.png){kind=icon}
.png){kind=icon}
Return
Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex
.png){kind=icon}
OA
AI
DOI：10.1016/j.neuron.2016.09.038.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Neural responses in the visual cortex are variable, and there is now an abundance of data characterizing how the magnitude and structure of this variability depends on the stimulus. Current theories of cortical computation fail to account for these data; they either ignore variability altogether or only model its unstructured Poisson-like aspects. We develop a theory in which the cortex performs probabilistic inference such that population activity patterns represent statistical samples from the inferred probability distribution. Our main prediction is that perceptual uncertainty is directly encoded by the variability, rather than the average, of cortical responses. Through direct comparisons to previously published data as well as original data analyses, we show that a sampling-based probabilistic representation accounts for the structure of noise, signal, and spontaneous response variability and correlations in the primary visual cortex. These results suggest a novel role for neural variability in cortical dynamics and computations.
Keywords:
RESPONSE VARIABILITY
CORTICAL-NEURONS
BAYESIAN-INFERENCE
DYNAMICS
ORIENTATION
CELLS
INFORMATION
COMPUTATION
EMERGENCE
SPARSE
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
15
Papers:
1.4W
Citations:
9.9W