patterns with temporal features and temporal patterns with spatial features. We applied these algorithms to a shared fMRI repository of fa...
Chapter
This paper presents a fuzzy logic framework for dental caries and erosion risk assessment. Two interdependent modules are implemented within a cloud architecture. The first module is a fuzzy expert system designed for physicians and expert users, able to provide an active support in formulating risk judgements. The second module is oriented to gene...
Preprint
Full-text available
In this contribution, the clustering procedure based on K-Means algorithm is studied as an inverse problem, which is a special case of the ill-posed problems. The attempts to improve the quality of the clustering inverse problem drive to reduce the input data via Principal Component Analysis (PCA). Since there exists a theorem by Ding and He that l...
Article
In the present work, we investigate the usefulness of a new representation of the results obtained by fMRI data analysis, named weighted activation vector (WAV), built based on statistical parametric mapping. A software package for the generation and management of WAVs is illustrated. It is designed to support single-subject, multi-temporal and col...
Conference Paper
Full-text available
RS-fMRI data analysis for functional connectivity explorations is a challenging topic in computational neuroimaging. Several approaches have been investigated to discover whole-brain data features. Among these, clustering techniques based on Soft Competitive Learning (SCL) have been shown effective in providing useful information in various context...
Conference Paper
Full-text available
In the present work we use pattern vectors derived from Statistical Parametric Map, generated from a group of artificial and in-house collected fMRI data, to conduct cluster analysis. Two clustering algorithms, self-organizing map (SOM) and growing neural gas (GNG), are selected to explore inherent properties in the brain functional data. As seen i...

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