Biology subjects

Zbili, M.

Publications and source records attributed to Zbili, M..

10 recordsLinked to original sources

bioRxiv2026

A Detailed Model for Understanding the Human Neocortex

The neocortex underlies cognitive abilities that set humans apart from other species. Although Ramon y Cajal initiated its study in the 19th century, much about its fundamental properties remain poorly understood. Biologically detail modeling, has been shown to serve as a tool to understand the modeled system better. By comparing computational models for different species we can highlight functional differences between them, find their anatomical or physiological basis and thus improve our understanding of cortical function. In this study we built a detailed model of a human cortical microcircuit following an established workflow. We compared the human data and results against a previously published reconstruction of rat cortical circuitry. To parametrize the human model, we gathered new original data on human morphological reconstructions, axonal bouton densities, single cell and synaptic recordings. We combined them with data available in the literature and open-sourced databases. We also developed various strategies to overcome the missing data, such as generalizing or adapting data from rodents. The resulting model consists of seven columnar units with similar characteristics. Each column has a radius of 476 {micro}m, a height of 2622 {micro}m, a volume of 1.86 mm3, a total cell density of 24,186 cells/mm3, on the order of 35,000 cells, around 12 million connections and approximately 47 million synapses. Comparing the rat and the human model showed that the human cortex is less dense in terms of cell bodies than the rodent cortex. Human cells have more complex branching, but lower bouton densities than rodent cells. However, the number of connections between cell types is similar.

neuroscience↗

bioRxiv2025

Sex specific axon initial segment plasticity underlies cortical hyperexcitability in trigeminal pain

Neuropathic pain results from peripheral lesion, causing maladaptive plasticity and central sensitization. Clinical and preclinical studies demonstrate that modifications of primary sensory cortex (S1) activity are essential for neuropathic pain persistence. Rodent studies report heightened S1 pyramidal cell excitability in neuropathic pain model, the origins of which remain debated. The axon initial segment, the action potential trigger zone, is a major determinant of neuronal excitability and is known to undergo structural changes after neural perturbation but its role in chronic pain is poorly understood and no studies have explored its role in cortical hyperexcitability in chronic pain models. Besides, despite a higher prevalence of chronic pain in women, most of preclinical studies have been conducted in males. By integrating electrophysiology, immunohistochemistry, and computational modeling, this study demonstrates that in a trigeminal neuropathic pain rat model, sex-specific structural plasticity of the axon initial segment enhances the excitability of layer 5 pyramidal cells in the somatosensory cortex, potentially driving network-level hyperactivity.

neuroscience↗

bioRxiv2025

Sensory plasticity of dorsal horn silent neurons: a critical mechanism for neuropathic pain

The spinal cord dorsal horn (DH) integrates and modulates sensory processing but undergoes critical plasticity following nerve injury, leading to pain hypersensitivity. Mechanical allodynia, or touch-evoked pain, is a highly prevalent and debilitating symptom of neuropathic pain. It has been proposed that, after nerve injury, innocuous sensory neurons gain access to nociceptive-specific (NS) circuits in the DH due to altered spinal inhibitory controls, thereby converting touch into pain. It is however unclear how sensory processing is reorganized in these conditions across the different laminae of the DH to generate this symptom. In this study, we developed a novel ex vivo somatosensory preparation to selectively analyze excitatory neuronal activity across all DH laminae simultaneously, following physiological stimulations of the skin. Using two-photon calcium (Ca2+) imaging, we studied the DH activity under physiological conditions, after spinal disinhibition or nerve injury, and generated a computational model to reveal the sensory plasticity of individual DH neurons that leads to neuropathic pain. We demonstrate that spinal disinhibition, whether pharmacologically induced or resulting from nerve injury, converts most DH excitatory neurons into highly polymodal cells. We further show that such disinhibition unmasks an unprecedented number of previously silent neurons in both superficial and deep DH laminae, responding to a wide dynamic range (WDR) of sensory modalities. The computational model pinpoints that neuropathic pain does not result primarily from the transformation of excitatory NS neurons into WDR neurons, but rather from the activation of a previously dormant excitatory circuit. This newly active circuit spans both superficial and deep DH laminae and is predominantly composed of WDR excitatory neurons The identification of this extensive silent neuronal network provides critical insights into DH plasticity mechanisms underlying neuropathic pain, and should guide future therapeutic strategies. HighlightsSensory modalities of dorsal horn neurons are defined by spinal inhibition Neuropathic mechanical allodynia does not result from the transformation of nociceptive specific neurons into wide dynamic range neurons Neuropathic pain is mediated by the activation of a previously silent circuit

neuroscience↗

bioRxiv2024

Higher-Order Interactions in Neuronal Function: From Genes to Ionic Currents in Biophysical Models

