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.
Abstract
Mean-field models are extensively used in large-scale brain simulations because they provide a wieldy description of population dynamics while preserving key features of neural activity. Despite their widespread adoption, no common and reproducible methodology currently exists to systematically derive and validate mean-field models starting from biologically grounded single neuron dynamics. As a result, implementations are often ad hoc, difficult to reproduce and rarely reusable. Here we introduce BRIDGE, a modular, open-source Python pipeline that enables the bottom-up reconstruction, analysis, validation, and simulation of mean-field models from single neurons. The framework integrates single neurons modelling, network simulations, extraction of population statistics, parameters analysis, quantitative comparisons between spiking neural networks and corresponding mean-field representations, and simulation of network of mean-fields. Its flexible architecture allows users to incorporate different neuron models and to generate region-specific or state-dependent mean-field formulations. 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. Graphical abstract
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.
K. Ranak, William S. Anderson· Journal of Neural Engineerin...· 0 citations
Whole-brain transcriptomic atlases are now widely available, yet computational neural models are almost exclusively parameterized from rodent data and used to infer human brain function, an extrapolation whose cost remains unquantified. To address this, we constructed a biophysically detailed, conductance-based Hodgkin–Huxley spiking microcircuit of a five-population prefrontal network, where every ion-channel, receptor, and gap-junction conductance was scaled by cell-type-specific gene expression. We parameterized the identical circuit using single-nucleus RNA-seq from mouse mPFC and human DLPFC, alongside a literature-derived baseline, and compared their high-frequency-oscillation (HFO) outputs across seven physiological and pathological states. While population firing rates differed only modestly between the two refinements (∼20% for pyramidal and PV cells), the oscillatory dynamics diverged dramatically. The human-refined circuit generated strongly synchronized PV activity and robust ripple- and fast-ripple-band power (e.g., healthy-wake ripple power, in arbitrary units: 322 vs. 24 and 22), whereas the mouse-refined and literature arms remained asynchronous (interneuron synchrony: 0.21 vs. 0.02). This human ≫mouse ≈ original hierarchy was statistically consistent across all seven states (significant arm differences in 75/77 comparisons). Mechanistically, the human transcriptome drove markedly stronger PV–PV electrical coupling (gap-junction scale: 1.78 vs. 0.96) paired with stronger recurrent pyramidal excitation, which collectively synchronized the fast-spiking PV population into a coherent rhythm that perisomatic inhibition then imposed on the local field potential. Critically, these results are model-dependent; the gene-to-conductance mapping is phenomenological, and mRNA expression does not linearly translate to functional conductance. Nonetheless, under this mapping the divergence localizes PV-mediated coupling and excitation–inhibition balance as the parameters most in need of human-specific recalibration. More broadly, this work establishes transcriptome-informed spiking simulation as a powerful strategy for uncovering species-specific computational principles and for building mechanistically grounded, human-relevant models of prefrontal circuit dysfunction, an approach that moves beyond generic rodent defaults to enable targeted, species-appropriate modeling of neurological and psychiatric disorders.
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.
Yannaël Bossard, Lehna Bekri, A. Destexhe· 0 citations
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.
A. Lotlikar, Ian-Christopher Tanoh, Praful K. Vasireddy et al.· 0 citations
Accurate and scalable fitting of spiking neuron models to experimental electrophysiological data remains a significant challenge due to its large parametric space. The current paper introduces a hybrid optimization method to address the challenge of parameter fitting on spiking neuron models mapping them into experimental constraints. A sequential hybrid approach combining Differential Evolution (DE) and Nelder–Mead (NM) was used to fit point neuron model parameters to experimental benchmarks of cerebellar neurons. Cerebellum spiking dynamics were optimized with the hybrid algorithm and was compared to Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) under both parallelized and non-parallelized simulations. The robustness was tested under various constraint conditions, and the pattern separation analysis was assessed on public datasets. Statistical comparisons indicated that the hybrid DE-NM approach outperformed GA and PSO across various modelled neuron types, with faster convergence and lower optimization errors. Mean absolute errors were observed to be below 5 Hz and success rates above 75% across neuron types. The optimized models were used to reconstruct the cerebellar input layer network, reproducing theta-band resonance, sparse coding, and pattern separation. A GUI-based tool, NeuronOpt, was implemented on the workflow improving the accessibility and repeatability of the approach and allowing the method reusable beyond cerebellar neurons. The tool was validated with cerebellum neural and input layer circuit dynamics. The tool and the methodology allow computational reliability of the hybrid optimization framework to be used for optimization across multiple applications.
Radhika Shrimankar, Asha Vijayan, Giovanni Naldi et al.· IEEE Access· 0 citations
Micro-electrode array (MEA) recordings are widely used to characterize functional connectivity in neural cultures and have gained traction for the analysis of human brain slices. However, the impact of graph construction methodology on the resulting network topology has not been systematically quantified. Here, we benchmark three methods - shared spiking activity, Pearson cross-correlation, and the spike time tiling coefficient (STTC) - across 37 recordings from human cortical slice cultures classified into low, moderate, and high activity groups. We show that method choice alone produces large topological differences (Cohen’s d = 0.86–1.14 for clustering coefficient, d > 1.0 for node count), while higher-order features such as modularity remain stable. Each method exhibits a distinct sensitivity profile: shared spiking detects activity-dependent changes primarily through network size, correlation uniquely captures clustering differences, and STTC combines strong biological sensitivity with negligible parameter dependence across lag windows (all d < 0.1). Within shared spiking, z-score normalization dominates all other parameter choices (d > 1.0 versus bin size effects of d < 0.23), functioning as an implicit analytical null model that fundamentally reshapes the edge set rather than merely rescaling weights. Inter-method edge overlap is low (Jaccard index 0.08–0.45) and activity dependent, demonstrating that these methods identify substantially different connections from identical data. Our results reveal that methodological choices including construction method, threshold, and normalization introduce hidden degrees of freedom with effect sizes comparable to the biological signals being measured. We provide practical recommendations for parameter selection, reporting, and cross-method validation in MEA-based network neuroscience. Author Summary When we record electrical activity from brain tissue using grids of electrodes, we can ask how different sites influence one another and map the tissue as a network of connections. Thanks to novel culturing methods, this approach is increasingly used to study human brain slices. However, deciding what is “connected” is not well defined. Researchers use several different methods, and it has never been clear how much this choice shapes the network they end up describing. Here we compared three widely used methods on 37 recordings from human cortical slices spanning a range of activity levels. We found that the method alone can change the apparent structure of the network as much as real biological differences do. The methods frequently disagreed about which connections exist and some technical choices, including normalization techniques, had surprisingly large effects. Because these hidden choices can rival the biological signal, we provide this benchmarking work with practical recommendations for selecting, reporting, and cross-checking methods, so that network studies of brain tissue become more transparent, comparable, and reproducible.
J. Ort, V. Witzig, Aniella Bak et al.· bioRxiv· 0 citations