Neurotransmission-modulated whole-brain computation captures full task repertoire
Gustavo Deco1, Yonatan Sanz Perl2, Jakub Vohryzek3
1Center for Brain and Cognition, Computational Neuroscience Group, Faculty of Medicine and Life Sciences, Universitat Pompeu Fabra, Roc Boronat 138, 08018 Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, 08010 Barcelona, Spain; International Centre for Flourishing (multisite at Oxford, Aarhus and Barcelona), Oxford, UK.
The brain achieves complex computation on a minimal energy budget through neuromodulation, which alters brain region connectivity. This neurotransmission-modulated (NEMO) framework explains individual differences in brain computability and intelligence.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Cognitive Science
Background:
- The brain performs complex computations efficiently on a limited energy budget, outperforming artificial intelligence.
- Neuromodulation dynamically alters signal transmission effectiveness and regional connectivity, enabling computational richness.
- Understanding how the brain achieves this flexibility within a fixed architecture is a key challenge.
Purpose of the Study:
- To develop a whole-brain model integrating neurotransmitter maps to explain computational flexibility.
- To investigate how neuromodulation shapes brain dynamics for task performance.
- To define and measure "brain computability" and its relation to intelligence.
Main Methods:
- Integrated 19 empirical neurotransmitter maps into a whole-brain model.
- Developed the neurotransmission-modulated (NEMO) framework to simulate brain activity and task computation.
- Defined "brain computability" as an individual's capacity to fit all tasks within the model.
- Correlated brain computability with behavioral performance and intelligence measures.
Main Results:
- The NEMO framework demonstrated flexible computation across different tasks by modulating brain dynamics.
- Individual "brain computability" within the NEMO model correlated positively with task-specific behavioral performance.
- Higher brain computability also correlated with a general measure of individual intelligence.
- Neuromodulation was shown to sculpt brain dynamics within a fixed architecture to support diverse computations.
Conclusions:
- Neuromodulation is a critical mechanism for the brain's computational richness and energy efficiency.
- The NEMO framework provides a novel approach to modeling brain function and individual differences.
- Brain computability, as modeled by NEMO, is a valid predictor of cognitive abilities and intelligence.
