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bioRxiv2024

Biophysical modeling of thalamic reticular nucleus subpopulations and their differential contribution to network dynamics

The burst firing mode of thalamic reticular neurons plays a pivotal role in the generation and maintenance of sleep rhythms and is implicated in sleep-related deficits characteristic of neurodevelopmental disorders. Although several models of reticular neurons have been developed to date, we currently lack a biophysically detailed model able to accurately reproduce the heterogeneity of burst firing observed experimentally. Using electrophysiology recordings of patch-clamped fluorescently tagged Spp1+ and Ecel1+ reticular neurons, we leverage a previously established statistical framework to introduce differentiation of cell types in model thalamic reticular neurons. We developed a population of biophysically detailed models of thalamic reticular neurons that capture the diversity of their firing properties, particularly their ability to generate rebound bursts. These models incorporate key ion channels, such as T-type Ca2+ and small conductance potassium channels (SK), and enable systematic investigations into the impact of these channels on single-cell dynamics. By integrating these models into a thalamic microcircuit, we demonstrate that T-type Ca2+ and SK channel conductances have opposing effects on spindle oscillations. We identify a simple relationship between these conductances and the peak firing frequency of spindles, maintained across circuits with mixed reticular neuron populations, providing a framework for understanding how ion channel expression influences thalamic network dynamics. Collectively, these models establish a foundation for relating intrinsic cellular properties of reticular cell populations to network-level activity in both healthy and pathological conditions.

neuroscience↗

bioRxiv2024

Osmolarity regulates C. elegans egg-laying behavior via parallel chemosensory and biophysical mechanisms

Animals alter their behavior in response to changes in the environment. Upon encountering hyperosmotic conditions, the nematode worm C. elegans initiates avoidance and cessation of egg-laying behavior. While the sensory pathway for osmotic avoidance is well-understood, less is known about how egg laying is inhibited. We analyzed egg-laying behavior after acute and chronic shifts to and from hyperosmotic media. Animals on 400 mM sorbitol stop laying eggs immediately but then resume [~]3 hours later, after accumulating additional eggs in the uterus. Surprisingly, the hyperosmotic cessation of egg laying did not require known osmotic avoidance signaling pathways. Acute hyperosmotic shifts in hyperosmotic-resistant mutants overproducing glycerol also blocked egg laying, but these animals resumed egg laying more quickly than similarly treated wild-type animals. These results suggest that hyperosmotic conditions disrupt a high-inside hydrostatic pressure gradient required for egg laying. Consistent with this hypothesis, animals adapted to hyperosmotic conditions laid more eggs after acute shifts back to normosmic conditions. Optogenetic stimulation of the HSN egg-laying command neurons in hyper-osmotic treated animals led to fewer and slower egg-laying events, an effect not seen following direct optogenetic stimulation of the postsynaptic vulval muscles. Hyperosmotic conditions also affected egg-laying circuit activity with the vulval muscles showing reduced Ca2+ transient amplitudes and frequency even after egg-laying resumes. Together, these results indicate that hyperosmotic conditions regulate egg-laying via two parallel mechanisms: a sensory pathway that acts to reduce HSN excitability and neurotransmitter release, and a biophysical mechanism where a hydrostatic pressure gradient reports egg accumulation in the uterus. Summary StatementWe find that hyperosmotic conditions inhibit C. elegans egg laying through both a sensory pathway and a separate biophysical pathway affecting a high-inside hydrostatic pressure gradient.

