Abstract
(Neuron 110, 1240–1257.e1–e8; April 6, 2022)Figure S5A feedforward neural network for object recognition explains firing rates relatively well, but poorly accounts for γ-synchronization. Relates to Figure 5 of main text (original)View Large Image Figure ViewerDownload Hi-res image Download (PPT) (Neuron 110, 1240–1257.e1–e8; April 6, 2022) Predictive coding of natural images by V1 firing rates and rhythmic synchronizationUran et al.NeuronFebruary 3, 2022In BriefUran, Peter et al. use self-supervised neural networks to quantify stimulus predictability in natural images to investigate the context-dependence of V1 signals. Firing rates decrease with the predictability, specifically of high-level image features. By contrast, γ-synchronization increases with the predictability of low-level features and emerges for low-dimensional, strongly compressible images. Full-Text PDF Open Access
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
