Computational models link cellular mechanisms of neuromodulation to large-scale neural dynamics

Drawing from advances in mathematics and related fields, we show that biophysical models of large-scale neural dynamics can help to bridge the gap between neuromodulation at the cellular scale and mesoscale systems dynamics at the whole-brain level. Decades of neurobiological research have disclosed the diverse manners in which the response properties of neurons are dynamically modulated to support adaptive cognitive functions. This neuromodulation is achieved through alterations in the biophysical properties of the neuron. However, changes in cognitive function do not arise directly from the modulation of individual neurons, but are mediated by population dynamics in mesoscopic neural ensembles. Understanding this multiscale mapping is an important but nontrivial issue. Here, we bridge these different levels of description by showing how computational models parametrically map classic neuromodulatory processes onto systems-level models of neural activity. The ensuing critical balance of systems-level activity supports perception and action, although our knowledge of this mapping remains incomplete. In this way, quantitative models that link microscale neuronal neuromodulation to systems-level brain function highlight gaps in knowledge and suggest new directions for integrating theoretical and experimental work.

Automated summary

This article demonstrates how biophysical models of large-scale neural dynamics can bridge the gap between neuromodulation at the cellular scale and whole-brain systems dynamics, through advancements in mathematics and related fields. While neuromodulation involves altering the biophysical properties of neurons to support adaptive cognitive functions, changes in cognitive function are not directly caused by individual neuron modulation, but are mediated by population dynamics in neural ensembles. Understanding this multiscale mapping is a challenging but crucial task.