neuroscience↗

bioRxiv2025

Precise calcium-to-spike inference using biophysical generative models

The intramolecular dynamics of fluorescent calcium indicators distort the relationship between calcium signals and action potentials ("spikes"), hampering efficient spike inference from calcium imaging. To address this problem, we characterized the calcium response kinetics of three widely used indicators, GCaMP6f, jGCaMP7f, and jGCaMP8f, using in vitro stopped-flow measurements and brain slice recordings. We identify previously unreported kinetic features, including use-dependent slowing of fluorescence decay, that introduce systematic errors in linear model-based inference methods. Using these observations, we developed a multistate model of GCaMP and used it to create biophysically-inspired Bayesian Sequential Monte Carlo and machine learning inference models trained on synthetic datasets. These methods outperform existing methods on spike timing accuracy and correlation benchmarks derived from diverse cell types. Our results show that using synthetic data derived from our biophysical model yields a decoder that outperforms even those trained on extensive experimental data. By separating indicator characterization from inference, our framework, Calcium Spike Processing using Integrated Kinetic Estimation and Simulation (C-SPIKES), provides a generalizable strategy applicable to existing and future calcium indicators.

neuroscience↗

bioRxiv2025

Biophysical and molecular mechanisms responsible for phytoplankton sinking in response to starvation

Marine phytoplankton face eco-evolutionary pressure to regulate their vertical position in the ocean to access light, which is abundant towards the surface, and nutrients, which are found deeper down the water column. All phytoplankton experience gravitational sinking, which can contribute to their vertical migration. However, the biophysical and molecular mechanisms that impact gravitational sinking have not been systematically characterized across taxa and environmental conditions. Here, we combine simulations with measurements of cell mass, volume, and composition to investigate the effects of nutrient availability on gravitational sinking in 9 representative unicellular pico- and nanoplankton species. We find that gravitational sinking becomes faster in most species when starved, but the biophysical changes responsible for this vary across species and starvation conditions. For example, the faster sinking of Chaetoceros calcitrans is nearly exclusively driven by cell density whereas that of Emiliania huxleyi is due to cell volume. On the molecular level, the altered sinking is predominantly attributed to changes in cellular dry contents, rather than water. For example, starch accumulation increases sinking in 3 green algae species, and lipid accumulation decreases sinking in Phaeodactylum tricornutum. Overall, our work reveals that phytoplankton physiology has evolved multiple mechanisms that impact gravitational sinking in response to starvation, possibly to support the vertical migration of the cell.

cell biology↗

bioRxiv2025

Integrating Single-Cell Biophysical and Transcriptomic Features to Resolve Functional Heterogeneity in Mantle Cell Lymphoma

Intra-tumor heterogeneity impacts disease progression and therapeutic resistance but remains poorly characterized by conventional histologic, immunophenotypic, and molecular approaches. Single-cell biophysical properties distinguish functional phenotypes complementary to these approaches, providing additional insight into cellular diversity. Here we link both buoyant mass and stiffness to gene expression to identify clinically relevant phenotypes within primary mantle cell lymphoma (MCL) cells, employing MCL as a model of biological and clinical diversity in human cancer. Linked measurements reveal that buoyant mass and stiffness characterize B-cell development states from naive to plasma cell and correlate with expression of oncogenic B-cell receptor signaling genes such as BLK and CD79A. Additionally, changes in cell buoyant mass within primary patient specimens ex vivo correlate with sensitivity to Brutons Tyrosine Kinase inhibitors in vivo in MCL and chronic lymphocytic leukemia, another B-cell malignancy. These findings highlight the value of biophysical properties as biomarkers of response in pursuit of future precision therapeutic strategies.

cancer biology↗

bioRxiv2025

An Integrated In Vitro Platform and Biophysical Modeling Approach for Studying Synaptic Transmission in Isolated Neuronal Pairs

Studying synaptic transmission and plasticity is facilitated in experimental systems that isolate individual neuronal connections. We developed an integrated platform combining polydimethylsiloxane (PDMS) microstructures with high-density microelectrode arrays to isolate and record single neuronal pairs from human induced pluripotent stem cell (hiPSC)-derived neurons. The system maintained hundreds of parallel neuronal pairs for over 100 days, demonstrating functional synapses through pharmacological validation and long-term potentiation studies. We coupled this platform with a biophysical Hodgkin-Huxley model and simulation-based inference to extract mechanistic parameters from electrophysiological data. The analysis of long-term potentiation stimulation using a biophysical model revealed (-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid) AMPA and (N-methyl-D-aspartate) NMDA receptor-specific alterations, providing quantitative insights into synaptic plasticity mechanisms. This integrated approach represents the first system combining isolated synaptic pairs, long-term stability, and mechanistic modeling, offering unprecedented opportunities for studying human synaptic function and plasticity.

