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Research outline

My scientific activity concerns the study of the dynamics of biologically plausible neural networks (cortical areas and hippocampus) using statistical mechanics techniques and complex systems theory. In addition, I work in collaboration with Luca Pugliese to apply neural networks to the analysis of remote sensing images.

Dynamics of biological neural networks

(S.Scarpetta A de Candia PLOS ONE 2013) Abbiamo introdotto un parametro d'ordine che misura l'overlap (somiglianza)tra la attività della rete e i pattern  immagazzinati.Abbiamo trovato che, in funzione dell'eccitabilità della rete, sono possibili differenti regimi.Alla soglia critica   le fluttuzioni del parametro  d'ordine sono massimizzate(fig.1), come avviene  nelle transizioni di fase del secondo ordine, e  le distribuzioni delle size e delle durate delle valanghe neurali seguono delle leggi a potenza (fig.2),come sperimentalmente trovato.

Experimental techniques allow today to measure the activity of many interacting neurons with high temporal and spatial precision, both in-vivo and in-vitro. We have studied the dynamics of this complex system consisting of many cortical neurons interacting through a LIF neuron model with asymmetric connections. Initially proposed by our group to explain the phenomenon of the so-called theta phase precession in the hippocampus, it was then studied in 2010 from the point of view of the storage capacity of dynamic patterns
(S.Scarpetta et al FRONT.IN SYNAP.NEUROSCI.2010, JCNS 2012).
The idea that the neural system works at the critical point has recently been put forward to maximize efficiency and flexibility. This hypothesis is currently the subject of our modeling (S.Scarpetta et al PLOS ONE 2013).

Applications of Neural Networks for Automatic Image Discrimination

In collaboration with Luca Pugliese (IIASS) I developed an unsupervised method for automatic discrimination of remote sensing images, while in collaboration with Flora Giudicepietro (of the Vesuvian Observatory INGV Naples) I developed an automatic discrimination system of seismic signals based on a network supervised neural type.

Research Group:

Dott.ssa Silvia Scarpetta  


S. Scarpetta (2019) Critical Behavior and Memory Function in a Model of Spiking Neurons with a Reservoir of Spatio-Temporal Patterns. In: Tomen N., Herrmann J., Ernst U. (eds) The Functional Role of Critical Dynamics in Neural Systems. Springer Series on Bio- and Neurosystems, vol 11. Springer

Neural Avalanches at the Critical Point between Replay and Non-Replay of Spatiotemporal Patterns
Silvia Scarpetta, Antonio de Candia
PLOS ONE (ISSN:1932-6203), pp.64162- 64170, Vol. 8 doi:  10.1371/journal.pone.0064162, 2013.

Storage capacity of phase-coded patterns in sparse neural networks
S Scarpetta, F Giacco, A de Candia
EPL (Europhysics Letters) 95 (2), 28006

Uncertainty Analysis for the Classification of cialishowtobuy Multispectral Satellite Images Using SVMs and SOMs
F Giacco, C Thiel, L Pugliese, S Scarpetta, M Marinaro
Geoscience and Remote Sensing, IEEE Transactions on 48 (10), 3769-3779, 2010

Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity
S Scarpetta, A De Candia, F Giacco
Frontiers in Synaptic Neuroscience 2, 2010

Automatic classification of seismic signals at Mt. Vesuvius volcano, Italy, using neural networks
S Scarpetta, F Giudicepietro, EC Ezin, S Petrosino, E Del Pezzo, M Martini ...
Bulletin of the Seismological Society of America 95 (1), 185-196, 2005


.............. in costruzione


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