Biological Cybernetics

Inhibitory synapses can control a neurons firing rate and also control supralinear dendritic integration. It is not known how inhibitory synapses can learn to perform these functions using only signals available locally at the synaptic site. We study an inhibitory plasticity rule based on the Bienenstock-Cooper-Munro theory in multicompartment models of striatal projection neurons, and show that it can perform these two functions. The rule uses local voltage-gated calcium concentration in the dendrites to regulate inhibitory synaptic strength. We show that, for rate-coded inputs, the rule can achieve precise control of neuronal firing rate after changes in excitatory input rate or excitatory synaptic strength. Additionally, for sparsely-coded inputs that activate localized synaptic clusters in dendrites, the rule can either allow or inhibit the supralinear dendritic response evoked by the clustered excitatory synapses, or equalize the dendritic response arising from different clusters. Finally, we demonstrate the use of learning to inhibit supralinear dendritic integration for solving the nonlinear feature binding problem (NFBP), in tandem with a simple excitatory plasticity rule. We conclude by discussing why the collateral inhibitory synapses between striatal projection neurons could contribute to solving the NFBP with this plasticity rule. Author summaryNeurons are the main cells in the nervous system that process information. They receive signals from the bodys senses--both external and internal--and use them to guide actions such as muscle movement and the regulation of bodily functions. A neuron becomes active when incoming signals excite it strongly enough. But for neurons to work timely, precisely, and reliably, their activity needs to be shaped, modified and controlled. This is done by inhibition, which comes from specialized inhibitory neurons. In this article we study how inhibition can learn to do two of its most basic roles in the nervous system. The first is to help neurons stay responsive across a wide range of input strengths--from very weak to very strong stimulation. For example, neurons in the retina allow vision both in dim starlight and in bright sunlight, even though these conditions differ in brightness by a trillion-fold. Inhibition contributes to handling this huge range by preventing overstimulation of the neurons in bright light. The second role of inhibition is to control strong, local excitations that occur on specific dendritic branches of a neuron. These local excitations can suddenly push a neuron into activity, and inhibition controls whether such excitations are allowed or suppressed. We use a learning mechanism that is already known to exist for excitatory synapses, but here we apply it to inhibition to explore what it could achieve. The results show that if inhibitory synapses used this same learning rule, they could support the two fundamental roles of inhibition in the nervous system described above.

Rhythmic neural activity underlies essential biological functions such as locomotion, breathing, and feeding. Computational models are widely used to study how such rhythms emerge from interactions between neuron-level and circuit-level dynamics. Intrinsically bursting neurons are key components of many central pattern generators (CPGs), yet existing models span a tradeoff between biological realism and practical usability. Biophysical models involve many parameters that are difficult to tune, whereas abstract models often integrate poorly into neural circuit simulations. We propose a simplified model of intrinsically bursting neurons derived from a reduced non-spiking biophysical formulation. The model integrates readily into neural circuits while enabling direct and independent control of bursting characteristics, including duration, amplitude, and shape. We show that the model reproduces single-unit biophysical responses to diverse stimuli as well as circuit-level activity patterns from crustacean and mammalian CPGs. This model provides a practical tool for studying rhythm generation in neural circuits.

