Predictive Coding in Auditory Cortical Neurons of Songbirds
Srihita Rudraraju, Brad Theilman, Michael Turvey, Timothy Gentner, University of California San Diego, United States
Contributed Talks 3 Lecture
Grand Ballroom A-C
Sun, 28 Aug, 10:00 - 10:20 Pacific Time (UTC -7)
The inferential basis of perception is thought to arise in the sensory cortex through prediction of future events that aid processing efficiency. Predictive coding (PC), a theoretical framework in which the brain compares a generative model to incoming sensory signals, seeks to explain this inferential process. There is little understanding, however, of how PC might be implemented at a mechanistic level in individual neurons within the auditory system. Here, we examined responses of single neurons in caudomedial nidopallium (NCM) and caudal mesopallium (CM), analogs of higher order auditory cortex, in anesthetized European starlings listening to conspecific songs. We trained a feedforward temporal prediction model (TPM) to define a “latent” predictive feature space and its corresponding feature space representing prediction error. We show that NCM spiking responses are best modeled by the predictive features of spectrotemporal song, while CM responses capture both predictive and error features. This provides strong support for the notion of a feature-based predictive auditory code implemented in single neurons in songbirds.