Predictive Coding in the Auditory Cortex
Characterization of response properties of neurons in higher-level sensory areas is not well defined. Here we show that firing rates of neurons in a secondary sensory forebrain area of songbirds can be modeled by different representations of birdsong. In this work, we modeled neurons in the caudo-medial nidopallium (NCM) of adult European starlings with three different representations of the natural birdsong called signal, prediction, and error. Prediction spectrogram was computed by training the data as a Gaussian distribution on a loss function given by the negative log likelihood, and then estimating the means and variances of the signal. Using our Maximum Noise Entropy (MNE) model, responses were predicted by the logistic function, the parameters of which are obtained from the MNE model. Predictions of neural responses were computed by using both a full MNE model, and then by only considering the linear parameters of the model. The neural responses to natural stimuli obtained using prediction and error MNEs were close to the actual response in the NCM. The concept of stimulus representations obtained from predictive coding models may be useful for modeling neural responses in higher-order sensory areas whose functions have been poorly understood.