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Personal profile

Research interests

Artificial (General) Intelligence, Deep Learning, Continual Learning, Meta-Learning, Deep Reinforcement Learning, Computational Neuroscience, Artificial Life.

Education/Academic qualification

Computer science, PhD, University of Sheffield

Computer science, Master’s Degree, University of Edinburgh

Computer science, Honours Degree, Sant'Anna School of Advanced Studies

Computer science, Bachelor’s Degree, University of Pisa

Fingerprint Dive into the research topics where Giacomo Spigler is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Sensory feedback Engineering & Materials Science
Neural networks Engineering & Materials Science
Denoising Mathematics
Learning algorithms Engineering & Materials Science
Neurons Engineering & Materials Science
Confidence Mathematics
Chemical activation Engineering & Materials Science
Neural Networks Mathematics

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2017 2019

A new sensory feedback system for lower-limb amputees: Assessment of discrete vibrotactile stimuli perception during walking

Filosa, M., Cesini, I., Martini, E., Spigler, G., Vitiello, N., Oddo, C. & Crea, S., 2019, Biosystems and Biorobotics. Springer International Publishing AG, p. 105-109 5 p. (Biosystems and Biorobotics; vol. 22).

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Sensory feedback
Chemical activation

A wearable haptic feedback system for assisting lower-limb amputees in multiple locomotion tasks

Cesini, I., Spigler, G., Prasanna, S., Taxis, D., Dell’Agnello, F., Martini, E., Crea, S., Vitiello, N., Mazzoni, A. & Oddo, C. M., 2019, Biosystems and Biorobotics. Springer International Publishing AG, p. 115-119 5 p. (Biosystems and Biorobotics; vol. 22).

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Convergence of regular spiking and intrinsically bursting Izhikevich neuron models as a function of discretization time with Euler method

Gunasekaran, H., Spigler, G., Mazzoni, A., Cataldo, E. & Oddo, C. M., 20 Jul 2019, In : Neurocomputing. 350, p. 237-247 11 p.

Research output: Contribution to journalArticleScientificpeer-review

Neurons
Bionics
Computational efficiency
Dynamic models
Robotics

Denoising Autoencoders for Overgeneralization in Neural Networks

Spigler, G., 21 May 2019, In : IEEE Transactions on Pattern Analysis and Machine Intelligence. p. 1-1 1 p.

Research output: Contribution to journalArticleScientificpeer-review

Open Access
Denoising
Confidence
Neural Networks
Neural networks
Open set

The temporal singularity: Time-accelerated simulated civilizations and their implications

Spigler, G., 2018, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, p. 207-216 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10999 LNAI).

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review