Developing new tools and methodological approaches (whether at the hardware or algorithmic level) is often a critical step to advance scientific knowledge. Below, we report a list of contributions to the development of new methods in the field of behavioral neurophysiology, including innovative eye-tracking and head-tracking systems for small mammals (Zoccolan et al., 2010; Vanzella et al., 2019) and machine learning approaches for laminar identification of cortical recording sites (Matteucci et al., 2020) and development of visual prostheses (Romeni et al., 2021).

Finally, earlier studies of the PI and his collaborators have applied computer vision approaches and signal processing tools for the quantitive analysis of leech motor behavior (Zoccolan et al, 2001; Mazzoni et al, 2005). 

Selected articles
A machine learning framework to optimize optic nerve electrical stimulation for vision restoration
Romeni S, Zoccolan D & Micera S (2021)
Patterns: 2(7), 100286
A template-matching algorithm for laminar identification of cortical recording sites from evoked response potentials.
Matteucci G*, Riggi M* & Zoccolan D (2020)
J. Neurophys.: 124, 102-114
A passive, camera-based head-tracking system for real-time, three-dimensional estimation of head position and orientation in rodents.
Vanzella W*, Grion N*, Bertolini D*, Perissinotto A, Gigante M & Zoccolan D (2019)
J. Neurophys. : 122, 2220-2242
A self-calibrating, camera-based eye tracker for the recording of rodent eye movement.
Zoccolan D, Graham JB & Cox DD (2010)
Front. Neurosci. : 4:193
Quantitative characterization and classification of leech behavior
Mazzoni A, Garcia-Perez E, Zoccolan D, Graziosi S, Torre V (2005)
J. Neurophysiol.: 93:580-93
The use of optical flow to characterize muscle contraction
Zoccolan D, Giachetti A, Torre V (2001)
J. Neurosci. Methods: 110, 65-80