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1. Multi-modal Decoding of Reach-to-Grasping from EEG and EMG via Neural Networks

Author

Borra, Davide, Fraternali, Matteo, Ravanelli, Mirco, Magosso, Elisa, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Suen, Ching Yee, editor, Krzyzak, Adam, editor, Ravanelli, Mirco, editor, Trentin, Edmondo, editor, Subakan, Cem, editor, and Nobile, Nicola, editor

Published
2024
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2. EEG Features Learned by Convolutional Neural Networks Reflect Alterations of Social Stimuli Processing in Autism

Author

Borra, Davide, Diciotti, Stefano, Magosso, Elisa, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wand, Michael, editor, Malinovská, Kristína, editor, Schmidhuber, Jürgen, editor, and Tetko, Igor V., editor

Published
2024
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3. BRAVE: Bio Responsive Alert VEst

Author

Salice, Fabio, Magosso, Elisa, Re, Sofia, Golino, Marco, Masciadri, Andrea, Comai, Sara, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Goos, Gerhard, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Miesenberger, Klaus, editor, Peňáz, Petr, editor, and Kobayashi, Makoto, editor

Published
2024
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9. A Bayesian-Optimized Convolutional Neural Network to Decode Reach-to-Grasp from Macaque Dorsomedial Visual Stream

Author

Borra, Davide, Filippini, Matteo, Ursino, Mauro, Fattori, Patrizia, Magosso, Elisa, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nicosia, Giuseppe, editor, Ojha, Varun, editor, La Malfa, Emanuele, editor, La Malfa, Gabriele, editor, Pardalos, Panos, editor, Di Fatta, Giuseppe, editor, Giuffrida, Giovanni, editor, and Umeton, Renato, editor

Published
2023
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26. A neurocomputational analysis of visual bias on bimanual tactile spatial perception during a crossmodal exposure

Author

Cuppini, Cristiano, Magosso, Elisa, Monti, Melissa, Ursino, Mauro, Yau, Jeffrey M., Cuppini, Cristiano, Magosso, Elisa, Monti, Melissa, Ursino, Mauro, and Yau, Jeffrey M.

Subjects
Cellular and Molecular Neuroscience, Spatial Processing, neural network, visual bias, bimanual interaction, tactile perception, crossmodal competition, Touch Perception, Touch, Space Perception, Cognitive Neuroscience, Visual Perception, Neuroscience (miscellaneous), Humans, Sensory Systems
Abstract

Vision and touch both support spatial information processing. These sensory systems also exhibit highly specific interactions in spatial perception, which may reflect multisensory representations that are learned through visuo-tactile (VT) experiences. Recently, Wani and colleagues reported that task-irrelevant visual cues bias tactile perception, in a brightness-dependent manner, on a task requiring participants to detect unimanual and bimanual cues. Importantly, tactile performance remained spatially biased after VT exposure, even when no visual cues were presented. These effects on bimanual touch conceivably reflect cross-modal learning, but the neural substrates that are changed by VT experience are unclear. We previously described a neural network capable of simulating VT spatial interactions. Here, we exploited this model to test different hypotheses regarding potential network-level changes that may underlie the VT learning effects. Simulation results indicated that VT learning effects are inconsistent with plasticity restricted to unisensory visual and tactile hand representations. Similarly, VT learning effects were also inconsistent with changes restricted to the strength of inter-hemispheric inhibitory interactions. Instead, we found that both the hand representations and the inter-hemispheric inhibitory interactions need to be plastic to fully recapitulate VT learning effects. Our results imply that crossmodal learning of bimanual spatial perception involves multiple changes distributed over a VT processing cortical network.

Published
2022
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30. Sensory Fusion

Author

Ursino, Mauro, Magosso, Elisa, Cuppini, Cristiano, Cutsuridis, Vassilis, editor, Hussain, Amir, editor, and Taylor, John G., editor

Published
2011
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35. Learning the Lexical Aspects of a Second Language at Different Proficiencies: A Neural Computational Study

Author

Cuppini, Cristiano, Magosso, Elisa, and Ursino, Mauro

Abstract

We present an original model designed to study how a second language (L2) is acquired in bilinguals at different proficiencies starting from an existing L1. The model assumes that the conceptual and lexical aspects of languages are stored separately: conceptual aspects in distinct topologically organized Feature Areas, and lexical aspects in a single Lexical Network. Lexical and semantic aspects are then linked together during Hebbian learning phases by presenting L2 lexical items and their L1 translation equivalents. The model hypothesizes the existence of a competitive mechanism to solve conflicts and simulate language switching tasks. Results demonstrate that, at the beginning of training, an L2 lexicon must parasitize its L1 equivalent to access its conceptual meaning. At intermediate proficiency, L2 items may evoke their semantics independently of L1, but with a high risk of interference. At higher proficiency, the L2 representation becomes progressively similar to the L1 representation, according to Green's (2003) convergence hypothesis.

