Daou, A. | Search bioRxiv

bioRxiv2026

Hill-Based Reformulation of the Hodgkin-Huxley Model for Interpretable Neuronal Excitability

Conductance-based models of neuronal excitability depend critically on the mathematical form used to describe voltage-dependent ion channel gating. The classical Hodgkin-Huxley (HH) formalism employs empirically derived rate expressions fitted to squid giant axon data that are not readily transferable across cell types or interpretable in terms of measurable gating properties. Here we introduce a Hill-based reformulation of the HH model in which steady-state sodium and potassium activation curves and the sodium inactivation rate are recast using Hill-type sigmoidal functions, a biologically motivated family widely used to describe cooperative and saturating processes in enzyme kinetics, gene regulation, and receptor binding. Systematic benchmarking against four compact sigmoid alternatives demonstrates that Hill functions provide superior fits to the original HH-derived gating data across all three targets. The resulting hybrid model reproduced canonical spike waveforms and frequency-current behavior, preserving the broad input-output organization of the original model. Importantly, the reformulation linked specific gating parameters to firing regimes and spike features, revealing how shifts in activation, inactivation, and steepness can systematically reshape excitability phenotypes. By making the relationship between channel kinetics and neuronal output more transparent, this framework provides an interpretable route for adapting conductance-based models to cell-specific excitability and channel-dependent changes in neural function.

neuroscience↗

bioRxiv2026

Rebound Relays and Inhibitory Vetoes Stabilize Sparse Sequential Activity in HVC

Brains build behavior by chaining actions and perceptions into precisely timed sequences, an ability central to speech, skilled movement, and memory, yet the circuit logic that propagates sequences remains unclear. Songbird HVC captures this problem: premotor HVCRA neurons burst once per motif, while basal ganglia-projecting HVCX neurons burst 2-4 times across the same motif. We developed a biophysically grounded HVC network as linked microcircuits encoding sub-syllabic segments. The model highlights inhibition not merely as suppressive but actively structuring sequence propagation and fidelity. Tonic inhibitory epochs prime HVCX by deinactivating T-type Ca2+ channels and recruiting Ih, so that release elicits precisely timed rebound bursts that recruit the next HVCRA ensemble. A complementary phasic inhibitory veto suppresses off-time activation, preventing pathological restarts while preserving HVCRA single-burst sparseness. More broadly, inhibitory timing can serve as the brains internal "clocked handshake," converting suppression into forward drive to advance sequences while enforcing error-corrected precision.

neuroscience↗

bioRxiv2025

Post-synaptic facilitation and network dynamics underlying stimulus-specific combination sensitivity

Combination-sensitive neurons (CSNs) integrate multiple stimulus features to generate behaviorally meaningful responses. While such neurons are well studied in fast timescale systems such as bat echolocation, the mechanisms enabling extended temporal integration in species like songbirds remain poorly understood. Using a computational model, we show that syllable-specific neurons in the songbird auditory system function as coincidence detectors whose selectivity depends on both post-inhibitory facilitation and persistent network activity. This persistence serves as a "memory trace", allowing precise association of sequential syllables across hundreds of milliseconds. We propose that this dynamic interplay between intrinsic neuronal properties and convergent synaptic inputs gives rise to a higher-order "meta-combination sensitivity" enabling the auditory system to transform discrete acoustic events into temporally extended percepts. Our findings provide a mechanistic framework that bridges theories of coincidence detection with longer-timescale working memory, highlighting the importance of distributed network mechanisms for auditory temporal coding.

neuroscience↗

bioRxiv2025

Temperature-Induced Bifurcations and Timescale Warping in the Hodgkin-Huxley Model: Dynamical Systems Insights into Temperature Modulation of Neuronal Excitability and Action Potential Morphology