neuroscience↗

bioRxiv2025

Hybrid Scaffolds Decouple Biochemical & Biophysical Regulation of Cell Phenotype

The extracellular matrix changes dramatically during the progression of diseases like cancer. These complex, tissue-specific changes are not adequately replicated by most current biomaterial disease models. Here we demonstrate, for the first time, a biomaterial system allowing combined, independent control over stiffness, composition and 3D collagen architecture. We demonstrate successful perfusion of defined hydrogel formulations into ice-templated collagen scaffolds, controlling the composition of these hybrid scaffolds at constant stiffness. The Youngs moduli of these hybrid scaffolds can also be tuned independently of composition via chemical crosslinking. Encapsulation of human dermal fibroblasts reveals that fibroblast morphology depends on hybrid scaffold composition and on viscoelasticity, highlighting the importance of a system that decouples biophysical from biochemical properties. Finally, we demonstrate application of this system to exert combined control over biochemical and biophysical drivers of cell growth and invasion, focussing on breast cancer as proof-of-concept. We show that collagen fibre patterning enhances breast cancer cell proliferation, also directing the invasion of patient-derived breast cancer cells. These hybrid scaffolds are therefore promising new tools for dissecting the diverse but complementary roles played by the extracellular matrix in regulating cell phenotype, in a range of healthcare applications.

bioengineering↗

bioRxiv2025

Biophysically relevant network model of the piriform cortex predicts odor frequency encoding using network mechanisms

Olfactory-guided animals utilize fast concentration fluctuations in turbulent odor plumes to perceive olfactory landscapes. Recent studies have demonstrated that the olfactory bulb (OB) encodes such temporal features present in natural odor stimuli. However, whether this temporal information is encoded in the piriform cortex (PCx) remains unknown. Hence, we developed a biophysically relevant PCx network model and simulated it using previously recorded in vivo activities of mitral and tufted cells in response to 2Hz and 20Hz odor frequencies for three stimulus mixtures. Analysis of single-cell activity across trials revealed that individual pyramidal neurons (PYRs) were largely ineffective at discriminating between 2Hz and 20Hz. However, the trial-averaged activity of the PYR population could discriminate between the two frequencies significantly. Moreover, using log-likelihood scores we further discovered that odor frequency discrimination happened through a highly distributed mechanism among the PYRs. One-dimensional convolutional neural network models trained and tested on PYRs activities achieved discrimination accuracies up to 95%. Using virtual synaptic knockout models, we found that eliminating either feedback or feedforward inhibition onto PYRs improved the decoding accuracy across all odor conditions. Conversely, eliminating recurrent excitation among PYRs or simultaneously eliminating recurrent inhibition within both interneuron populations degraded decoding performance. Removing recurrent connections within individual interneuron populations had minimal effects on the performance. Overall, our PCx model demonstrates that it can discriminate between 2Hz and 20Hz odor stimuli, with a bidirectional capability of performance modulation by specific circuit motifs. These findings predict that the piriform cortex encodes and processes temporal features of odor stimuli. New & NoteworthyWe simulated the first biophysically relevant network model of the piriform cortex (PCx) to show differential encoding of odor frequencies at 2Hz and 20Hz. 1D convolutional neural networks demonstrated a distributed role of pyramidal neurons in the encoding. Surprisingly, eliminating feedforward or feedback inhibition improves frequency discrimination, while eliminating recurrency impairs it. Specific circuit motifs, not just baseline activity levels, determine odor frequency representation. Overall, our results predict the PCxs capacity for temporal odor processing.

neuroscience↗

bioRxiv2025

Closed-Loop Connectivity Best Supports Angular Tuning and Sleep Dynamics in a Biophysical Thalamocortical Circuit Model

