The use of computational modeling to link sensory processing with behavior in Drosophila

Abstract

A major goal of systems neuroscience is to understand how the brain represents information, and how those representations are used to drive behavior. Even though genetic model organisms like Drosophila grant unprecedented experimental access to the nervous system for manipulating and recording neural activity, the complexity of natural stimuli and natural behaviors still poses a challenge for solving the connections between neural activity and behavior. Here, we advocate for the use of computational modeling to complement (and enhance) the Drosophila toolkit. We first lay out a modelling framework for making sense of the relation between natural sensory stimuli, neuronal responses, and natural behavior. We then highlight how this framework can be used to reveal how neural circuits drive behavior, using selected case studies.PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.2720v1 | CC BY 4.0 Open Access |

Quantifying behavior to solve sensorimotor transformations: advances from worms and flies

“…Instead, newer computational techniques are needed to estimate relevant sensory features in spite of these correlations, by finding those most linked to the output[38–40]. These methods, in general, fit a linear filter that processes an input before being passed through a nonlinear function [41]. The linear filter (Figure 3, column iii) describes which temporal features of the inputs drive the output.…”