Back

Increments in Visual Motion Coherence are More Readily Detected than Decrements

Wei, L.; Mitchell, A. O.; Maunsell, J. H. R.

2023-01-26 neuroscience
10.1101/2023.01.25.525590 bioRxiv
Show abstract

Understanding the circuits that access and read out information in the cerebral cortex to guide behavior remains a challenge for systems-level neuroscience. Recent optogenetic experiments targeting specific cell classes in mouse primary visual cortex (V1) have shown that mice are sensitive to optically-induced increases in V1 spiking, but are relatively insensitive to decreases in neuronal spiking of similar magnitude and time course. This asymmetry suggests that the readout of signals from cortex depends preferentially on increases in spike rate. We investigated whether humans display a similar asymmetry by measuring thresholds for detecting changes in the motion coherence of dynamic random dot stimuli. The middle temporal visual area (MT) has been shown to play an important role in discriminating random dot stimuli, and the responses of its individual neurons to dynamic random dots are well characterized. While both increments and decrements in motion coherence have heterogeneous effects on MT responses, increments cause on average more increases in firing rates. Consistent with this, we found that subjects are far more sensitive to increments of random dot motion coherence than to decrements of coherence. The magnitude of the difference in detectability was largely consistent with the expected difference in effectiveness of coherence increments and decrements in producing increases in MT spike rates. The results add strength to the notion that the circuit mechanisms that read out cortical signals are relatively insensitive to decrements in cortical spiking.

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.