Despite recent advancements in mapping thalamic and cortical projections, the specific organization of intrathalamic and corticothalamic connectivity remains elusive. Current experimental approaches cannot definitively determine whether these connections are arranged in reciprocal (closed-) or non-reciprocal (open-loop) circuits. We developed a biophysically detailed multi-compartmental model of the mouse whisker pathway, built on anatomical and physiological data. We showed that closed-loop intrathalamic projections between the thalamocortical (TC) relay neurons in the ventral posteromedial nucleus and the inhibitory neurons in the thalamic reticular nucleus (TRN) best reproduce thalamic spiking and local field potential responses across awake and sleep states. Increasing the percentage of closed-loop projections regulates the angular tuning in the awake state, while also supporting spindle oscillations during sleep. We also showed that direct activation of closed-loop corticothalamic feedback (CT[->]TC and CT[->]TRN), simulating TC inputs, sharpens the angular tuning in the thalamus. These results contribute to resolving a long-standing question regarding the organization of intrathalamic projections, offering insights into how thalamo-cortical circuits balance precise sensory tuning with robust oscillatory rhythms across behavioral states. Moreover, all model resources are open-source and available to other researchers interested in studying thalamocortical circuits. Author summaryA long standing question in the study of thalamocortical interactions is whether neurons in the thalamus form so-called open- or closed-loops when they project within the thalamus and to the cortex. In this study, we used a detailed computational model of the thalamic neurons in the whisker pathway of the mouse with realistic biophysics to investigate this question. We evaluated the impact of different connectivity arrangements in reproducing the activity of thalamic neurons observed during wakefulness and sleep. Our results show that a closed-loop circuit arrangement provides the best alternative to reproduce wake and sleep neuronal responses, highlighting the importance of computational modeling as a tool to disentangle thalamic circuit organization. We also showed that closed-loop projections from the cortex to the thalamus help further amplify the selectivity of thalamic neurons, suggesting a similar organization of corticothalamic projections. We hope our predictions can inform future experiments, and elucidate principles of thalamocortical connectivity that can be generalized across other thalamocortical motifs besides the whisker pathway and across species.

neuroscience↗

bioRxiv2025

Evaluating BOLD functional MRI biophysical simulation approaches: impact of vascular geometry, magnetic field calculations, and water diffusion models

Biophysical simulations have guided the development of blood oxygenation level-dependent (BOLD) functional MRI (fMRI) acquisitions and signal models that relate the BOLD signal to the underlying physiology, such as calibrated BOLD and vascular fingerprinting. Numerous simulation techniques have been developed, however, few of them have been directly compared, thus limiting the assessment of the accuracy and interchangeability of these methods as well as the accuracy of the quantitative techniques derived from them. In this work, we compared the accuracy and computational demands of eight previously published simulation approaches that adopt different geometries (ranging from infinite cylinders to synthetic vascular anatomical networks (VANs)), field offset calculations (analytical and Fourier-based), and water diffusion implementations (Monte Carlo and convolution-based), all of which are available in an open-source Python toolkit, BOLDs{omega}imsuite. The reference simulation approach for comparison used three-dimensional infinite cylinders, analytical field offsets, and Monte Carlo diffusion. When compared with the reference approach, most of the simulations, including two- and three-dimensional geometries, were in excellent agreement when assuming the intravascular signal contribution was small. Two commonly employed simulation approaches were notably biased; both used two-dimensional geometries with overly simplified vasculature or field offset calculations. In general, the simulated intravascular signal was the least consistent across approaches, thus potentially resulting in larger errors when the intravascular signal contribution is large. Lastly, the VAN results were in good agreement with the reference but they diverged slightly, yet systematically, from each other at smaller radii ([lsim] 3 m), primarily driven by intravascular signal differences. We conclude, therefore, that the reference approach is an attractive option for exploratory simulations in the many cases where anatomical and hemodynamic realism is not needed, balancing ease of implementation, accessibility, versatility, computational efficiency, accuracy of results, and interpretability. These findings help pave the way for a broader adoption of forward modelling of the BOLD signal and more reliable interpretations of biophysical simulations aiming to develop quantitative models of the BOLD signal.

