Back

Targeted cortical stimulation reveals principles of cortical contextual interactions

Wang, S.; Palmigiano, A.; Miller, K. D.; Van Hooser, S. D.

2022-06-24 neuroscience
10.1101/2022.06.22.497254 bioRxiv
Show abstract

Cross-orientation suppression is a classic form of contextual normalization in visual cortex, yet the degree to which cortical circuits participate in the normalization computation is unclear. We visualized orientation maps of individual ferrets, and provided patterned optogenetic stimulation to both excitatory and inhibitory cells in orientation columns that either matched or were orthogonal to the preferred visual orientation of neurons recorded with electrodes. When visual or optogenetic stimulation of columns preferring one orientation was combined with optogenetic stimulation of columns preferring the orthogonal orientation, we observed less suppression than when orthogonal stimulation was provided visually, suggesting that cortical circuits do not provide a large fraction of visual cross-orientation suppression. Integration of visual and optogenetic signals was linear when neurons exhibited low firing rates and became sublinear when neurons exhibited higher firing rates. We probed the nature of sublinearities in cortex by examining the influence of optogenetic stimulation of cortical interneurons. We observed a range of responses, including evidence for paradoxical responses in which interneuron stimulation caused a decrease in inhibitory firing rate, presumably due to the withdrawal of recurrent excitation. These results are compatible with cortical circuits that exhibit strong recurrent excitation with stabilizing inhibition that provides normalization, albeit normalization that is too weak across columns to account for cross-orientation suppression.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.