Extended Data Fig. 2: CNN layer depth comparison. | Nature Machine Intelligence

Extended Data Fig. 2: CNN layer depth comparison.

From: A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications

Extended Data Fig. 2

The first column details the CoNNear architecture. The next columns describe the total number of required model parameters, the required training time per epoch of 2310 TIMIT training sentences and average L1 loss across all windows of the TIMIT training set. Average L1 losses were also computed for BM displacement predictions to a number of unseen acoustic stimuli (click and 1-kHz pure tones) with levels between 0 and 90 dB SPL. Lastly, average L1 loss was also computed for the 550 sentences of the TIMIT test set. For each evaluated category, the best performing architecture is highlighted in bold font.

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