D. Zoccolan

发表

How Does the Brain Solve Visual Object Recognition?

James J. DiCarlo, Davide Zoccolan, Nicole C. Rust, 2012, Neuron.

What response properties do individual neurons need to underlie position and clutter "invariant" object recognition?

David D. Cox, J. DiCarlo, D. Cox, 2009, Journal of neurophysiology.

Transformation-Tolerant Object Recognition in Rats Revealed by Visual Priming

D. Zoccolan, S. Tafazoli, A. Di Filippo, 2012, The Journal of Neuroscience.

Accuracy of Rats in Discriminating Visual Objects Is Explained by the Complexity of Their Perceptual Strategy

Jakob H. Macke, Daniele Bertolini, Davide Zoccolan, 2018, Current Biology.

Neuron Perspective How Does the Brain Solve Visual Object Recognition ?

Nicole C. Rust, N. Rust, J. DiCarlo, 2022 .

Dynamics and reproducibility of a moderately complex sensory-motor response in the medicinal leech.

V. Torre, E. García-Pérez, D. Zoccolan, 2004, Journal of neurophysiology.

Sensory-Motor Response in the Medicinal Leech Dynamics and Reproducibility of a Moderately Complex

V. Torre, E. García-Pérez, D. Zoccolan, 2004 .

Highly Variable Spike Trains Underlie Reproducible Sensorimotor Responses in the Medicinal Leech

V. Torre, D. Zoccolan, G. Pinato, 2002, The Journal of Neuroscience.

Distributed motor pattern underlying whole-body shortening in the medicinal leech.

V. Torre, D. Zoccolan, I. Arisi, 2001, Journal of neurophysiology.

The use of optical flow to characterize muscle contraction

V. Torre, Andrea Giachetti, D. Zoccolan, 2001, Journal of Neuroscience Methods.

Using Optical Flow to Characterize Sensory-Motor Interactions in a Segment of the Medicinal Leech

V. Torre, D. Zoccolan, 2002, The Journal of Neuroscience.

Statistics of Decision Making in the Leech

V. Torre, H. Robinson, A. Mazzoni, 2005, The Journal of Neuroscience.

Invariant visual object recognition and shape processing in rats

D. Zoccolan, 2015, Behavioural Brain Research.

12 78 4 v 1 [ cs . L G ] 2 9 M ay 2 01 9 Intrinsic dimension of data representations in deep neural networks

J. Macke, A. Laio, D. Zoccolan, 2019 .

Object Recognition? Need to Underlie Position and Clutter ''Invariant'' What Response Properties Do Individual Neurons

David D. Cox, N. P. Bichot, Ethan M. Meyers, 2011 .

Nonlinear processing of shape information in rat lateral extrastriate cortex

Giulio Matteucci, Davide Zoccolan, Rosilari Bellacosa Marotti, 2018 .

Accuracy of Rats in Discri minating Visual Objects Is Explained by the Complexity of Their Perceptual Strategy Graphical

J. Macke, D. Zoccolan, V. Djurdjević, 2018 .

Editorial: What can simple brains teach us about how vision works

David D. Cox, Davide Zoccolan, Andrea Benucci, 2015, Front. Neural Circuits.

Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks

A. Laio, D. Zoccolan, S. Tafazoli, 2022, ArXiv.

The use of Videomicroscopy and Computer Vision to monitor muscle contraction

Andrea Giachetti, Davide Zoccolan, Vincent Torre, 2001 .

Unsupervised experience with temporal continuity of the visual environment is causally involved in the development of V1 complex cells.

D. Zoccolan, Giulio Matteucci, 2020, Science advances.

Information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition

R. Zecchina, Carlo Baldassi, A. Pagnani, 2012, BMC Neuroscience.

A general-purpose mechanism of visual feature association in visual word identification and beyond

D. Zoccolan, D. Crepaldi, Eva Viviani, 2021, Current Biology.

A general-purpose mechanism of visual feature association at the core of word learning

Davide Zoccolan, Eva Viviani, Davide Crepaldi, 2020, bioRxiv.

Characterization of Visual Object Representations in Rat Primary Visual Cortex pdf

Marcello Pelillo, Sebastiano Vascon, Eis Annavini, 2018, ECCV Workshops.

Davide Zoccolan, Alessio Ansuini, Alireza Alemi, 2015, Front. Neural Circuits.

A rodent model for the study of invariant visual object recognition

James J DiCarlo, David D. Cox, Davide Zoccolan, 2009, Proceedings of the National Academy of Sciences.

Emergence of transformation-tolerant representations of visual objects in rat lateral extrastriate cortex

Stefano Panzeri, Houman Safaai, Davide Zoccolan, 2017, eLife.

Temporal stability of stimulus representation increases along rodent visual cortical hierarchies

V. Balasubramanian, D. Zoccolan, H. P. Op de Beeck, 2021, Nature Communications.

Intrinsic dynamics enhance temporal stability of stimulus representation along a visual cortical hierarchy

Vijay Balasubramanian, Davide Zoccolan, Eugenio Piasini, 2019, bioRxiv.

A template-matching algorithm for laminar identification of cortical recording sites from evoked response potentials

Giulio Matteucci, Davide Zoccolan, Margherita Riggi, 2019, bioRxiv.

