Topic: encoding models

The cost of experimental neural recordings strongly limits the stimuli or conditions one can present. Ideally, we would therefore like to know how neural responses observed in specific experiments generalize beyond them. E.g., how does the brain’s response to isolated sounds presented in the lab compare to its response to those sounds within natural language in complex social settings? We addressed this problem in fly courtship–a complex, acoustically mediated social behavior. Starting with recorded responses from diverse female neurons to isolated elements of male courtship song, we found the

A central goal of sensory neuroscience is to build parsimonious computational models that can both predict neural responses to natural stimuli and reveal interpretable functional organization in the brain. Statistical “component” models can learn interpretable, low-dimensional structure across different brain regions and subjects, but lack an explicit “encoding model” that links these components to the stimuli that drive them, and thus cannot generate predictions for new stimuli or generalize across different experiments. The predictive power of sensory encoding models has improved substantial

Hippocampal studies provide a rich window into the mechanisms of memory via the paradigm of spatial navigation. To interpret neural responses, it is common practice to first build encoding models using position during locomotion as the label, and then decode the position using this model. Of particular interest is the mismatch between the decoded and the actual position, for instance, replay of non-local trajectories during rest. However, it is unclear whether a priori chosen labels explain neural data the best. By imposing spatial structure to times of immobility, we may be systematically mis