Back

Regulation of expression site and generalizability of experience-dependent plasticity in visual cortex

Lantz, C. L.; Murase, S.; Quinlan, E. M.

2019-09-17 neuroscience
10.1101/773275 bioRxiv
Show abstract

The experience-dependent decrease in stimulus detection thresholds that underly perceptual learning can be induced by repetitive exposure to a visual stimulus. Robust stimulus-selective potentiation of visual responses is induced in the primary mouse visual cortex by repetitive low frequency visual stimulation (LFVS). How the parameters of the repetitive visual stimulus impact the site and specificity of this experience-dependent plasticity is currently a subject of debate. Here we demonstrate that the stimulus selective response potentiation induced by repetitive low frequency (1 Hz) stimulation, which is typically limited to layer 4, shifts to superficial layers following manipulations that enhance plasticity in primary visual cortex. In contrast, repetitive high frequency (10 Hz) visual stimulation induces response potentiation that is expressed in layers 4 and 5/6, and generalizes to novel visual stimuli. Repetitive visual stimulation also induces changes in the magnitude and distribution of oscillatory activity in primary visual cortex, however changes in oscillatory power do not predict the locus or specificity of response potentiation. Instead we find that robust response potentiation is induced by visual stimulation that resets the phase of ongoing gamma oscillations. Furthermore, high frequency, but not low frequency, repetitive visual stimulation entrains oscillatory rhythms with enhanced sensitivity to phase reset, such that familiar and novel visual stimuli induce similar visual response potentiation.

Matching journals

The top 2 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.