Nonlinear decoding of natural images from large-scale primate retinal ganglion recordings

• Main Authors: Batty, E. (Author), Brackbill, N. (Author), Chichilnisky, E.J (Author), Kim, Y.J (Author), Lee, J. (Author), Mitelut, C. (Author), Paninski, L. (Author), Tong, W. (Author)
• Format: Article
• Language: English
• Published: MIT Press Journals 2021
• Subjects: Aldehydes; animal; Animals; Brain; brain computer interface; Brain computer interface; Brain machine interface; Brain-Computer Interfaces; Decoding; Large population; Macaca; Neural Networks, Computer; Neurons; Nonlinear computations; Non-linear methods; Nonlinear techniques; Ophthalmology; Physical environments; retina; Retina; retina ganglion cell; Retinal ganglion cells; Retinal Ganglion Cells; Spatial features
• Online Access: View Fulltext in Publisher

 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. © 2021 Massachusetts Institute of Technology.