Existing function in primary visual cortex is not perturbed by new skill acquisition of a non-matched sensory task

Abstract

The ability to learn and practice new skills applies to the computation of abstract properties such as geometric form and neuroprosthetic control, as well as physical actions. We trained adult mice to successfully use an optical brain-computer interface (BCI) device so that we could assess the extent to which abstract skill learning disrupts previously acquired cortical function. We found that animals used multiple neural strategies to gain volitional control of the BCI device, and continued to do so after additional training. Despite significant engagement of local neural activity during BCI performance, tuning properties and stimulus encoding were not perturbed by new skill acquisition. These results indicate that flexible selection of neural strategies during goal-directed practice may facilitate the integration of new skills with existing function.

Sensory constraints on volitional modulation of the motor cortex

“…This would be consistent with a model of cortex in which sensory and volitional signals impinge on the same neurons, resulting in a mixing of sensory and volitional signals at the level of individual cells. If this holds true for other cortical areas, this framework would be consistent with the observation of volitional modulation across diverse areas of the brain, including primary sensory areas 13,53–60 , as well as explaining sensory-driven activity in motor areas. If both volitional and sensory signals coexist across the entire cortex, perhaps the key difference between sensory and motor areas is the degree to which they are under volitional control versus sensory-obligate modulation.…”

Visual experience has opposing influences on the quality of stimulus representation in adult primary visual cortex

“…We reported the performance of this decoding process as accuracy across all response vector tested. Previously, we found that a value of k=4 resulted in the best average rank across mice ( Jeon et al, 2021 ), therefore we fixed the value of k to 4. Chance performance of the classifiers was 1 divided by the number of stimuli classified.…”

“…Furthermore, the fraction of neurons that were included in each figure panel is listed in Supplementary file 1B-D ; in the case the SEM was non-overlapping between the first imaging session and a given imaging sessions, the SD is included and paired t-test p values, adjusted for multiple comparisons as appropriate, are indicated. Prior to initiating the study, based on prior work ( Jeon et al, 2021 ), it was estimated that a sample size of n=6 animals would be sufficient to detect differences if present. Reported power was computed using IBM SPSS in the case of t-tests, and a power estimate (power e ) was computed in the case of Wilcoxon rank-sum tests using the Matlab function ‘sampsizepwr’ with alpha set to 0.01.…”

Visual experience has opposing influences on the quality of stimulus representation in adult primary visual cortex

“…We reported the performance of this decoding process as accuracy across all response vector tested. Previously we found that a value of k= 4 resulted in the best average rank across mice 50 , therefore we fixed the value of k to 4. Chance performance of the classifiers was 1 divided by the number of stimuli classified.…”