Transcriptome-Inspired Spiking Simulations Uncover Human-Specific Prefrontal Dynamics and Provide a Mechanistic Platform for Species-Appropriate Disease Modeling

BRIDGE: A Computational Workflow from Single Neurons to Network of Mean-Field Models

BRIDGE provides a reproducible foundation for developing biologically informed mean-field models suitable for large-scale and whole-brain simulations, supporting the transition from generic homogeneous population models toward region-specific ones.

Mechanistic bridges from receptors to whole-brain dynamics: mean-field reductions, validity domains, and computational trade-offs

Many pharmacological and pathological perturbations arise at molecular, synaptic, or cellular scales, but are observed through population and whole-brain signals. Cross-scale reductions must preserve relevant mechanisms while remaining tractable. This review asks which microscopic mechanisms remain explicit, interpretable, and testable after reduction, and what claims these models support. Using receptor-aware adaptive mean fields from the master-equation lineage as a worked case, we trace finite-size population statistics and semi-analytical transfer functions into conductance-based adaptive nodes coupled through the connectome. We compare this strategy with phenomenological neural masses, low-dimensional and population-density reductions, large-scale spiking models, and learned or hybrid surrogates, including computational work and memory traffic. Receptor-dependent synaptic kinetics, conductance state, and spike-frequency adaptation can remain manipulable across scales, enabling interpretable interventions and testable mesoscopic and macroscopic consequences. However, this relies on coarse-grained Markovianity, population homogeneity, quasi-stationary transfer functions, moment closure, regional uniformity, and measurement-specific observation models. First-order implementations discard covariance dynamics, while macroscopic agreement cannot identify a unique molecular cause. Node-local biological detail mainly changes prefactors, whereas dense global covariances change the scaling class. Cross-scale models should therefore be judged by the interventions and observables they preserve, validity domain, identifiability, empirical adequacy, and computational burden. Receptor-aware mean fields are not universal, but offer a transparent, tractable strategy for selected mechanistic questions when each reduction step is independently validated.

A computational framework for fitting biophysical basal-ganglia network models, applied to Parkinsonian beta oscillations

Objective. Fitting biophysically detailed spiking-network models to data is constrained by computational cost: simulating thousands of coupled conductance-based neurons at sub-millisecond time steps makes large parameter searches impractical on conventional hardware, so circuit-level models have relied on manual tuning, reduced neuron formalisms, or modest network sizes. We present an integrated pipeline that makes automated, data-driven fitting of such models tractable on a single cloud graphics processing unit (GPU). Approach. The pipeline couples a just-in-time (JIT) compiled implementation of a biophysically detailed subthalamic–pallidal (STN, GPe, GPi) network in JAX with covariance-matrix-adaptation evolution strategy black-box optimization under Optuna, and a fixed-indegree connectivity scheme so that fitted configurations transfer across network sizes. Because fitting is inexpensive, each configuration is reported with its sensitivity to search bounds, loss weights, optimizer seeds, neuronal heterogeneity, and connectivity density. Main results. On an NVIDIA L4 GPU, JIT compilation and kernel fusion accelerate a 450-neuron simulation by approximately 736-fold over the same model run as an un-jitted Python loop, with a further twofold from GPU over an 8-core CPU. A 1000-trial optimization completes in roughly 17 min, and a fitted configuration transfers across a hundredfold range of network size for less than a twofold increase in wall time. Applied to firing-rate, coefficient-of-variation, and beta-band targets from the MPTP-primate parkinsonism literature, the pipeline recovers a parkinsonian configuration whose subthalamic beta power peaks near 29 Hz and is highly elevated relative to healthy. The probes separate a data-constrained increase in STN-to-GPe excitatory weight, robust under a symmetric-bounds control, from a prior-constrained reduction in GPe-to-STN inhibitory weight that reverses when bounds are made symmetric. Significance. The pipeline is an accessible, transparent tool for fitting biophysically detailed network models, turning parameter identifiability into a routine output; the basal ganglia result is a proof-of-concept rather than a mechanistic claim about pathological beta.

Learning Biophysical Models of Large-Scale Multineuronal Data to Enable Precise Neurostimulation

Multi-compartment Hodgkin-Huxley (HH) models provide a principled framework for predicting neural dynamics and responses to electrical stimulation. However, fitting HH biophysical parameters typically requires intracellular recordings, which are invasive and low-throughput, limiting the ability to capture the geometry and cell-specific properties of many neurons in a given neural circuit. Multi-electrode arrays (MEAs) offer a scalable alternative - high-density extracellular measurements from full neural populations, but HH model complexity has so far precluded reliable biophysical inference from extracellular data alone. Here, we introduce a framework to rapidly infer HH parameters from designed features of extracellular MEA measurements by leveraging differentiable biophysical simulation and simulation-based inference, unlocking a wide range of downstream applications. In this work, we focus on a central goal of translational neuroengineering: predicting neural spiking responses to candidate neurostimulation patterns that would take hours to measure clinically. To validate our approach, we collected hundreds of hours of stimulation and recording data from isolated macaque retina with a 30 um-pitch 512-electrode array. Our framework predicted previously unseen multi-electrode stimulation responses with 90.6% accuracy using HH models fit from only a few minutes of recording, replacing hours of stimulus testing.

Mouse to Human Cross-Species Transfer Learning for Electrophysiology-to-Transcriptomics Mapping in Cortical GABAergic Interneurons

Connectome-constrained modeling identifies neurons and synapses that sustain spontaneous activity in Drosophila