Jul 2026· Journal of Neural Engineering· Vol 23, pp. 046036· 0 citations· 36 references
PhysicsMedicine
TL;DR
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.
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.
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.
Ilaria Carannante, D. Depannemaecker, M. Woodman et al.· bioRxiv· 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.
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.
Yannaël Bossard, Lehna Bekri, A. Destexhe· 0 citations
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, 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
The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timing remains a fundamental open question. Dominant spiking neural network (SNN) approaches circumvent this by approximating backpropagation through surrogate gradients, decoupling learning from biological spike timing. Here, we reformulate temporal credit assignment as a state separation problem: extracting task-required components induced by historical perturbations directly from the current neural state. This enables an online feedback learning framework for NMCs through a gradient tunneling (GT) algorithm and the lead-lag expansion technique that derives credit assignment from local synaptic spike timing, while remaining compatible with ANN-SNN hybrid architectures. Experimentally, GT-trained NMCs excel at long-timescale evidence integration and noise-robust memory retention, and perform comparably to leading SNN online learning methods on real-world benchmarks with far fewer parameters. The proposed framework addresses the two-decade-old NMC feedback learning problem and suggests a computationally plausible explanation for the brain's learning mechanisms.
Xiangnan Zhang, Jingxin Liu, Ranqi Lu et al.· 0 citations
Large-scale whole-brain network models rely on structural connectomes to constrain simulated neural dynamics, yet the optimal processing of these anatomical scaffolds is not fully established. Here, we investigate the impact of structural connectome thresholding on whole-brain model performance to advance precision individual modelling. We measure model goodness-of-fit to both static functional connectivity and dynamic functional connectivity across a comprehensive spectrum of network densities, spatial parcellations, and model complexities using neuroimaging datasets of healthy individuals and a clinical cohort of post-stroke patients. We demonstrate that the optimal structural sparsity is highly resolution dependent. In coarse- grained parcellations, proportional thresholding enhances the model’s fit to static functional connectivity but relies on denser connectome to maintain dynamic functional fits. Conversely, fine-grained models require stringent connectome thresholding which simultaneously optimizes both static and dynamic functional fits. Taken together, these results indicate that the uncritical use of raw structural connectomes introduces suboptimal dynamical regimes, establishing resolution-tailored thresholding as an indispensable step for constructing precision brain network models.
Unknown authors· bioRxiv· 0 citations
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