Aran Nayebi
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Explore the profile of Aran Nayebi including associated specialties, affiliations and a list of published articles.
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9
Citations
254
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Recent Articles
1.
Hermann K, Nayebi A, van Steenkiste S, Jones M
Behav Brain Sci
. 2023 Dec;
46:e394.
PMID: 38054325
Bowers et al. express skepticism about deep neural networks (DNNs) as models of human vision due to DNNs' failures to account for results from psychological research. We argue that to...
2.
Nayebi A, Kong N, Zhuang C, Gardner J, Norcia A, Yamins D
PLoS Comput Biol
. 2023 Oct;
19(10):e1011506.
PMID: 37782673
Studies of the mouse visual system have revealed a variety of visual brain areas that are thought to support a multitude of behavioral capacities, ranging from stimulus-reward associations, to goal-directed...
3.
Maheswaranathan N, McIntosh L, Tanaka H, Grant S, Kastner D, Melander J, et al.
Neuron
. 2023 Jul;
111(17):2742-2755.e4.
PMID: 37451264
Understanding the circuit mechanisms of the visual code for natural scenes is a central goal of sensory neuroscience. We show that a three-layer network model predicts retinal natural scene responses...
4.
Nayebi A, Rajalingham R, Jazayeri M, Yang G
ArXiv
. 2023 Jun;
PMID: 37292459
Humans and animals have a rich and flexible understanding of the physical world, which enables them to infer the underlying dynamical trajectories of objects and events, plausible future states, and...
5.
Nayebi A, Sagastuy-Brena J, Bear D, Kar K, Kubilius J, Ganguli S, et al.
Neural Comput
. 2022 Jul;
34(8):1652-1675.
PMID: 35798321
The computational role of the abundant feedback connections in the ventral visual stream is unclear, enabling humans and nonhuman primates to effortlessly recognize objects across a multitude of viewing conditions....
6.
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
Tanaka H, Nayebi A, Maheswaranathan N, McIntosh L, Baccus S, Ganguli S
Adv Neural Inf Process Syst
. 2022 Mar;
32:8537-8547.
PMID: 35283616
Recently, deep feedforward neural networks have achieved considerable success in modeling biological sensory processing, in terms of reproducing the input-output map of sensory neurons. However, such models raise profound questions...
7.
Melander J, Nayebi A, Jongbloets B, Fortin D, Qin M, Ganguli S, et al.
Cell Rep
. 2021 Nov;
37(6):109972.
PMID: 34758304
Cortical function relies on the balanced activation of excitatory and inhibitory neurons. However, little is known about the organization and dynamics of shaft excitatory synapses onto cortical inhibitory interneurons. Here,...
8.
Zhuang C, Yan S, Nayebi A, Schrimpf M, Frank M, DiCarlo J, et al.
Proc Natl Acad Sci U S A
. 2021 Jan;
118(3).
PMID: 33431673
Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model...
9.
McIntosh L, Maheswaranathan N, Nayebi A, Ganguli S, Baccus S
Adv Neural Inf Process Syst
. 2017 Jul;
29:1369-1377.
PMID: 28729779
A central challenge in sensory neuroscience is to understand neural computations and circuit mechanisms that underlie the encoding of ethologically relevant, natural stimuli. In multilayered neural circuits, nonlinear processes such...