Claudi, Federico Claudio; (2022) Neural and behavioral bases of innate behaviors. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Recently, ethological studies of animal behavior uncovered its complexity while neuroscientific work began unraveling the neural bases of behavior. Improvements in algorithmic understanding of behavior and neural function contributed to re- cent breakthroughs in robotics and artificial intelligence systems. Yet, animals’ decision-making and motor-control are unequalled by human engineered systems and the continued investigation of the behavioral and neural bases of these abilities is crucial for understanding brain function and inform further technological devel- opments. In my PhD work, I first investigate escape path selection in mice presented with threat, demonstrating how mice combined rapidly acquired spatial knowledge with an innate choice heuristic to inform decision-making. This strategy minimizes the requirement for trial-and-error learning and yields accurate decision-making by combining knowledge acquired at an evolutionarily time-scale with that acquired by the individual. Future work aimed at understanding how these sources of in- formation are combined in the brain to inform decision-making may lead to more efficient artificial learning agents. Next, I studied goal-directed locomotion behav- ior in which mice move rapidly through an environment to reach a goal location. Successful goal-directed locomotion behavior requires substantial navigation and motor control skills and, additionally, sophisticated planning and control of move- ments while moving at high speed. Detailed behavioral quantification and compar- ison to a control-theoretic model demonstrated that mice do possess such planning skills, allowing them to execute rapid and efficient trajectories to a goal. Population- level extracellular recordings of neural activity during goal directed locomotion was also used to begin uncovering the neural bases of planning during locomotion. Altogether, my work combined accurate quantification of animal movements with the- oretical models of optimal behavior to understand behavior at a computation level, aiming to provide crucial information to inform future studies on the neural bases of innate behaviors and aid in the development of novel artificial learning systems

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