Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings.
Neural Comput
; 33(7): 1719-1750, 2021 06 11.
Article
de En
| MEDLINE
| ID: mdl-34411268
ABSTRACT
Decoding sensory stimuli from neural activity can provide insight into how the nervous system might interpret the physical environment, and facilitates the development of brain-machine interfaces. Nevertheless, the neural decoding problem remains a significant open challenge. Here, we present an efficient nonlinear decoding approach for inferring natural scene stimuli from the spiking activities of retinal ganglion cells (RGCs). Our approach uses neural networks to improve on existing decoders in both accuracy and scalability. Trained and validated on real retinal spike data from more than 1000 simultaneously recorded macaque RGC units, the decoder demonstrates the necessity of nonlinear computations for accurate decoding of the fine structures of visual stimuli. Specifically, high-pass spatial features of natural images can only be decoded using nonlinear techniques, while low-pass features can be extracted equally well by linear and nonlinear methods. Together, these results advance the state of the art in decoding natural stimuli from large populations of neurons.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Cellules ganglionnaires rétiniennes
/
Interfaces cerveau-ordinateur
Limites:
Animals
Langue:
En
Journal:
Neural Comput
Sujet du journal:
INFORMATICA MEDICA
Année:
2021
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique