Matches in SemOpenAlex for { <https://semopenalex.org/work/W2974313581> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W2974313581 endingPage "209a" @default.
- W2974313581 startingPage "209a" @default.
- W2974313581 abstract "Deep convolutional neural networks (DCNN) constitute a promising initial approximation to the cascade of computations along the ventral visual stream that support visual recognition. DCNNs predict object-evoked neural activity in inferior temporal (IT) cortex (Yamins et al., 2014), and the mapping between neural and DCNN activity generalizes across object categories (Horikawa and Kamitani, 2017; Yamins et al., 2014). Generalization is critical to build models of the ventral visual stream that capture the generic neural code for shape and make accurate predictions about novel categories beyond the training sample (zero-shot decoding). However, the degree to which mappings between DCNN and IT activity generalize across object categories has not been explicitly tested. To address this, we built zero-shot neural decoders for object category from multi-electrode array recordings in rhesus macaque IT, obtained while viewing images of rendered objects on arbitrary natural scene backgrounds (Majaj et al., 2015). Our zero-shot decoders generalized to predict novel categories despite not being trained on neural activity from the test categories. DCNN activity was computed for each image using VGG-16 (Simonyan and Zisserman, 2015), and DCNN activity was reconstructed from IT activity using linear regression. Linear classifiers were trained to predict object category from DCNN activity, then these classifiers were used to predict object category from DCNN activity reconstructed from IT responses. We held out neural activity from two test categories when learning the IT to DCNN mappings and found robust zero-shot decoding accuracies, indicating that the mappings generalize across categories. Intriguingly, training on a single category alone was sufficient to permit zero-shot decoding of novel categories. We show the relationship between IT and DCNN activity is stable across object categories, demonstrating the feasibility of zero-shot neural decoding systems based on electrophysiological recordings." @default.
- W2974313581 created "2019-09-26" @default.
- W2974313581 creator A5061849378 @default.
- W2974313581 creator A5081541767 @default.
- W2974313581 creator A5083103426 @default.
- W2974313581 date "2019-09-06" @default.
- W2974313581 modified "2023-10-16" @default.
- W2974313581 title "Zero-shot neural decoding from rhesus macaque inferior temporal cortex using deep convolutional neural networks" @default.
- W2974313581 doi "https://doi.org/10.1167/19.10.209a" @default.
- W2974313581 hasPublicationYear "2019" @default.
- W2974313581 type Work @default.
- W2974313581 sameAs 2974313581 @default.
- W2974313581 citedByCount "1" @default.
- W2974313581 countsByYear W29743135812023 @default.
- W2974313581 crossrefType "journal-article" @default.
- W2974313581 hasAuthorship W2974313581A5061849378 @default.
- W2974313581 hasAuthorship W2974313581A5081541767 @default.
- W2974313581 hasAuthorship W2974313581A5083103426 @default.
- W2974313581 hasBestOaLocation W29743135811 @default.
- W2974313581 hasConcept C11413529 @default.
- W2974313581 hasConcept C134306372 @default.
- W2974313581 hasConcept C153180895 @default.
- W2974313581 hasConcept C154945302 @default.
- W2974313581 hasConcept C15744967 @default.
- W2974313581 hasConcept C169760540 @default.
- W2974313581 hasConcept C177148314 @default.
- W2974313581 hasConcept C2778950215 @default.
- W2974313581 hasConcept C2779345533 @default.
- W2974313581 hasConcept C2781238097 @default.
- W2974313581 hasConcept C33923547 @default.
- W2974313581 hasConcept C41008148 @default.
- W2974313581 hasConcept C57273362 @default.
- W2974313581 hasConcept C64876066 @default.
- W2974313581 hasConcept C81363708 @default.
- W2974313581 hasConceptScore W2974313581C11413529 @default.
- W2974313581 hasConceptScore W2974313581C134306372 @default.
- W2974313581 hasConceptScore W2974313581C153180895 @default.
- W2974313581 hasConceptScore W2974313581C154945302 @default.
- W2974313581 hasConceptScore W2974313581C15744967 @default.
- W2974313581 hasConceptScore W2974313581C169760540 @default.
- W2974313581 hasConceptScore W2974313581C177148314 @default.
- W2974313581 hasConceptScore W2974313581C2778950215 @default.
- W2974313581 hasConceptScore W2974313581C2779345533 @default.
- W2974313581 hasConceptScore W2974313581C2781238097 @default.
- W2974313581 hasConceptScore W2974313581C33923547 @default.
- W2974313581 hasConceptScore W2974313581C41008148 @default.
- W2974313581 hasConceptScore W2974313581C57273362 @default.
- W2974313581 hasConceptScore W2974313581C64876066 @default.
- W2974313581 hasConceptScore W2974313581C81363708 @default.
- W2974313581 hasIssue "10" @default.
- W2974313581 hasLocation W29743135811 @default.
- W2974313581 hasOpenAccess W2974313581 @default.
- W2974313581 hasPrimaryLocation W29743135811 @default.
- W2974313581 hasRelatedWork W2134444175 @default.
- W2974313581 hasRelatedWork W2200925278 @default.
- W2974313581 hasRelatedWork W2607976391 @default.
- W2974313581 hasRelatedWork W2767651786 @default.
- W2974313581 hasRelatedWork W2801801420 @default.
- W2974313581 hasRelatedWork W2892559168 @default.
- W2974313581 hasRelatedWork W2912288872 @default.
- W2974313581 hasRelatedWork W3195820617 @default.
- W2974313581 hasRelatedWork W4320197069 @default.
- W2974313581 hasRelatedWork W564581980 @default.
- W2974313581 hasVolume "19" @default.
- W2974313581 isParatext "false" @default.
- W2974313581 isRetracted "false" @default.
- W2974313581 magId "2974313581" @default.
- W2974313581 workType "article" @default.