Caixa comportamental aprimorada para análise de tarefas de discriminação tátil em roedores

Abstract

Among the research works carried out with rodents in the area of Neuroscience, behavioral studies stand out. In order to carry out these studies, closed and controlled platforms are generally used in which the rodent is inserted, and tasks are elaborated, depending on the study, which the rodent must carry out. To study the learning of behaviors from tactile discrimination, for example, tasks are usually accompanied by a reward, normally following the reinforcement learning model. These platforms are commonly known as behavioral boxes. In this thesis, we aim to improve the methodologies and techniques used in this research area, developing new technologies for this type of study, including the automation of some procedures with the behavioral box aimed at tactile discrimination tasks. For that, we started with some initial tests of a behavioral box, using a computational model of physical simulation, from which we designed a new structure for the platform, with best-elaborated spaces and materials, and using only components that can be bought in the market or easily developed. Our demonstrated hypothesis is that this new structure for the behavioral box should improve the study of models related to the sensorimotor system in a more refined way. For example, the designed platform enables more precise control of the opening of the discrimination bars, which is currently not done satisfactorily. The final model also allows for the design of more complex decisionmaking experiments using the camera and sensor system, allowing a better evaluation of rodent performance. This includes improvements in determining the number of correct answers in the tasks performed in the studies in question. Therefore, as a main practical contribution, we believe that the present study provides that laboratories that work with this type of research can enjoy this new low-cost tool that is easy to develop and use. All developed materials and documents are made available on a multi-user collaborative platform.