Idx_2020

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Recent publications (from 2020)

A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients

Calesella, F., Testolin, A., De Filippo De Grazia, M., & Zorzi, M. (2021). A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients. Brain Informatics, 8(1), 1-13.

PDF document icon Calesella et al - Brain Informatics - 2021.pdf — PDF document, 3.33 MB (3487234 bytes)

A Developmental Approach for Training Deep Belief Networks

Zambra, M., Testolin, A., & Zorzi, M. (2023). A developmental approach for training deep belief networks. Cognitive Computation, 15(1), 103-120.

PDF document icon Zambra et al. - 2022 - Cognitive Computation.pdf — PDF document, 4.43 MB (4649860 bytes)

Automated detection of dolphin whistles with convolutional networks and transfer learning

Nur Korkmaz, B., Diamant, R., Danino, G., & Testolin, A. (2023). Automated detection of dolphin whistles with convolutional networks and transfer learning. Frontiers in Artificial Intelligence, 6, 1099022.

PDF document icon Korkmaz et al. - Frontiers in AI - 2023.pdf — PDF document, 893 KB (914497 bytes)

AUV navigation using cues in the sand ripples

Shalev, H., Nagar, L., Abu, A., Testolin, A., & Diamant, R. (2023). AUV navigation using cues in the sand ripples. Autonomous Robots, 47(1), 95-107.

PDF document icon Shalev et al. 2022 - Autonomous Robots.pdf — PDF document, 2.66 MB (2789328 bytes)

Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets

Testolin, A., & Diamant, R. (2020). Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets. Sensors, 20(10), 2945.

PDF document icon TestolinDiamant-Sensors-2020.pdf — PDF document, 8.96 MB (9396271 bytes)

Detecting submerged objects using active acoustics and deep neural networks: A test case for pelagic fish

Testolin, A., Kipnis, D., & Diamant, R. (2020). Detecting submerged objects using active acoustics and deep neural networks: A test case for pelagic fish. IEEE Transactions on Mobile Computing, 21(8), 2776-2788.

PDF document icon Testolin et al. 2021 - IEEE TMC.pdf — PDF document, 2.96 MB (3103380 bytes)

Do estimates of numerosity really adhere to Weber’s law? A reexamination of two case studies

Testolin, A., & McClelland, J. L. (2020). Do estimates of numerosity really adhere to Weber’s law? A reexamination of two case studies. Psychonomic Bulletin & Review, 1-11.

PDF document icon Testolin_McClelland-2020-Psychonomic_Bulletin_&_Review.pdf — PDF document, 1.38 MB (1443375 bytes)

Do estimates of numerosity really adhere to Weber’s law? A reexamination of two case studies

Testolin, A., & McClelland, J. L. (2021). Do estimates of numerosity really adhere to Weber’s law? A reexamination of two case studies. Psychonomic Bulletin & Review, 28, 158-168.

PDF document icon Testolin_McClelland-2021-Psychonomic_Bulletin_&_Review.pdf — PDF document, 1.38 MB (1448486 bytes)

Electrophysiological signatures of resting state networks predict cognitive deficits in stroke

Romeo Z, Mantini D, Durgoni E, Passarini L, Meneghello F, Zorzi M. Electrophysiological signatures of resting state networks predict cognitive deficits in stroke. Cortex. 2021;138:59-71

PDF document icon 1-s2.0-S001094522100054X-main.pdf — PDF document, 1.37 MB (1439869 bytes)

Emergence of Network Motifs in Deep Neural Networks

Zambra, M., Maritan, A., & Testolin, A. (2020). Emergence of Network Motifs in Deep Neural Networks. Entropy, 22(2), 204.

PDF document icon Zambra.et.al.-Entropy-2020.pdf — PDF document, 6.49 MB (6801416 bytes)

Evidence of language-related left hypofrontality in Major Depression: An EEG Beta band study

Spironelli, C., Maffei, A., Romeo, Z., Piazzon, G., Padovan, G., Magnolfi, G., … Angrilli, A. (2020). Evidence of language-related left hypofrontality in Major Depression : An EEG Beta band study. Scientific Reports, 10,8166.