Published
2013
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37. A Neural Network for the Analysis of Multisensory Integration in the Superior Colliculus

Author

Cuppini, Cristiano, Magosso, Elisa, Serino, Andrea, Di Pellegrino, Giuseppe, Ursino, Mauro, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, de Sá, Joaquim Marques, editor, Alexandre, Luís A., editor, Duch, Włodzisław, editor, and Mandic, Danilo, editor

Published
2007
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39. Neural Bases of Peri-Hand Space Plasticity through Tool-Use: Insights from a Combined Computational-Experimental Approach

Author

Magosso, Elisa, Ursino, Mauro, and di Pellegrino, Giuseppe

Abstract

Visual peripersonal space (i.e., the space immediately surrounding the body) is represented by multimodal neurons integrating tactile stimuli applied on a body part with visual stimuli delivered near the same body part, e.g., the hand. Tool use may modify the boundaries of the peri-hand area, where vision and touch are integrated. The neural mechanisms underlying such plasticity have not been yet identified. To this aim, neural network modelling may be integrated with experimental research. In the present work, we pursued two main objectives: (i) using an artificial neural network to postulate some physiological mechanisms for peri-hand space plasticity in order to account for in-vivo data; (ii) validating model predictions with an ad-hoc behavioural experiment on an extinction patient. The model assumes that the modification of peri-hand space arises from a Hebbian growing of visual synapses converging into the multimodal area, which extends the visual receptive field (RF) of the peripersonal bimodal neurons. Under this hypothesis, the model is able to interpret and explain controversial results in the current literature, showing how different tool-use tasks during the learning phase result in different re-sizing effects of the peri-hand space. Importantly, the model also implies that, after tool-use, a far visual stimulus acts as a near one, independently of whether the tool is present or absent in the subject's hand. This prediction has been validated by an in-vivo experiment on a right brain-damaged patient suffering from visual-tactile extinction. This study demonstrates how neural network modelling may integrate with experimental studies, by generating new predictions and suggesting novel experiments to investigate cognitive processes. (Contains 3 tables and 12 figures.)

Published
2010
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42. Interpretable Convolutional Neural Networks for Decoding and Analyzing Neural Time Series Data

Author

Magosso, Elisa, Borra, Davide <1992>, Magosso, Elisa, and Borra, Davide <1992>

Abstract

Machine learning is widely adopted to decode multi-variate neural time series, including electroencephalographic (EEG) and single-cell recordings. Recent solutions based on deep learning (DL) outperformed traditional decoders by automatically extracting relevant discriminative features from raw or minimally pre-processed signals. Convolutional Neural Networks (CNNs) have been successfully applied to EEG and are the most common DL-based EEG decoders in the state-of-the-art (SOA). However, the current research is affected by some limitations. SOA CNNs for EEG decoding usually exploit deep and heavy structures with the risk of overfitting small datasets, and architectures are often defined empirically. Furthermore, CNNs are mainly validated by designing within-subject decoders. Crucially, the automatically learned features mainly remain unexplored; conversely, interpreting these features may be of great value to use decoders also as analysis tools, highlighting neural signatures underlying the different decoded brain or behavioral states in a data-driven way. Lastly, SOA DL-based algorithms used to decode single-cell recordings rely on more complex, slower to train and less interpretable networks than CNNs, and the use of CNNs with these signals has not been investigated. This PhD research addresses the previous limitations, with reference to P300 and motor decoding from EEG, and motor decoding from single-neuron activity. CNNs were designed light, compact, and interpretable. Moreover, multiple training strategies were adopted, including transfer learning, which could reduce training times promoting the application of CNNs in practice. Furthermore, CNN-based EEG analyses were proposed to study neural features in the spatial, temporal and frequency domains, and proved to better highlight and enhance relevant neural features related to P300 and motor states than canonical EEG analyses. Remarkably, these analyses could be used, in perspective, to design novel EEG biomarke

Published
2022

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