ArticleJournal of vision2020
Selectivity and robustness of sparse coding networks.
Article in Journal of vision, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Neural Model for V1 That Incorporates Dendritic Nonlinearities and Backpropagating Action Potentials.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Beyond ℓ1 sparse coding in V1.PLoS computational biology · 2023Article
- Subtle adversarial image manipulations influence both human and machine perception.Nature communications · 2023Article
- Efficient coding of natural scenes improves neural system identification.PLoS computational biology · 2023Article
- Information-theoretic analysis of Hierarchical Temporal Memory-Spatial Pooler algorithm with a new upper bound for the standard information bottleneck method.Frontiers in computational neuroscience · 2023Article
- FP-nets as novel deep networks inspired by vision.Journal of vision · 2022Article
- Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments.Frontiers in neurorobotics · 2022Article
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Authors and funding
6 authors.
Funding
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Abstract
We investigate how the population nonlinearities resulting from lateral inhibition and thresholding in sparse coding networks influence neural response selectivity and robustness. We show that when compared to pointwise nonlinear models, such population nonlinearities improve the selectivity to a preferred stimulus and protect against adversarial perturbations of the input. These findings are predicted from the geometry of the single-neuron iso-response surface, which provides new insight into the relationship between selectivity and adversarial robustness. Inhibitory lateral connections curve the iso-response surface outward in the direction of selectivity. Since adversarial perturbations are orthogonal to the iso-response surface, adversarial attacks tend to be aligned with directions of selectivity. Consequently, the network is less easily fooled by perceptually irrelevant perturbations to the input. Together, these findings point to benefits of integrating computational principles found in biological vision systems into artificial neural networks.
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Registered trials
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