neuroscience↗

bioRxiv2025

Dynamic Gene Regulatory Network Inference with Interpretable, Biophysically-Motivated Neural ODEs

Gene Regulatory Networks (GRNs) are complex dynamical systems that modulate gene expression and drive transitions between phenotypic cell states. Determining these networks is crucial in understanding how gene dysregulation can lead to phenotypic variation and perturbation responses. We present a novel biophysically-motivated neural ordinary differential equation (ODE) model framework with a biologically interpretable deep learning architecture that leverages dynamic single-cell data: in-CAHOOTTS (gene regulatory network Inference with Context Aware Hybrid neural-ODEs on Transcriptional Time-series Systems). Our approach combines accurate prediction with mechanistic interpretability, decomposing gene expression dynamics into fundamental biophysical processes (mRNA transcription and degradation) while inferring regulatory network structure. We validate in-CAHOOTTS by learning the Saccharomyces cerevisiae response to rapamycin treatment, and learning the dynamic cell cycle progression. The trained model accurately predicts gene expression trajectories and identifies biologically relevant rapamycin response regulators. The framework achieves stable long-term predictions with time frames extending 30 times beyond training data, maintaining realistic oscillatory dynamics for over 40 hours in cell cycle modeling, demonstrating that neural ODEs can capture true biological attractors rather than merely fitting data. We argue that interpretable neural ODEs can successfully model complex biological dynamics while revealing mechanistic insights essential for understanding living systems. By bridging extensible machine learning frameworks and mechanistic biology, in-CAHOOTTS represents a significant advance toward deep learning systems that both predict biological outcomes and explain the mechanisms driving them.

systems biology↗

bioRxiv2025

The environmental stress response controls the biophysical properties of the cytoplasm and is critical for survival in quiescence

All organisms have evolved survival strategies to cope with changes in environmental conditions. Nutrient deprivation, one of the most frequently encountered stresses in nature, causes haploid budding yeast to enter a reversible state of non-proliferation known as quiescence, which entails extensive remodeling of gene expression, metabolism and the cellular biophysical properties. Yeast cells can adapt to and survive long periods of time in glucose starvation-induced quiescence, provided they are able to respire in the early stages of glucose withdrawal. When respiration is blocked during glucose withdrawal, cells prematurely age and exhibit markedly reduced survival and cytoplasmic diffusion. We find here that respiration is required to induce a quiescence-related gene expression program. Induction of this program prior to withdrawing glucose in respiration-inhibited cells bypasses the need for respiration and rescues survival and biophysical properties to levels seen in glucose-starved but respiration-competent cells. This rescue effect relies on proteomic adaptation, which partially occurs through inactivation of Ras/PKA signaling and activation of the environmental stress response via the transcription factors Msn2/4. This signaling cascade triggers the expression of stress response genes and modulates the cytoplasmic diffusion state of cells, ensuring long-term survival in quiescence even in the absence of respiration. Our results highlight the importance of stress adaptation in quiescence and aging, integrating gene expression control and modulation of cytoplasmic properties to maintain cell fitness.

cell biology↗

bioRxiv2025

Exploring the likelihood of the membrane pollution hypothesis with a physiologically plausible biophysical model.

