Search bioRxiv⌕ Search

Biology subjects

Bassetto, G.

Publications and source records attributed to Bassetto, G..

2 recordsLinked to original sources

Freely-moving mice visually pursue prey using a retinal area with least optic flow

Mice have a large visual field that is constantly stabilized by vestibular ocular reflex driven eye rotations that counter head-rotations. While maintaining their extensive visual coverage is advantageous for predator detection, mice also track and capture prey using vision. However, in the freely moving animal quantifying object location in the field of view is challenging. Here, we developed a method to digitally reconstruct and quantify the visual scene of freely moving mice performing a visually based prey capture task. By isolating the visual sense and combining amouse eye optic model with the head and eye rotations, the detailed reconstruction of the digital environment and retinal features were projected onto the corneal surface for comparison, and updated throughout the behavior. By quantifying the spatial location of objects in the visual scene and their motion throughout the behavior, we show that the image of the prey is maintained within a small area, the functional focus, in the upper-temporal part of the retina. This functional focus coincides with a region of minimal optic flow in the visual field and consequently minimal motion-induced image blur during pursuit, as well as the reported high density-region of Alpha-ON sustained retinal ganglion cells.

neuroscience↗

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.

neuroscience↗