@article {67234, title = {High-precision coding in visual cortex.}, journal = {Cell}, volume = {184}, year = {2021}, month = {2021 May 13}, pages = {2767-78}, abstract = {

Individual neurons in visual cortex provide the brain with unreliable estimates of visual features. It is not known whether the single-neuron variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher order visual areas and measured stimulus discrimination thresholds of 0.35{\textdegree} and 0.37{\textdegree}, respectively, in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported in mice. This discrepancy could not be explained by stimulus properties or arousal states. Furthermore, behavioral variability during a sensory discrimination task could not be explained by neural variability in V1. Instead, behavior-related neural activity arose dynamically across a network of non-sensory brain areas. These results imply that perceptual discrimination in mice is limited by downstream decoders, not by neural noise in sensory representations.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2021.03.042}, author = {Stringer, Carsen and Michaelos, Michalis and Tsyboulski, Dmitri and Lindo, Sarah E and Pachitariu, Marius} } @article {67263, title = {Hunger or thirst state uncertainty is resolved by outcome evaluation in medial prefrontal cortex to guide decision-making.}, journal = {Nature Neuroscience}, volume = {24}, year = {2021}, month = {2021 Jul 01}, pages = {907-912}, abstract = {

Physiological need states direct decision-making toward re-establishing homeostasis. Using a two-alternative forced choice task for mice that models elements of human decisions, we found that varying hunger and thirst states caused need-inappropriate choices, such as food seeking when thirsty. These results show limits on interoceptive knowledge of hunger and thirst states to guide decision-making. Instead, need states were identified after food and water consumption by outcome evaluation, which depended on the medial prefrontal cortex.

}, issn = {1546-1726}, doi = {10.1038/s41593-021-00850-4}, author = {Eiselt, Anne-Kathrin and Chen, Susu and Chen, Jim and Arnold, Jon and Kim, Tahnbee and Pachitariu, Marius and Sternson, Scott M} } @article {65752, title = {High precision coding in visual cortex}, journal = {BioRxiv}, year = {2019}, month = {06/2019}, pages = {679324}, abstract = {

Single neurons in visual cortex provide unreliable measurements of visual features due to their high trial-to-trial variability. It is not known if this {\textquotedblleft}noise{\textquotedblright} extends its effects over large neural populations to impair the global encoding of sensory stimuli. We recorded simultaneously from \~{}20,000 neurons in mouse visual cortex and found that the neural population had discrimination thresholds of 0.3{\textdegree} in an orientation decoding task. These thresholds are \~{}100 times smaller than those reported behaviorally in mice. The discrepancy between neural and behavioral discrimination could not be explained by the types of stimuli we used, by behavioral states or by the sequential nature of trial-by-trial perceptual learning tasks. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.

}, doi = {https://doi.org/10.1101/679324}, author = {Stringer, Carsen and Michaelos, Michalis and Pachitariu, Marius} } @article {49534, title = {High-dimensional geometry of population responses in visual cortex.}, journal = {Nature}, volume = {571}, year = {2019}, month = {2019 Jun 26}, pages = {361-65}, abstract = {

A neuronal population encodes information most efficiently when its activity is uncorrelated and high-dimensional, and most robustly when its activity is correlated and lower-dimensional. Here, we analyzed the correlation structure of natural image coding, in large visual cortical populations recorded from awake mice. Evoked population activity was high dimensional, with correlations obeying an unexpected power-law: the n-th principal component variance scaled as 1/n. This was not inherited from the 1/f spectrum of natural images, because it persisted after stimulus whitening. We proved mathematically that the variance spectrum must decay at least this fast if a population code is smooth, i.e. if small changes in input cannot dominate population activity. The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, which we validated experimentally. These results suggest that coding smoothness represents a fundamental constraint governing correlations in neural population codes.

}, doi = {10.1038/s41586-019-1346-5}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Carandini, Matteo and Harris, Kenneth D} }