Neural Computation Lab @ SISSA

We are interested in the computational principles that underlie the ability of the animal brain to perform efficient inference and prediction under tight resource constraints. We study behavior and cognition in animals and humans, and information processing in neuronal circuits.

We develop and employ techniques that draw from a broad range of approaches, including statistical learning, information theory, artificial neural networks, and Bayesian statistics. We care about open and reproducible science.

Eugenio Piasini, Principal Investigator (epiasini@sissa.it). Eugenio got his PhD from University College London, with a thesis on information processing in the input stage of the cerebellar cortex. After his PhD he was a postdoctoral researcher at the Italian Institute of Technology, working on neural coding and information theory. Later on, he was a Fellow of the Computational Neuroscience Initiative at the University of Pennsylvania. He joined SISSA in 2021.

Francesco Guido Rinaldi, PhD student (frinaldi@sissa.it). Francesco received his Master's Degree in Physics from the University of Rome "La Sapienza". He then worked as a research fellow at IIT under the supervision of Giancarlo Ruocco, focusing on graph theoretical properties of Symmetric Recurrent Neural Networks. He joined the Neural Computation Lab in 2021 as a Ph.D. student and is now studying the relationship between Intuitive Data Interpretation and Statistical Model Selection. In his free time, he delved into photography, digital painting, video editing, creative writing and has now mastered the art of switching hobbies.

Sara Varetti, PhD student (svaretti@sissa.it). Sara is a Phd Student from the Cognitive Neuroscience group at Sissa. She graduated in Physics from the University of Naples, carrying out a thesis about critical phenomena in the brain. Currently she works in collaboration with the Neural Newtorks group and the Neural Computation Lab group, where she investigates the learning dynamics of recurrent neural networks, with biological plausibile learning algorithms, and their application to neural population codes. In her spare time, she enjoys hiking, photography, reading and cinema.

Ludovica De Paolis, PhD student (ldepaoli@sissa.it). I joined SISSA in 2022 as a PhD student in Cognitive Neuroscience. I have a BA in philosophy and a MSc in cognitive science, during which I spent an exchange period in the US and one in South Korea. I'm currently working on deep neural networks for modeling vision. Specifically, I program generative models with latent to investigate the statistics of visual textures. My research interests also include computational models of brain and language. Outside academia I enjoy rock climbing, playing the piano, nonfiction and yoga.

Daniele Tirinnanzi, PhD student (dtirinna@sissa.it). Daniele joined the Lab in 2022 as a Cognitive Neuroscience Master’s student and carried out his thesis at SISSA as part of a joint program with the University of Trento. He was born in Florence, where he got a BA in Psychology, and is currently enrolled in the National PhD in Artificial Intelligence for Health and Life Sciences, in collaboration with Università Campus Bio-Medico (Rome). For his Master’s thesis, he collaborated with the International Center for Theoretical Physics (Trieste) to compare the performance of humans and neural networks in an adaptation of a theoretical computer science problem. He is currently interested in normative models of behavior and sensory systems and in the comparison between biological and artificial intelligence. In his free time, he enjoys being in the nature, whether it’s doing sports, having a walk, or simply lying in the sun.

Monica Paoletti, PhD student (mpaolett@sissa.it). She joined the Lab in 2023 as a PhD student in Cognitive Neuroscience. She studied Mathematics at the University of Turin. She carried out her Master’s thesis in collaboration with Forschungszentrum Jülich, where she worked in Sonja Grün’s group of Computational Neuroscience, on the detection of Spatio-Temporal spike Patterns in neuronal data. She currently works in collaboration with Mathew Diamond’s Lab and her PhD research is focused on the development of the brain's model of choice strategies adopted by humans and rats in sequential decision-making experiments. In her free time, she enjoys hiking, painting and playing the transverse flute.

Carlo Orientale Caputo, PhD student (corienta@sissa.it). Carlo joined the Neural Computational Lab in 2024 as a PhD student in Cognitive Neuroscience. He earned his Bachelor's degree in Physics from the University Federico II of Naples, where he conducted a thesis on General Relativity. He pursued a Master's degree in Physics of Complex Systems, studying at Politecnico di Torino, SISSA, and Sorbonne University. His Master's thesis focused on a first-principle approach to deep learning, with a particular emphasis on deep belief networks. Currently he is working on neural population dynamics using machine learning tools like Neural ODE (Ordinary Differential Equations) to understand neural computation. In his free time, he enjoys staying active through sports like yoga and climbing or hiking with friends.

For current openings in the group, see the dedicated page.

See the teaching page.

Matteucci G, Piasini E, Zoccolan D. Unsupervised learning of mid-level visual representations. Current Opinion in Neurobiology 2023. doi:10.1016/j.conb.2023.102834

Piasini E*, Liu S*, Chaudhari P, Balasubramanian V, Gold J I. How Occam's razor guides human decision-making. Biorxiv 2023. doi:10.1101/2023.01.10.523479

Choi K*, Piasini E*, Díaz-Hernández E, Vargas Cifuentes L, Henderson N, Holly E, Subramaniyan M, Gerfen C, Fuccillo M. Distributed processing for value-based choice by prelimbic circuits targeting anterior-posterior dorsal striatal subregions in male mice. Nature Communications 2023. doi:10.1038/s41467-023-36795-4

Caramellino R*, Piasini E*, Buccellato A, Carboncino A, Balasubramanian V, Zoccolan D. Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes. eLife 2021. doi:10.7554/eLife.72081

Piasini E*, Soltuzu L*, Muratore P, Caramellino R, Vinken K, Op de Beeck H, Balasubramanian V, Zoccolan D. Temporal stability of stimulus representation increases along rodent visual cortical hierarchies. Nature Communications 2021. doi:10.1038/s41467-021-24456-3

Molano-Mazon M, Onken A, Piasini E*, Panzeri S*. Synthesizing realistic neural population activity patterns using generative adversarial networks. ICLR 2018. arXiv:1803.00338

Pica G, Piasini E, Safaai H, Runyan C A, Diamond M E, Fellin T, Kayser C, Harvey C D, Panzeri S. Quantifying how much sensory information in a neural code is relevant for behavior. NeurIPS 2017. arXiv:1712.02449

Pica G, Piasini E, Chicharro D, Panzeri S. Invariant components of synergy, redundancy, and unique information among three variables. Entropy 2017. doi:10.3390/e19090451

Panzeri S, Harvey C D, Piasini E, Latham P E, Fellin T. Cracking the neural code for sensory perception by combining statistics, intervention, and behavior. Neuron 2017. doi:10.1016/j.neuron.2016.12.036

Runyan C*, Piasini E*, Panzeri S, Harvey C. Distinct timescales of population coding across cortex. Nature 2017. doi:10.1038/nature23020

Billings G, Piasini E, Lőrincz A, Nusser Z, Silver R A. Network structure within the cerebellar input layer enables lossless sparse encoding. Neuron 2014. doi:10.1016/j.neuron.2014.07.020