Temperature fluctuations can have detrimental effects on the firing pattern and electrical activity of biological neurons, eliciting diverse responses depending on the neuronal cell types and the underlying ion channels exhibited. Using the classical Hodgkin-Huxley (HH) model, we performed a comprehensive dynamical systems analysis to determine how temperature fluctuations alter neuronal excitability, spike morphology, and bifurcation structure. We first relied on experimentally-derived temperature coefficients, or Q10 values, associated with gating kinetics and conductances, and examined codimension-1 and codimension-2 bifurcations across a range of temperatures and standard HH parameters governing the intrinsic properties (firing frequency, spike amplitude, spike width, afterhyperpolarization (AHP), time-to-peak AHP, etc...) of the model HH neuron. Our analysis revealed that increasing temperature accelerates gating dynamics, leading to narrower and higher-frequency spikes but reduced amplitudes, and ultimately to a loss of sustained firing via temperature-induced depolarization block. We identified generalized Hopf (Bautin) bifurcations as critical boundaries beyond which the system becomes strictly monostable. Extending the model to independently scale sodium activation, sodium inactivation, and potassium activation kinetics showed that excitability is particularly sensitive to potassium gating dynamics. Our findings provide a quantitative framework for understanding temperature modulations of neuronal activity, highlighting how temperature reshapes the excitability landscape, unveiling the intricate interplays between the activation/inactivation kinetics of ion channels, and identifying key parameters governing temperature robustness in neuronal models. Author SummaryNeurons communicate using brief electrical signals called action potentials, which are generated through the coordinated activity of the cocktail of ion channels that are expressed on the cell membrane. These electrical signals are sensitive to temperature; changes in heat can alter how quickly ion channels open and close, and even whether neurons can keep firing at all. While it is known that temperature can affect brain function, the precise ways it changes the fundamental electrical properties of neurons have not been fully mapped. In this study, we used the well-known Hodgkin-Huxley mathematical model of a neuron to explore how varying temperature changes its firing patterns. We applied tools from dynamical systems theory, such as bifurcation analysis, to identify the precise points where the neuron switches between active firing, silent resting, and mixed "bistable" states. We also discovered that certain ion channel properties, especially those controlling potassium flow, are particularly important in determining a neurons temperature sensitivity. Our results provide a detailed roadmap for understanding how temperature affects neural activity, with implications for biology, medicine, and the design of temperature-robust neural models.

neuroscience↗

bioRxiv2025

An Explainable Web-Based Diagnostic System for Alzheimer's Disease Using XRAI and Deep Learning on Brain MRI

Background Alzheimers disease (AD) is a progressive neurodegenerative condition marked by cognitive decline and memory loss. Despite advancements in AI-driven neu-roimaging analysis for AD detection, clinical deployment remains limited due to chal-lenges in model interpretability and usability. Explainable AI (XAI) frameworks such as XRAI offer potential to bridge this gap by providing clinically meaningful visualizations of model decision-making. Methods: This study developed a comprehensive, clinically deployable AI system for AD severity classification using 2D brain MRI data. Three deep learning architectures MobileNet-V3 Large, EfficientNet-B4, and ResNet-50 were trained on an augmented Kaggle dataset (33,984 images across four AD severity classes). The models were evaluated on both augmented and original datasets, with integrated XRAI explainability providing region-based attribution maps. A web-based clinical interface was built using Gradio to deliver real-time predictions and visual explanations. Results: MobileNet-V3 achieved the highest accuracy (99.18% on the augmented test set; 99.47% on the original dataset), while using the fewest parameters (4.2M), confirming its effi-ciency and suitability for clinical use. XRAI visualizations aligned with known neuroana-tomical patterns of AD progression, enhancing clinical interpretability. The web interface delivered sub-20 second inference with high classification confidence across all AD sever-ity levels, successfully supporting real-world diagnostic workflows. Conclusion: This re-search presents the first systematic integration of XRAI into AD severity classification us-ing MRI and deep learning. The MobileNet-V3-based system offers high accuracy, com-putational efficiency, and interpretability through a user-friendly clinical interface. These contributions demonstrate a practical pathway toward real-world adoption of explainable AI for early and accurate Alzheimers disease detection.

neuroscience↗

bioRxiv2025

Temperature Induced Codimension-One and Codimension-Two Bifurcations in Hodgkin-Huxley Neurons