PDF document icon Spironelli_et_al-2020-Scientific_Reports.pdf — PDF document, 1.98 MB (2073015 bytes)

L’approccio moderno all’intelligenza artificiale e la rivoluzione del deep learning

Testolin, A., & Zorzi, M. (2021). L’approccio moderno all’intelligenza artificiale e la rivoluzione del deep learning. Giornale italiano di psicologia, 48(2), 313-334.

PDF document icon Testolin and Zorzi 2021 - GIP.pdf — PDF document, 406 KB (415910 bytes)

L’approccio moderno all’intelligenza artificiale e la rivoluzione del deep learning

Testolin, A., & Zorzi, M. (2021). L’approccio moderno all’intelligenza artificiale e la rivoluzione del deep learning. Giornale italiano di psicologia, 48(2), 313-334.

PDF document icon Testolin and Zorzi 2021 - GIP.pdf — PDF document, 406 KB (415910 bytes)

Learning Numerosity Representations with Transformers: Number Generation Tasks and Out-of-Distribution Generalization

Boccato, T., Testolin, A., & Zorzi, M. (2021). Learning Numerosity Representations with Transformers: Number Generation Tasks and Out-of-Distribution Generalization. Entropy, 23(7), 857.

PDF document icon Boccato et al. Entropy 2021.pdf — PDF document, 1.59 MB (1666161 bytes)

Methodological Issues in Evaluating Machine Learning Models for EEG Seizure Prediction: Good Cross-Validation Accuracy Does Not Guarantee Generalization to New Patients

Shafiezadeh, S., Duma, G. M., Mento, G., Danieli, A., Antoniazzi, L., Del Popolo Cristaldi, F., ... & Testolin, A. (2023). Methodological issues in evaluating machine learning models for EEG seizure prediction: Good cross-validation accuracy does not guarantee generalization to new patients. Applied Sciences, 13(7), 4262.

PDF document icon Shafiezadeh et al. - Applied Sciences - 2023.pdf — PDF document, 2.36 MB (2477732 bytes)

Numerosity discrimination in deep neural networks: Initial competence, developmental refinement and experience statistics

Testolin, A., Zou, W. Y., & McClelland, J. L. (2020). Numerosity discrimination in deep neural networks: Initial competence, developmental refinement and experience statistics. Developmental Science

PDF document icon Testolin.et.al.-DevSci-2020.pdf — PDF document, 1.40 MB (1468640 bytes)

Poor numerical performance of guppies tested in a Skinner box

Gatto, E., Testolin, A., Bisazza, A., Zorzi, M., & Lucon-Xiccato, T. (2020). Poor numerical performance of guppies tested in a Skinner box. Scientific Reports, 10(1), 1-9.

PDF document icon Gatto_et_al-2020-Scientific_Reports.pdf — PDF document, 1.25 MB (1311924 bytes)

Sensorimotor, Attentional, and Neuroanatomical Predictors of Upper Limb Motor Deficits and Rehabilitation Outcome after Stroke

D’Imperio D, Romeo Z, Maistrello L, et al. Sensorimotor , Attentional , and Neuroanatomical Predictors of Upper Limb Motor Deficits and Rehabilitation Outcome after Stroke. Neural Plast. 2021;2021.

PDF document icon 8845685.pdf — PDF document, 1.22 MB (1275745 bytes)

The Challenge of Modeling the Acquisition of Mathematical Concepts

Testolin, A. (2020). The challenge of modeling the acquisition of mathematical concepts. Frontiers in Human Neuroscience, 14.

PDF document icon Testolin - FrontHumNeurosc 2020.pdf — PDF document, 2.11 MB (2215396 bytes)

Visual sense of number vs. sense of magnitude in humans and machines

Testolin, A., Dolfi, S., Rochus, M., & Zorzi, M. (2020). Visual sense of number vs. sense of magnitude in humans and machines. Scientific reports, 10(1), 1-13.

PDF document icon Testolin_et_al-2020-Scientific_Reports.pdf — PDF document, 3.70 MB (3876567 bytes)