.png){kind=icon}
.png){kind=icon}
Return
Brain information processing capacity modeling
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-022-05870-z.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Neurophysiological measurements suggest that human information processing is evinced by neuronal activity. However, the quantitative relationship between the activity of a brain region and its information processing capacity remains unclear. We introduce and validate a mathematical model of the information processing capacity of a brain region in terms of neuronal activity, input storage capacity, and the arrival rate of afferent information. We applied the model to fMRI data obtained from a flanker paradigm in young and old subjects. Our analysis showed that-for a given cognitive task and subject-higher information processing capacity leads to lower neuronal activity and faster responses. Crucially, processing capacity-as estimated from fMRI data-predicted task and age-related differences in reaction times, speaking to the model's predictive validity. This model offers a framework for modelling of brain dynamics in terms of information processing capacity, and may be exploited for studies of predictive coding and Bayes-optimal decision-making.
Keywords:
VISUAL WORKING-MEMORY
NEURONAL-ACTIVITY
NEURAL ACTIVITY
DYNAMICS
FMRI
POPULATIONS
ACTIVATION
NETWORKS
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.9
Papers:
27.1W
Citations:
83.5W