Neuronal firing patterns are the consequence of precise variations in neuronal membrane potential, which are themselves shaped by multiple ionic currents. In this study, we use biophysical models, statistical methods, and information theory to explore the interaction between these ionic currents and neuron electrophysiological phenotype. We created numerous electrical models with diverse firing patterns. By analyzing these models, we identified intricate relationships between model parameters and electrical features. Our findings show that neuronal activity is often influenced by multiple biophysical model parameters, in a non-additive (i.e. synergistic) fashion. When comparing this with single-cell RNAseq data, we found a contrasting structure: gene expression profiles were dominated by redundancy, reflecting differences in regulatory constraints and sampling diversity. This research sheds light on the complex links between biophysical parameters and neuronal phenotypes.

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bioRxiv2023

Controlling morpho-electrophysiological variability of neurons with detailed biophysical models

Variability is a universal feature among biological units such as neuronal cells as they enable a robust encoding of a high volume of information in neuronal circuits and prevent hyper synchronizations such as epileptic seizures. While most computational studies on electrophysiological variability in neuronal circuits were done with simplified neuron models, we instead focus on the variability of detailed biophysical models of neurons. With measures of experimental variability, we leverage a Markov chain Monte Carlo method to generate populations of electrical models able to reproduce the variability from sets of experimental recordings. By matching input resistances of soma and axon initial segments with the one of dendrites, we produce a compatible set of morphologies and electrical models that faithfully represent a given morpho-electrical type. We demonstrate our approach on layer 5 pyramidal cells with continuous adapting firing type and show that morphological variability is insufficient to reproduce electrical variability. Overall, this approach provides a strong statistical basis to create detailed models of neurons with controlled variability.

neuroscience↗

bioRxiv2022

A multi-modal fitting approach to construct single-neuron models with patch clamp and high-density microelectrode arrays

In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of non-somatic compartments. In order to provide a richer set of data as input to multicompartment models, we here investigate the combination of somatic patch-clamp recordings with recordings of high-density micro-electrode arrays (HD-MEAs). HD-MEAs enable the observation of extracellular potentials and neural activity of neuronal compartments at sub-cellular resolution. In this work, we introduce a novel framework to combine patch-clamp and HD-MEA data to construct multicompartment models. We first validate our method on a ground-truth model with known parameters and show that the use of features extracted from extracellular signals, in addition to intracellular ones, yields models enabling better fits than using intracellular features alone. We also demonstrate our procedure using experimental data by constructing cell models from in vitro cell cultures. The proposed multi-modal fitting procedure has the potential to augment the modeling efforts of the computational neuroscience community and to provide the field with neuronal models that are more realistic and can be better validated. Author SummaryMulticompartment models are one of the most biophysically detailed representations of single neurons. The vast majority of these models are built using experimental data from somatic recordings. However, neurons are much more than just their soma and one needs recordings from distal neurites to build an accurate model. In this article, we combine the patch-clamp technique with extracellular high-density microelectrode arrays (HD-MEAs) to compensate this shortcoming. In fact, HD-MEAs readouts allow one to record the neuronal signal in the entire axonal arbor. We show that the proposed multi-modal strategy is superior to the use of patch clamp alone using an existing model as ground-truth. Finally, we show an application of this strategy on experimental data from cultured neurons.

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bioRxiv2021

After-hyperpolarization promotes the firing of mitral cells through a voltage dependent modification of action potential threshold

