A Critical Test of Deep Convolutional Neural Networks' Ability to Capture Recurrent Processing in the Brain Using Visual Masking.
J Cogn Neurosci
; 34(12): 2390-2405, 2022 11 01.
Article
em En
| MEDLINE
| ID: mdl-36122352
ABSTRACT
Recurrent processing is a crucial feature in human visual processing supporting perceptual grouping, figure-ground segmentation, and recognition under challenging conditions. There is a clear need to incorporate recurrent processing in deep convolutional neural networks, but the computations underlying recurrent processing remain unclear. In this article, we tested a form of recurrence in deep residual networks (ResNets) to capture recurrent processing signals in the human brain. Although ResNets are feedforward networks, they approximate an excitatory additive form of recurrence. Essentially, this form of recurrence consists of repeating excitatory activations in response to a static stimulus. Here, we used ResNets of varying depths (reflecting varying levels of recurrent processing) to explain EEG activity within a visual masking paradigm. Sixty-two humans and 50 artificial agents (10 ResNet models of depths -4, 6, 10, 18, and 34) completed an object categorization task. We show that deeper networks explained more variance in brain activity compared with shallower networks. Furthermore, all ResNets captured differences in brain activity between unmasked and masked trials, with differences starting at â¼98 msec (from stimulus onset). These early differences indicated that EEG activity reflected "pure" feedforward signals only briefly (up to â¼98 msec). After â¼98 msec, deeper networks showed a significant increase in explained variance, which peaks at â¼200 msec, but only within unmasked trials, not masked trials. In summary, we provided clear evidence that excitatory additive recurrent processing in ResNets captures some of the recurrent processing in humans.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Percepção Visual
/
Redes Neurais de Computação
Limite:
Humans
Idioma:
En
Revista:
J Cogn Neurosci
Assunto da revista:
NEUROLOGIA
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Holanda