Title : Brain information processing capacity modeling
Abstract:
Characterizing the information processing capacity of the human brain is a key challenge in cognitive psychology and neuroscience. Most of the existing research in this area has focused on the capacity limit of short-term working memory, or how well an individual handles information processing demands when several tasks have to be executed simultaneously. It is believed that our visual short-term memory can maintain representations of three to four objects at any given moment. Along this line, information processing capacity was mapped to the computational capacity of a dynamic system and characterized as the total number of linearly independent functions of its stimuli the system can compute.
Previous research in neurophysiology suggests that human information processing is reflected in neuronal activity. Existing models of neuronal activity offer a panoramic coverage of brain dynamics, from the single neuron, through neural populations, to brain networks. However, under all these models, the quantitative relationship between the activity of a brain region and its information processing capacity remains unclear.
In this paper, we considered neuronal activity and information processing capacity from an information-theoretic perspective. Starting from an information conservation law, we showed that for an individual brain region, the neuronal activity, the information processing capacity, the input storage capacity, and the arrival rate of exogenous information can all be related through a first-order differential equation. Theoretically, our model indicates that the difference between the information arrival rate and the information processing rate directly influences neural activity changes. Higher information arrival rate enhances the neuronal activity, while larger processing capacity decreases neuronal activity; on the other hand, larger input information storage capacity can alleviate the demand on neuronal activity, when the arrival rate increases.
We applied this model to an empirical fMRI dataset, which was acquired under a rapid event-related arrow flanker task—used to study aging-associated decline in selective attention and executive functions. Both young and old adult groups participated in the experiment. We analyzed individual brain regions that were activated in both the young and old groups. We also considered overall information processing by averaging the data from each region. Our numerical analysis demonstrated the accuracy of the model in explaining fMRI measurements and showed that—for a given cognitive task—higher information processing capacity engenders a lower neuronal activity and faster response in younger subjects. That is, younger adults have faster responses and better performance in the flanker test than the seniors because they have higher information processing capacity. This result is consistent with the findings in literature suggesting that high-capacity individuals tend to have lower neuronal activity, and that—compared with young adults—more brain activation was required for older adults to accomplish the same cognitive task. Crucially, these findings speak to the predictive and construct validity of the model, in the sense that we were able to predict the behavioral responses more accurately from (independent) fMRI responses.
While the processing capacity model is a new finding, it is reassuring that—although originating from information theory—our model has a similar functional form to the conductance-based neural mass models in DCM, as well as the IF model of individual neurons. The implication here is that—with an information conservation law as the cornerstone—our model is not limited to brain regions but can be applied to any neuronal system that has the attributes of information processing and storage capability. In sum, the model offers a framework for multiscale modelling of brain dynamics in terms of information processing and provides a new perspective on computational architectures in the brain; and it can be applied to any data from which neuronal activity can be estimated.
What will audience learn from your presentation?
• The audience will learn an innovative method on how to estimate localized information processing and storage capacity from neuronal activity in individual brain regions or brain networks.
• This paper is an initial step towards the quantitative characterization of the information processing capacity of individual brain regions. The IPC 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. The model can be used to explore the capacity limit of human brain, and can also be used to evaluate the information loss in different neural systems or brain regions, especially those involved in overflow-driven faulty decision making or abnormal conditions such as Alzheimer’s disease or seizures.
