Biology subjects

Carannante, I.

Publications and source records attributed to Carannante, I..

5 recordsLinked to original sources

bioRxiv2026

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

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 O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/742067v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1124404org.highwire.dtl.DTLVardef@2f8b2aorg.highwire.dtl.DTLVardef@1598f37org.highwire.dtl.DTLVardef@c9814b_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Circadian Modulation of Excitation-Inhibition Balance Drives Ictal Transitions in a Mechanistic Model of Epileptic Networks

Epileptic seizures emerge from pathological synchronization in neuronal networks and are strongly influenced by circadian rhythms. Here, we developed a computational framework to investigate how circadian modulation of excitation/inhibition (E/I) balance shapes transitions from physiological to pathological activity. The model consists of interacting excitatory and inhibitory populations containing varying proportions of impaired neurons with altered intrinsic excitability. Circadian effects were incorporated through modulation of synaptic time constants, mimicking daily fluctuations in E/I dynamics. Network activity was characterized using firing rates and the Spike Time Tiling Coefficient (STTC), enabling simultaneous assessment of excitability and synchrony. Our results show that seizure-like dynamics arise from nonlinear interactions between network composition, neuronal impairment, and synaptic kinetics. Distinct dynamic regimes emerged, separated by sharp transitions in synchrony and activity patterns. These findings provide a mechanistic link between circadian regulation and seizure susceptibility, supporting the development of chronotherapy approaches for epilepsy.

neuroscience↗

bioRxiv2025

Neuromodulators control neuronal dynamics through feature space reshaping

The activity of neurons in the central nervous system changes drastically across different states like asleep or awake, attentive or drowsy, executing motor commands or resting. Neuromodulation supports these changes by affecting neuronal excitability. Much work has been devoted to understanding the effects of neuromodulators on various neurons. However, we still lack a cohesive picture of how neuromodulation affects neuronal dynamics. Here, we analysed electrophysiological data from published papers and extracted features for seven types of neurons from five areas of the human and rodent central nervous system, under the effect of five neuromodulators. We studied neuromodulation dynamics using the widespread, simple, yet biologically accurate, Adaptive Exponential Integrate-and-Fire model. We have found that different neuromodulators remap the feature space of neurons into non-overlapping clusters. This work organises our knowledge about neuromodulation in a compact format, useful for modellers and theoreticians.

neuroscience↗

bioRxiv2023

The impact of Parkinson's disease on striatal network connectivity and cortico-striatal drive: an in-silico study

Striatum, the input stage of the basal ganglia, is important for sensory-motor integration, initiation and selection of behaviour, as well as reward learning. Striatum receives glutamatergic inputs from mainly cortex and thalamus. In rodents, the striatal projection neurons (SPNs), giving rise to the direct and the indirect pathway (dSPNs and iSPNs, respectively), account for 95% of the neurons and the remaining 5% are GABAergic and cholinergic interneurons. Interneuron axon terminals as well as local dSPN and iSPN axon collaterals form an intricate striatal network. Following chronic dopamine depletion as in Parkinsons disease (PD), both morphological and electrophysiological striatal neuronal features have been shown to be altered in rodent models. Our goal with this in-silico study is twofold: a) to predict and quantify how the intrastriatal network connectivity structure becomes altered as a consequence of the morphological changes reported at the single neuron level, and b) to investigate how the effective glutamatergic drive to the SPNs would need to be altered to account for the activity level seen in SPNs during PD. In summary we predict that the richness of the connectivity motifs in the striatal network is significantly decreased during PD, while at the same time a substantial enhancement of the effective glutamatergic drive to striatum is present. AUTHOR SUMMARYThis in-silico study predicts the impact that the single cell neuronal morphological alterations will have on the striatal microcircuit connectivity. We find that the richness in the topological striatal motifs is significantly reduced in Parkinsons disease, highlighting that just measuring the pairwise connectivity between neurons gives an incomplete description of network connectivity. Moreover, we predict how the resulting electrophysiological changes of SPN excitability together with their reduced number of dendritic branches affect their response to the glutamatergic drive from cortex and thalamus. We find that the effective glutamatergic drive is likely significantly increased in PD, in accordance with the hyperglutamatergic hypothesis.

neuroscience↗

bioRxiv2019

Phenomenological models of NaV1.5. A side by side, procedural, hands-on comparison between Hodgkin-Huxley and kinetic formalisms.

BackgroundComputational models of ionic channels represent the building blocks of conductance-based, biologically inspired models of neurons and neural networks. Ionic channels are still widely modelled by means of the formalism developed by the seminal work of Hodgkin and Huxley, although the electrophysiological features of the channels are currently known to be better fitted by means of kinetic (Markov-type) models.\n\nObjectiveThe present study is aimed at showing why kinetic, simplified models are better suited to model ionic channels compared to Hodgkin and Huxley models, and how the manual optimization process is rationally carried out in practice for these two kinds of models.\n\nMethodsPreviously published experimental data on macroscopic currents of an illustrative ionic channel (NaV1.5) are exploited to develop a step by step optimization of the two models in close comparison. The proposed kinetic model is a simplified one, consisting of five states and ten transitions.\n\nResultsA conflicting practical limitation is recognized for the Hodgkin and Huxley model, which only supplies one parameter to model two distinct electrophysiological behaviours (namely the steady-state availability and the recovery from inactivation). In addition, a step by step procedure is provided to correctly optimize the kinetic model.\n\nConclusionSimplified kinetic models are at the moment the best option to closely approximate the known complexity of the ionic channel macroscopic currents. Their optimization is achievable by means of a rationally guided procedure, and it results in models with computational burdens comparable with those from Hodgkin and Huxley models.

neuroscience↗