Research Output per year
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 proceeding › Chapter › Scientific › peer-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 proceeding › Chapter › Scientific › peer-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 journal › Article › Scientific › peer-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 journal › Article › Scientific › peer-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 proceeding › Chapter › Scientific › peer-review