  Over the last decade, numerous studies have shown that deep neural networks
exhibit sensory representations similar to those of the mammalian brain, in
that their activations linearly map onto cortical responses to the same sensory
inputs. However, it remains unknown whether these artificial networks also
learn like the brain. To address this issue, we analyze the brain responses of
two ferret auditory cortices recorded with functional UltraSound imaging (fUS),
while the animals were presented with 320 10\,s sounds. We compare these brain
responses to the activations of Wav2vec 2.0, a self-supervised neural network
pretrained with 960\,h of speech, and input with the same 320 sounds.
Critically, we evaluate Wav2vec 2.0 under two distinct modes: (i) "Pretrained",
where the same model is used for all sounds, and (ii) "Continuous Update",
where the weights of the pretrained model are modified with back-propagation
after every sound, presented in the same order as the ferrets. Our results show
that the Continuous-Update mode leads Wav2Vec 2.0 to generate activations that
are more similar to the brain than a Pretrained Wav2Vec 2.0 or than other
control models using different training modes. These results suggest that the
trial-by-trial modifications of self-supervised algorithms induced by
back-propagation aligns with the corresponding fluctuations of cortical
responses to sounds. Our finding thus provides empirical evidence of a common
learning mechanism between self-supervised models and the mammalian cortex
during sound processing.