A central question in neuroscience is how neural processing generates or encodes behavior. Caenorhabditis elegans is well suited to addressing this question, given its compact nervous system and near-complete structural connectome. Despite this, findings from previous studies remain inconclusive. While some have shown that the connectome can robustly encode specific behaviors such as locomotion, others report that functional connectivity can be reconfigured across behaviors. We aim to understand the relationship between structural connectivity, functional connectivity and biological behavior in silico by using an experimentally motivated computational model leveraging the structural connectome. Stimulation of specific neurons in the model induces oscillatory neural responses, enabling us to infer neuronal functional connectivity. Functional connectivity is found to be stronger among some neurons, allowing us to identify functional communities. We find that electrical synapses play a critical role in determining functional communities, and the resulting mesoscale functional architecture is predominantly gap junctionally assortative. Furthermore, comparison with behavioral circuits shows that locomotion circuits are largely segregated into distinct functional communities while other circuits are more distributed across multiple functional communities. We also observe that stimulation of neurons belonging to these distributed circuits elicits a more synchronized neuronal response compared to stimulation of neurons within the more segregated circuits. This is consistent with the presence of behavioral patterns that originate in one circuit and terminate in another (e.g., chemosensation leading to locomotion), such that stimulation of one circuit can activate the other and eventually result in a synchronized response. We also find a large repertoire of chimera-like synchronization patterns upon stimulation of certain behavioral circuits (chemosensation, mechanosensation) indicating high dynamical flexibility. Overall, our results demonstrate that while certain behaviors are governed by functionally segregated circuits, others emerge from the synchronization of multiple functional communities, which are, to begin with, influenced by the underlying structural connectivity. Author summaryAnimals constantly transform sensory inputs into actions, but it is still unclear how this mapping from neural activity to behavior is implemented in a real nervous system. Caenorhabditis elegans offers a unique testbed for this question because its entire wiring diagram is nearly completely mapped. Yet, previous works have reached mixed conclusions about how well this anatomical circuit diagram predicts actual patterns of activity and behavior. Here, we use a biologically inspired computational model of the C. elegans nervous system to bridge this gap between structure, function, and behavior. By virtually stimulating individual neurons and observing the resulting network-wide oscillations, we infer how strongly different pairs and groups of neurons interact in functional terms. We then use network analysis tools to identify groups of neurons that tend to co-activate, and relate these functional communities to known behavioral circuits for locomotion and sensory processing. We find that gap junctions play a key role in shaping functional communities, and that locomotion-related neurons are more functionally segregated than neurons involved in other behaviors, which are more functionally distributed. Our results suggest that some behaviors rely on specialized, functionally isolated circuits, whereas others emerge from the coordinated activity of multiple functional communities.

Following the suggestion of L. F. Jaffe [1] we propose a mathematical model of fast calcium induced calcium influx waves (CICI Waves). They can propagate at relatively high speeds (up to 1300 micrometers/s). According to [1], they propagate due to a mechanochemical interaction of actomyosin network with the cell membrane. The local stretching of the membrane caused by actin filaments opens mechanically operated ion channels resulting in the influx of calcium to the cell. Moreover, stretching a cells membrane at one point opens nearby stretch activated calcium channels because the mechanical force is relayed by the actin filaments interconnected by myosin bridges. The number of bridges as well as filament density increases with calcium concentration, causing the contraction of the actomyosin network. Thus, the force acting on the membrane from tangled actin filaments is transmitted ahead of the moving front of the calcium concentration. As a result, the ion channels are opened even before the signal of calcium reaches them. This leads to much larger propagation speed of CICI waves in comparison with calcium induced calcium released (CICR) waves, where the wave is sustained by the diffusion of calcium and autocatalytic release of calcium from the internal stores (e.g. endoplasmic reticula).

Muscle mass significantly influences skeletal muscle behaviour, potentially explaining why traditional massless Hill-type models struggle to predict the forces generated by larger muscles during dynamic, submaximal contractions. However, the applicability of mass-enhanced Hill-type models in human locomotion remains unexplored. Here, we compared the predicted force from a 1D mass-enhanced Hill-type muscle model with a traditional 1D massless Hill-type muscle model across a range of experimentally measured human movements. Kinematic and electromyographic data were collected from twenty participants performing locomotor tasks and supplemented with existing cycling data. Muscle size was geometrically scaled by factors from 0.1 to 10, which causes lengths to be scaled proportionally, cross-sectional area and peak isometric force F0 with the square, and mass with the cube of the factor. Muscle tissue mass (inertia) and cadence increased the differences between mass-enhanced and massless predictions of force and power. At high cadence and the largest scale, the normalized root mean square difference between force traces reached 7% of F0, (averaged across muscles). However, differences between models were minimal (<1%) at human-sized scale 1. Real muscle additionally deforms in 3D, we still do not know the extent to which this extra dimensionality affects muscle forces for these human movements.

