ePoster · Neuroscience
Convolutional neural networks describe encoding subspaces of local circuits in auditory cortex
Stephen David, Samuel Norman-Haignere, Jereme Wingert, Satyabrata Parida · COSYNE 2025
Convolutional neural networks (CNNs) provide a generalizable architecture for encoding models that predict neural activity evoked by natural sounds with much greater accuracy than other established models. However, the complexity of CNNs makes it difficult to discern the computational properties that support their improved predictive power. We used a combination of local linear approximation and dimensionality reduction to measure the tuning subspace captured by a CNN. Single-unit data was recorded using microelectrode arrays from primary auditory cortex (A1) of awake, passively listening ferr