In the olfactory bulb (OB), mitral cells (MCs) display a spontaneous firing that is characterized by bursts of action potentials intermixed with silent periods. Burst firing frequency and duration are heterogeneous among MCs and increase with membrane depolarization. By using patch clamp recording on rat slices, we dissected out the intrinsic properties responsible of this activity. We showed that the threshold of action potential (AP) generation dynamically changes as a function of the trajectory of the membrane potential; becoming more negative when the membrane was hyperpolarized and having a recovering rate, inversely proportional to the membrane repolarization rate. Such variations appeared to be produced by changes in the inactivation state of voltage dependent Na+ channels. Thus, the modification AP threshold favours the initiation of the burst following hyperpolarizing event such as negative membrane oscillations or inhibitory transmission. After the first AP, the following afterhyperpolarization (AHP) brought the threshold just below the membrane resting potential or within membrane oscillations and, as a consequence, the threshold was exceeded during the fast repolarization component of the AHP. In this way the fast AHP acts as a regenerative mechanism that sustains the firing. Bursts were stopped by the development of a slow repolarization component of the AHP. The AHP characteristics appeared as determining the bursting properties; AHP with larger amplitudes and faster repolarizations being associated with longer and higher frequency bursts. Thus, the increase of bursts length and frequency upon membrane depolarization would be attributable to the modifications of the AHP and of Na+ channels inactivation.

neuroscience↗

bioRxiv2021

Homeostatic regulation of axonal Kv1.1 channels accounts for both synaptic and intrinsic modifications in CA3 circuit

Homeostatic plasticity of intrinsic excitability goes hand-in-hand with homeostatic plasticity of synaptic transmission. However, the mechanisms linking the two forms of homeostatic regulation have not been identified so far. Using electrophysiological, imaging and immunohistochemical techniques, we show here that blockade of excitatory synaptic receptors for 2-3 days induces an up-regulation of synaptic strength at CA3-CA3 connexions and intrinsic excitability of CA3 pyramidal neurons. Activity-deprived connexions were found to express a high release probability, an insensitivity to dendrotoxin, and a lack of depolarization-induced presynaptic facilitation, indicating a loss of presynaptic Kv1.1 function. The down-regulation of Kv1.1 channels in activity-deprived neurons was confirmed by their broader action potentials measured in the axon that were insensitive to dendrotoxin. We conclude that regulation of axonal Kv1.1 channel constitutes a unique mechanism linking intrinsic excitability and synaptic strength that accounts for the functional synergy existing between homeostatic regulation of intrinsic excitability and synaptic transmission.

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bioRxiv2020

A quick and easy way to estimate entropy and mutual information for neuroscience

AO_SCPLOWBSTRACTC_SCPLOWCalculations of entropy of a signal or mutual information between two variables are valuable analytical tools in the field of neuroscience. They can be applied to all types of data, capture nonlinear interactions and are model independent. Yet the limited size and number of recordings one can collect in a series of experiments makes their calculation highly prone to sampling bias. Mathematical methods to overcome this so-called "sampling disaster" exist, but require significant expertise, great time and computational costs. As such, there is a need for a simple, unbiased and computationally efficient tool for estimating the level of entropy and mutual information. In this paper, we propose that application of entropy-encoding compression algorithms widely used in text and image compression fulfill these requirements. By simply saving the signal in PNG picture format and measuring the size of the file on the hard drive, we can estimate entropy changes through different conditions. Furthermore, with some simple modifications of the PNG file, we can also estimate the evolution of mutual information between a stimulus and the observed responses through different conditions. We first demonstrate the applicability of this method using white-noise-like signals. Then, while this method can be used in all kind of experimental conditions, we provide examples of its application in patch-clamp recordings, detection of place cells and histological data. Although this method does not give an absolute value of entropy or mutual information, it is mathematically correct, and its simplicity and broad use make it a powerful tool for their estimation through experiments.

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bioRxiv2019

Axonal Na+ channels detect and transmit levels of input synchrony in local brain circuits

Sensory processing requires mechanisms of fast coincidence-detection to discriminate synchronous from asynchronous inputs. Spike-threshold adaptation enables such a discrimination but is ineffective in transmitting this information to the network. We show here that presynaptic axonal sodium channels read and transmit precise levels of input synchrony to the postsynaptic cell by modulating the presynaptic action potential (AP) amplitude. As a consequence, synaptic transmission is facilitated at cortical synapses when the presynaptic spike is produced by synchronous inputs. Using dual soma-axon recordings, imaging, and modeling, we show that this facilitation results from enhanced AP amplitude in the axon due to minimized inactivation of axonal sodium-channels. Quantifying local circuit activity and using network modeling, we found that spikes induced by synchronous inputs produced a larger effect on network activity than spikes induced by asynchronous inputs. Therefore, this input-synchrony dependent facilitation (ISF) may constitute a powerful mechanism regulating spike transmission.

neuroscience↗