A DATA-DRIVEN BIOPHYSICAL FRAMEWORK BRIDGING NEUROMODULATION TO MESOSCALE DYNAMICS

Neuromodulators profoundly reshape brain dynamics by acting on multiple cellular and synaptic targets, yet how these distributed effects combine to reorganize network states remains poorly understood. A key challenge is linking well-characterized single-cell effects of neuromodulators to emergent mesoscale phenomena.

Here, we introduce a generic, biophysically grounded spiking network model designed as a task-agnostic, data-constrained framework to study the mechanistic effects of neuromodulation. It consists of scalable Hodgkin–Huxley–type neurons in configurable circuit topologies and incorporates neuromodulatory actions on intrinsic currents and synaptic interactions. The model is optimized to reproduce the activity of networks of cortical neurons recorded in vivo using large-scale electrophysiology, directly relating cellular modulation to emergent network states.

Using this approach, we perform systematic sensitivity and perturbation analyses to identify how neuromodulation reorganizes network dynamics, alters transitions between activity regimes, and interacts with network topology. At the mesoscale, we characterize the effects of neuromodulation on population dimensionality, avalanche statistics, and perturbation propagation. These results reveal a complex network reorganization rather than uniform gain modulation. Comparing neuromodulated and unmodulated networks reveals how distinct dynamical regimes emerge from combinations of cellular-level effects.

This work establishes a general, scalable computational framework for dissecting the mechanistic principles by which neuromodulators reorganize network dynamics across scales, which provides a mechanistic bridge between single-neuron modulation and population-level dynamics.