Epilepsy affects more than 52 million people worldwide and has been known since ancient times. Despite this long history, available therapeutic methods--both pharmacological and non-pharmacological--fail to control seizures in over 10% of patients. At the same time, there exists a diversity of theories regarding the fundamental mechanisms underlying epilepsy. Understanding the biophysical basis of the simplest manifestation of epileptic activity--the paroxysmal depolarization shift (PDS)--could therefore be highly valuable. Prevailing ideas consider PDS as exaggerated synaptic excitatory potentials, but experimental evidence shows that PDS can be generated in isolated neurons devoid of synaptic input. Ulrich Altrup proposed an alternative view, suggesting that PDS represent aberrantly large pacemaker potentials rather than giant excitatory postsynaptic potentials. Theoretical work by Hernandez-Caceres and Brenes supported this concept, demonstrating a bifurcation-like transformation from physiological pacemaker potentials to PDS and finally to long-duration sustained depolarizations. According to Altrups membrane pollution hypothesis (MPH), epileptic activity emerges as a consequence of incorporating amphiphilic pollutants into neuronal membranes, which leads to an increase in membrane micro-viscosity. Using a biophysically sound model for pacemaker activity, we explored the possibility of eliciting PDS through increased membrane micro-viscosity. Provided results suggest that this is theoretically plausible. Further experimental and theoretical research is needed to refine details of the MPH and to develop new strategies for combating epilepsy.

neuroscience↗

bioRxiv2025

Biophysical simulations of fMRI responses using realistic microvascular models: insights into distinct hemodynamics in humans and mice

1Functional Magnetic Resonance Imaging (fMRI) is broadly used to measure human brain activity, however the hemodynamic changes that comprise the fMRI response to neuronal activity are often interpreted using microscopy data in mice. These microscopy data provide ground-truth observations of how individual blood vessels respond to neuronal activity and thus form the basis of our fundamental understanding of neurovascular coupling. Although these invasive experiments provide invaluable insight, there are striking differences in the vascular architecture of mouse and human brains that may influence the hemodynamic response. Motivated by this, we developed a biophysical modeling framework for realistic hemodynamic simulations in both mouse and human cerebral cortex. For this, we utilized Vascular Anatomical Network (VAN) models that explicitly represent the full microvascular tree as a single connected network, originally based on anatomical reconstructions from a given location of mouse cerebral cortex. We extended the VAN modeling framework using synthetic VAN models representing the microvascular network at a single location of the human cerebral cortex. To account for larger size and complexity of the human VAN models, we developed an efficient computational framework to simulate the full hemodynamic responses in this human model and compared the simulated fMRI responses between mice and humans. Our biophysical simulations are based entirely on first principles (e.g., conservation of mass); model parameter values were fixed across all simulations, not tuned to fit data, as they represent meaningful physical constants taken from previous measurements. Only two simple calibrations were tuned for each simulation, to match baseline perfusion rates (blood flow) and oxygen extraction (OEF). Our results show that differences in microvasculature indeed influenced the hemodynamic response and led to observable differences in timing--e.g., the simulated fMRI response peak in humans was delayed by [~]2 s compared to that of mice, consistent with prior fMRI observations. While there are many known differences in vascular architecture in rodents and humans, we also discovered that, unexpectedly, an asymmetry in the numbers of branches of the penetrating intracortical arterioles and venules appears to be conserved across species. We demonstrate through further simulations that this anatomical property may also be needed for suitable hemodynamic responses. Our framework thus provides a valuable tool for bridging in-vivo microscopy of microvascular dynamics to human fMRI.

neuroscience↗

bioRxiv2025

Integrative Biophysical Illumination of the 3D GPCRome Dynamics

Achieving high-resolution insights into protein dynamics remains a major challenge, particularly for membrane proteins such as G protein-coupled receptors (GPCRs) - a highly dynamic class of cell surface receptors and key drug targets. Here, we present an integrative strategy that bridges computational and experimental biophysical methods for probing protein dynamics. To illustrate its potential, we developed GPCRmd-NMR, a platform that synergistically combines molecular dynamics (MD) simulations with high-resolution nuclear magnetic resonance (NMR) spectroscopy across the 3D GPCRome. The resulting dataset maps over 20 billion NMR signals to more than 7 million conformational snapshots of GPCRs, enabling structure-resolved analysis at an unprecedented scale. Application across multiple GPCRs provides access to previously underexplored functional GPCR dynamics, receptor states, and sites, including microswitches and hidden ligand entrance gateways. Our integrated framework offers a powerful solution to long-standing challenges faced by both experimental and computational researchers. To support broad accessibility, GPCRmd-NMR is available as an open-access, web-based framework equipped with user-friendly analysis tools (https://www.gpcrmd.org/gpcrmd-nmr-tool) and is readily applicable to other dynamic protein families beyond GPCRs and other types of biophysical data.

