The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Neural population control via deep image synthesis
Open accessPublished version of a preprint
Abstract
Predicting behavior of visual neurons: To what extent are predictive deep learning models of neural responses useful for generating experimental hypotheses? Bashivan et al. took an artificial neural network built to model the behavior of the target visual system and used it to construct images predicted to either broadly activate large populations of neurons or selectively activate one population while keeping the others unchanged. They then analyzed the effectiveness of these images in producing the desired effects in the macaque visual cortex. The manipulations showed very strong effects and achieved considerable and highly selective influence over the neuronal populations. Using novel and non-naturalistic images, the neural network was shown to reproduce the overall behavior of the animals' neural responses. Science , this issue p. eaav9436
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Cited by
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The neural architecture of language: Integrative modeling converges on predictive processing
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Deep Neural Networks as Scientific Models
Cichy, R. M., & Kaiser, D. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.01.009 · Cited by 454
Metadata
- Authors
- Bashivan, P., Kar, K., & DiCarlo, J. J.
- Venue
- Science
- Published
- 2019-05-03
- Volume
- 364
- Issue
- 6439
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2898929289
- Cited by
- 434
- References
- 32
- FWCI
- 27.99
- Type
- journal article
- Language
- en
- Open access
- green
- Funders
- Office of Naval Research (MURI-114407)
- National Eye Institute (R01-EY014970)
- Intelligence Advanced Research Projects Activity
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