Supralinear and Supramodal Integration of Visual and Tactile Signals in Rats: Psychophysics and Neuronal Mechanisms

Mathew E. Diamond, Davide Zoccolan, M. Diamond, 2018, Neuron.

Interneurone bursts are spontaneously associated with muscle contractions only during early phases of mouse spinal network development: a study in organotypic cultures

L. Ballerini, D. Zoccolan, M. Rosato-Siri, 2004, The European journal of neuroscience.

Methodological Approaches to the Behavioural Investigation of Visual Perception in Rodents

Davide Zoccolan, Alessandro Di Filippo, D. Zoccolan, 2018 .

Editorial: Sensory Adaptation

C. Clifford, D. Zoccolan, M. Adibi, 2021, Frontiers in Systems Neuroscience.

Carlo Baldassi, Riccardo Zecchina, James J. DiCarlo, 2013, PLoS Comput. Biol..

Trade-Off between Object Selectivity and Tolerance in Monkey Inferotemporal Cortex

Tomaso Poggio, James J DiCarlo, Davide Zoccolan, 2007, The Journal of Neuroscience.

Intrinsic dimension of data representations in deep neural networks

J. Macke, A. Laio, D. Zoccolan, 2019, NeurIPS.

A passive, camera-based head-tracking system for real-time, three-dimensional estimation of head position and orientation in rodents.

Walter Vanzella, Natalia Grion, Daniele Bertolini, 2019, Journal of neurophysiology.

A machine learning framework to optimize optic nerve electrical stimulation for vision restoration

Silvestro Micera, Davide Zoccolan, Simone Romeni, 2021, Patterns.

Unsupervised experience with temporal continuity of the visual environment is causally involved in the development of V1 complex cells

Giulio Matteucci, Davide Zoccolan, D. Zoccolan, 2020, Science Advances.

Quantitative characterization and classification of leech behavior.

Alberto Mazzoni, Vincent Torre, Davide Zoccolan, 2005, Journal of neurophysiology.

Multiple Object Response Normalization in Monkey Inferotemporal Cortex

James J DiCarlo, David D. Cox, Davide Zoccolan, 2005, The Journal of Neuroscience.

A Self-Calibrating, Camera-Based Eye Tracker for the Recording of Rodent Eye Movements

Brett J. Graham, David D. Cox, Davide Zoccolan, 2010, Front. Neurosci..

3D meshes of carbon nanotubes guide functional reconnection of segregated spinal explants

M. Prato, L. Ballerini, D. Zoccolan, 2016, Science Advances.

Rats spontaneously perceive global motion direction of drifting plaids

Rosilari Bellacosa Marotti, D. Zoccolan, Giulio Matteucci, 2021, bioRxiv.

NeuronsInteractions in Macaque Inferior Temporal

G. Orban, R. Vogels, J. DiCarlo, 2015 .

Innovative Methodology

D. Zoccolan, Giulio Matteucci, Margherita Riggi, 2020, Journal of Neurophysiology.

A template-matching algorithm for laminar identification of cortical recording sites from evoked response potentials.

Giulio Matteucci, Davide Zoccolan, Margherita Riggi, 2020, Journal of neurophysiology.

Nonlinear Processing of Shape Information in Rat Lateral Extrastriate Cortex

Giulio Matteucci, Davide Zoccolan, Margherita Riggi, 2018, The Journal of Neuroscience.

Multifeatural Shape Processing in Rats Engaged in Invariant Visual Object Recognition

Davide Zoccolan, Alireza Alemi-Neissi, Federica Bianca Rosselli, 2013, The Journal of Neuroscience.

Truly dorsal: nonlinear integration of motion signals is required to account for the responses of pattern cells in rat visual cortex

Rosilari Bellacosa Marotti, D. Zoccolan, Giulio Matteucci, 2023, bioRxiv.

Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks

A. Laio, D. Zoccolan, S. Tafazoli, 2022, NeurIPS.

Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

V. Balasubramanian, D. Zoccolan, Eugenio Piasini, 2021, bioRxiv.

How Does the Brain Solve Visual Object Recognition?

Nicole C. Rust, J. DiCarlo, D. Zoccolan, 2012, Neuron.

Information theoretic and machine learning approaches to quantify non-linear visual feature interaction underlying visual object recognition

Carlo Baldassi, Riccardo Zecchina, Alfredo Braunstein, 2012, BMC Neuroscience.

Transformation-Tolerant Object Recognition in Rats Revealed by Visual Priming

D. Zoccolan, S. Tafazoli, A. Di Filippo, 2012, The Journal of Neuroscience.

Causal adaptation to visual input dynamics governs the development of complex cells in V1

Giulio Matteucci, Davide Zoccolan, D. Zoccolan, 2019, bioRxiv.

A passive, camera-based head-tracking system for real-time, 3D estimate of head position and orientation in rodents

Daniele Bertolini, Davide Zoccolan, Walter Vanzella, 2019 .

Unsupervised learning of mid-level visual representations

D. Zoccolan, Giulio Matteucci, Eugenio Piasini, 2023, Current Opinion in Neurobiology.

Unraveling the complexity of rat object vision requires a full convolutional network - and beyond

D. Zoccolan, P. Muratore, Alireza Alemi, 2024, bioRxiv.

Truly pattern: Nonlinear integration of motion signals is required to account for the responses of pattern cells in rat visual cortex

D. Zoccolan, Giulio Matteucci, B. Zattera, 2023, Science advances.