Dynamical Processing of Orientation Precision in the Primary Visual Cortex

Ladret, Hugo; Cortes, Nelson; Ikan, Lamyae; Chavane, Frédéric; Casanova, Christian; Perrinet, Laurent

doi:10.2139/ssrn.4076336

Cited by 1 publication

(1 citation statement)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Across the visual cortex, various specific forms of recurrence have been studied and linked to perceptual phenomena. For instance: uncertainty computation ([Ladret et al, 2023]) and pattern completion ([Shin et al, 2023]) by lateral connections within V1, edge detection in cluttered images through lateral processing in V1 ([Self et al, 2013]), prediction about occluded shapes by feedback from the prefrontal cortex (PFC) to V4 ([Choi et al, 2018]), figure-ground modulation by feedback from V4 to V1 ([Klink et al, 2017, Angelucci et al, 2017]), and interactions with spatial frequency transformations by feedback from frontal areas ([Bar et al, 2006, Goddard et al, 2016]). Overall, the literature points to a wide range of roles played by recurrent processing to resolve a variety of visual challenges, with each recurrent cortical connection potentially operating a different computation on incoming sensory information.…”

Section: Introductionmentioning

confidence: 99%

Recurrent issues with deep neural network models of visual recognition

Maniquet,

de Beeck,

Costantino

2024

Preprint

View full text Add to dashboard Cite

Object recognition requires flexible and robust information processing, especially in view of the challenges posed by naturalistic visual settings. The ventral stream in visual cortex is provided with this robustness by its recurrent connectivity. Recurrent deep neural networks (DNNs) have recently emerged as promising models of the ventral stream. In this study, we asked whether DNNs could be used to explore the role of different recurrent computations during challenging visual recognition. We assembled a stimulus set that included manipulations that are often associated with recurrent processing in the literature, like occlusion, partial viewing, clutter, and spatial phase scrambling. We obtained a benchmark dataset from human participants performing a categorisation task on this stimulus set. By applying a wide range of model architectures to the same task, we uncovered a nuanced relationship between recurrence, model size, and performance. While recurrent models reach higher performance than their feedforward counterpart, we could not dissociate this improvement from that obtained by increasing model size. We found consistency between humans and models patterns of difficulty across the visual manipulations, but this was not modulated in an obvious way by the specific type of recurrence or size added to the model. Finally, depth/size rather than recurrence makes model confusion patterns more human-like. Contrary to previous assumptions, our findings challenge the notion that recurrent models are better models of human recognition behaviour than feedforward models, and emphasise the complexity of incorporating recurrence into computational models.

show abstract