Computational neuroethological framework for studying the neurobiology of social behavior

Computational neuroethology is an interdisciplinary approach to studying the brain. Ethology provides a framework for investigating the functional, mechanistic, developmental and evolutionary origins of stimulus-elicited natural behaviors. Neuroethology searches for the behaviors’ neural basis. Computational neuroscience enhances this search with quantitative methods for analyzing and modeling complex data sets. Together, computational neuroethology explores neural circuitry and coding by using biologically-motivated experiments to reveal correspondences between natural stimulus features, neural activity and behavior. The approach exploits variation in behavior to constrain the neural correlates mediating different stages of processing from encoding through perception and decision. Such correlates reflect putative features of the brain’s activity that could causally drive behavioral responses. Pursuing this strategy in rodents – where modern neuroscience tools can be used to test a mechanism’s necessity or sufficiency – gives us the potential to verify the causal roles for these endogenous correlates.