Brain information processing capacity modeling

• Published in: Scientific reports Vol. 12; no. 1; pp. 2174 - 16
• Main Authors: Li, Tongtong, Zheng, Yu, Wang, Zhe, Zhu, David C., Ren, Jian, Liu, Taosheng, Friston, Karl
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.02.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378; 639/166; Age; Aged; Bayesian analysis; Brain; Brain - diagnostic imaging; Brain - physiology; Brain Mapping; Cognitive ability; Decision making; Functional magnetic resonance imaging; Humanities and Social Sciences; Humans; Information processing; Magnetic Resonance Imaging; Male; Mathematical models; Mental Processes; Models, Neurological; multidisciplinary; Neural coding; Neurons - physiology; Science; Science (multidisciplinary); Sensory neurons; Storage capacity; Young Adult
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-05870-z

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.