The shoulder complex is a unique musculoskeletal structure capable of versatile motor behaviour yet requiring delicate control. The purpose of our work was to better understand the nature of musculoskeletal redundancy at the shoulder accounting for biomechanical demands and motor control strategies. Using a biomechanical model of the shoulder, we simulated a series of static exertions. Joint moment results from inverse dynamics were combined with an iterative sampling method to survey the landscape of feasible muscle activity patterns. By repeating the sampling process across different numbers of degrees of freedom at the shoulder, we demonstrate how emergent solutions are shaped by the biomechanical demands at each of the shoulder joints. Furthermore, we observed that the degree of musculoskeletal redundancy appears to be higher among the scapulohumeral muscles than the thoracohumeral and thoracoscapular muscles. Finally, we found that many of the muscle activity patterns requiring similar effort costs as the minimal effort solution have similar activation profiles, but there can be a wide range of possibilities especially at greater task intensities. Altogether, the simulations provide insight into neuromuscular control and musculoskeletal model decision-making process for the shoulder.

A key component of intraneuronal communication is the modulation of postsynaptic firing frequencies by stochastic transmitter release from presynaptic neurons. The time interval between successive postsynaptic firings is called the inter-spike interval (ISI), and understanding its statistics is integral to neural information processing. We start with a model of an excitatory chemical synapse with postsynaptic neuron firing governed as per a classical integrate-and-fire model. Using a first-passage time framework, we derive exact analytical results for the ISI statistical moments, revealing parameter regimes driving precision in postsynaptic action potential timing. Next, we extended this analysis to include both an excitatory and an inhibitory presynaptic connection onto the same postsynaptic neuron. We consider both a fixed postsynaptic-firing threshold and a threshold that adapts based on the postsynaptic membrane potential history. Our analysis shows that the latter adaptive threshold can result in scenarios where increasing the inhibitory input frequency increases the postsynaptic firing frequency. Moreover, we characterize parameter regimes where ISI noise is hypo-exponential or hyperexponential based on its coefficient of variation being less than or higher than one, respectively.

Neuronal activity alters extracellular ion concentrations and electric potentials. Ephaptic effects refer to the feedback influence that these extracellular changes can have on neuronal activity. While electric ephaptic effects occur on a fast timescale due to extracellular potential perturbations, ionic ephaptic effects are driven by slower, accumulative changes in ion concentrations. Among the previous computational studies of ephaptic effects, the vast majority have focused exclusively on electric effects, while ionic ephaptic effects have largely been neglected. In this work, we present an electrodiffusive computational framework consisting of two-compartment neurons that interact via a shared extracellular space. By accounting for both electric potentials and ion-concentration dynamics in a self-consistent manner, our framework enables us to explore the relative roles of electric and ionic ephaptic effects. Through numerical experiments, we demonstrate that ionic and electric ephaptic interactions play very different roles. While ionic ephaptic interactions increase population firing rates, electric ephaptic interactions primarily drive subtle shifts in spike timing. Furthermore, we show that these spike shifts cause the phase difference (the distance in spike times between a small collection of neurons) to converge to a stable, unique phase difference, which we coin the ephaptic intrinsic phase preference. Author summaryNeurons predominantly communicate through synapses: specialized contact points where a brief electrical signal, known as a spike or action potential, in one neuron influences another. Neurons generate these spikes by exchanging ions with the surrounding extracellular space. This way, spiking neurons alter extracellular ion concentrations and electric potentials. Since neurons are sensitive to such changes in their environment, they can also influence one another indirectly through the shared extracellular medium. This form of non-synaptic interaction is known as ephaptic coupling. Most computational models of neuronal activity neglect ephaptic interactions, and those that include them typically consider only electric effects while ignoring ionic contributions. As a result, the relative roles of electric and ionic ephaptic effects remain poorly understood. Here, we introduce a computational framework that accounts for both mechanisms in a self-consistent way. Our results show a functional distinction: ionic ephaptic effects act slowly, regulating population firing rates, whereas electric ephaptic effects act on millisecond timescales and subtly shift spike timing. These shifts cause spike-time differences between neurons to converge to a stable value, a phenomenon we call ephaptic intrinsic phase preference.