bioinformatics↗

bioRxiv2025

Neurophysiology of mismatch negativity generation: a biophysical modeling study

The mismatch negativity, or MMN, is a ubiquitous evoked brain response elicited by any discriminable change of an otherwise regular stimulus sequence. Despite its potential clinical relevance - the MMN is known to be affected by brain state, lesions, and neurologic/psychiatric disorders - and a growing body of animal work, the underlying neurophysiology of the MMN is not well understood. This hinders translation of circuit-level animal findings and mitigates the utility of the MMN as a neurologic/psychiatric biomarker. Here, we used biophysical modeling to examine the neurophysiological basis of the MMN as measured in a canonical auditory oddball paradigm with frequency deviants (i.e., tones whose frequency was shifted slightly with respect to standard tones). The response to standards was successfully modeled by a typical feedforward-followed-by-feedback input sequence. The response to deviants required additional, prolonged input to supragranular layers, consistent with input from the non-lemniscal thalamus. This additional input resulted in downward-going pyramidal-neuron currents in both layer 2/3 (via indirect somatic inhibition) and, critically, layer 5 (via direct apical excitation), which together generated the MMN. The results suggest that current circuit-level models of MMN generation derived from animal models are incomplete, and that further work is required to characterize the underlying neurophysiology of the MMN. SIGNIFICANCE STATEMENTThe mismatch negativity, or MMN, is a change-related brain response and one of the most widely studied brain responses in neuroscience. However, despite its prevalence and potential utility as a biomarker for neurological and psychiatric disorders, its underlying neurophysiology is not well understood. We combined MEG with biophysical modeling to better understand the cells, circuits, and cortical laminae that contribute to deviance processing and the MMN in humans.

neuroscience↗

bioRxiv2018

Biophysical mechanisms in the mammalian respiratory oscillator re-examined with a new data-driven computational model

An autorhythmic population of excitatory neurons in the brainstem pre-Botzinger complex is a critical component of the mammalian respiratory oscillator. Two intrinsic neuronal biophysical mechanisms--a persistent sodium current (INaP) and a calcium-activated non-selective cationic current (ICAN)--were proposed to individually or in combination generate cellular-and circuit-level oscillations, but their roles are debated without resolution. We re-examined these roles with a new computational model of an excitatory population with randomly distributed INaP and ICAN conductances and synaptic connections. This model robustly reproduces experimental data showing contrary to previous hypotheses, rhythm generation is independent of ICAN activation, which instead determines population activity amplitude. The novel insight is that this occurs when ICAN is primarily activated by neuronal calcium fluxes driven by synaptic mechanisms. Rhythm depends critically on INaP in a subpopulation forming the rhythmogenic kernel. The model explains how the rhythm and amplitude of respiratory oscillations involve distinct biophysical mechanisms.

bioinformatics↗

bioRxiv2018

Accurate action potential inference from a calcium sensor protein through biophysical modeling

Multiphoton imaging of genetically encoded calcium indicators is routinely used to report activity from populations of spatially resolved neurons in vivo. However, since the relationship between fluorescence and action potentials (APs) is nonlinear and varies over neurons, quantitatively inferring AP discharge is problematic. To address this we developed a biophysical model of calcium binding kinetics for the indicator GCaMP6s that accurately describes AP-evoked fluorescence changes in vivo. The models physical interpretation allowed the same parameters to describe GCaMP6s binding kinetics for both in vitro binding assays and in vivo imaging. Using this model, we developed an algorithm to infer APs from fluorescence and measured its accuracy with cell-attached electrical recordings. This approach consistently inferred more accurate AP counts and times than alternative methods for firing rates from 0 to >20 Hz, while requiring less training data. These results demonstrate the utility of quantitative, biophysically grounded models for complex biological data.

neuroscience↗