Temperature fluctuations can have detrimental effects on the firing pattern and electrical activity of biological neurons, eliciting diverse responses depending on the neuronal cell types and the underlying ion channels exhibited. Using the classical Hodgkin-Huxley (HH) model, we performed a comprehensive dynamical systems analysis to determine how temperature fluctuations alter neuronal excitability, spike morphology, and bifurcation structure. We first relied on experimentally-derived temperature coefficients, or Q10 values, associated with gating kinetics and conductances, and examined codimension-1 and codimension-2 bifurcations across a range of temperatures and standard HH parameters governing the intrinsic properties (firing frequency, spike amplitude, spike width, afterhyperpolarization (AHP), time-to-peak AHP, etc...) of the model HH neuron. Our analysis revealed that increasing temperature accelerates gating dynamics, leading to narrower and higher-frequency spikes but reduced amplitudes, and ultimately to a loss of sustained firing via temperature-induced depolarization block. We identified generalized Hopf (Bautin) bifurcations as critical boundaries beyond which the system becomes strictly monostable. Extending the model to independently scale sodium activation, sodium inactivation, and potassium activation kinetics showed that excitability is particularly sensitive to potassium gating dynamics. Our findings provide a quantitative framework for understanding temperature modulations of neuronal activity, highlighting how temperature reshapes the excitability landscape, unveiling the intricate interplays between the activation/inactivation kinetics of ion channels, and identifying key parameters governing temperature robustness in neuronal models.

neuroscience↗

bioRxiv2024

Biophysical network modeling of temporal and stereotyped sequence propagation of neural activity in the premotor nucleus HVC

Stereotyped neural sequences are often exhibited in the brain, yet the neurophysiological mechanisms underlying their generation are not fully understood. Birdsong is a prominent model to study such behavior particularly because juvenile songbirds progressively learn from their tutors and by adulthood are able to sing stereotyped song patterns. The songbird premotor nucleus HVC coordinate motor and auditory activity responsible for learned vocalizations. The HVC comprises three neural populations that has distinct in vitro and in vivo electrophysiological responses. Typically, models that explain HVCs network either rely on intrinsic HVC circuitry to propagate sequential activity, rely on extrinsic feedback to advance the sequence or rely on both. Here, we developed a physiologically realistic neural network model incorporating the three classes of HVC neurons based on the ion channels and the synaptic currents that had been pharmacologically identified. Our model is based on a feedforward chain of microcircuits that encode for the different sub-syllabic segments (SSSs) and that interact with each other through structured feedback inhibition. The network reproduced the in vivo activity patterns of each class of HVC neurons, and unveiled key intrinsic and synaptic mechanisms that govern the sequential propagation of neural activity by highlighting important roles for the T-type Ca2+ current, Ca2+-dependent K+ current, A-type K+ current, hyperpolarization activated inward current, as well as excitatory and inhibitory synaptic currents. The result is a biophysically realistic model that suggests an improved characterization of the HVC network responsible for song production in the songbird. Significance StatementLearned motor sequences acquired through repetitive practice undergo stabilization and are integrated into cortical motor circuits through a process of consolidation. The process of mastering a complicated motor sequence requires thorough motor exploration to achieve closer alignment with the desired outcome, yet the neural mechanisms underlying sequence generation remain largely unexplored. In this study, we investigate the neural circuitry in a premotor region of the songbird brain (known as HVC) that is well-known for generating extremely precise learned sequences. We develop a biophysically realistic network model that incorporate pharmacologically identified intrinsic and synaptic currents for the three different classes of HVC neurons. The network highlights fundamental intrinsic and synaptic mechanisms regulating the sequential propagation of neural activity.

neuroscience↗

bioRxiv2021

Sequencing SARS-CoV-2 in a malaria research laboratory in Mali, West Africa: the road to sequencing the first SARS-CoV-2 genome in Mali

Next generation sequencing (NGS) has become a necessary tool for genomic epidemiology. Even though the utility of genomics in human health has been proved, the genomic surveillance has never been so important until the COVID 19 pandemic. This has been evidenced with the detection of new variants of SARS-CoV-2 in the United Kingdom, South Africa and Brazil recently using genomic surveillance. Until recently, Malian scientists did not have access to any local NGS platform and samples had to be shipped abroad for sequencing. Here, we report on how we adapted a laboratory setup for Plasmodium research to generate the first complete SARS-CoV-2 genome locally. Total RNA underwent a library preparation using an Illumina TruSeq stranded RNA kit. A metagenomics sequencing was performed on an Illumina MiSeq platform following by bioinformatic analyses on a local server in Mali. We recovered a full genome of SARS-CoV-2 of 29 kb with an average depth coverage of 200x. We have demonstrated our capability of generating a high quality genome with limited resources and highlight the need to develop genomics capacity locally to solve health problems. We discuss challenges related to access to reagents during a pandemic period and propose some home-made solutions.

genomics↗