We present an approach to analysing cell homeostasis using a bond graph modelling approach that ensures that the conservation laws of physics (conservation of mass, charge, and energy, respectively) are satisfied for the interdependent biochemical, electrical, mechanical, and thermal energy storage mechanisms operating within the cell. We apply the bond graph approach to several cell membrane transport mechanisms and then consider how physics constrains intracellular electrolyte homeostasis for enterocytes (the epithelial absorptive cells of the gut). The model includes the electrogenic sodium-potassium ATPase pump (NKA), the glucose transporter (GLUT2), and an inwardly rectifying potassium channel, all in the basolateral membrane, and the electrogenic sodium-driven glucose transporter (SGLT1) in the apical membrane. Glycolysis converts the imported glucose to ATP to drive NKA. For specified levels of sodium, potassium, and glucose in the blood, the model demonstrates how enterocytes absorb sodium and glucose from the gut and transfer glucose to the blood while maintaining the membrane potential and homeostasis of intracellular sodium and potassium. The Gibbs free energy available from the ATP hydrolysis ensures that the cell operates as a sodium battery with a high external to internal ratio of sodium concentration in order to provide the energy for many other cellular transport processes. We show that the 3:2 stoichiometry of Na+/K+ exchange in NKA, coupled with 2:1 Na+/glucose cotransport in SGLT1, a 1:2:2 ratio between glucose consumption and ATP and water production in glycolysis, and K+ and glucose efflux through Kir and GLUT2, respectively, provides a balanced system that maintains homeostasis of intracellular Na+, K+, glucose, ATP and water, and homeostasis of the membrane potential, under varying levels of transport of glucose from the gut to the blood. We also show how the flux expressions for SLC transporters, ATPase pumps and ion channels can all be expressed in a consistent and thermodynamically valid way.

Voltage perturbations to a repetitively firing Hodgkin-Huxley (HH) model of neuronal spiking in the bistable regime with coexisting limit cycle and stable steady node can either lead to the spikes phase resetting or collapse to the stable steady state. The latter describes a non-firing hyperpolarized quiescent state of the neuron despite the presence of constant external current. Using asymptotic phase response curve (PRC), the impact of voltage perturbations on a repetitively firing HH model is studied here while it is diffusively coupled to another HH model under identical external stimulation. It is observed that the pre-perturbation state of synchronization and the coupling strength critically determine the PRC response of the perturbed HH dynamics. Higher coupling strengths of perfectly in-phase (anti-phase) synchronized HH models shrink (expand) the combinatorial space of perturbation strengths and the oscillation phases causing collapse to the quiescent state. This indicates reduced (enlarged) basin of attraction, viz. the null space, associated with the steady state in the HH phase space. The findings bear important implications to the spiking dynamics of diverse interneurons, as well as special cases of pyramidal neurons, coupled through electrical synapses via. gap junctions, and suggest the role of gap junction plasticity in tuning vulnerability to quiescent state in the presence of biological noise and spikelets.

Neural circuits are remarkably robust to perturbations that threaten their function. Activity-dependent homeostatic plasticity (ADHP) is a stabilizing mechanism that supports robustness by tuning neuronal ion conductances to combat chronic over- or under-activity. Its restorative capacity has been demonstrated in the pyloric circuit of the crustacean stomatogastric ganglion, whose neurons must burst in a specific order to coordinate digestive muscles. After disruption by physical and pharmacological manipulations, this circuit reliably recovers not only the activity levels of constituent neurons, but also the proper burst order. But how could ADHP, operating only on local information about each neurons average activity, maintain higher-order circuit properties? We explored this question in a computational model of the pyloric pattern generator. We first optimized a set of pyloric-like networks, then optimized ADHP mechanisms for each network to restore its pyloric character after parametric perturbations. This was possible for some networks and impossible for others, so we aimed to explain this disparity. We found that successful homeostatic regulators target average neural activity levels which happen to occur only among pyloric circuits and not among non-pyloric ones, within the set of reachable circuit configurations. Therefore, in subsets of parameter space where such dissociation is possible, activity carries indirect information about burst order, which ADHP can exploit to maintain pyloricness. Other subsets, whose pyloric averages are inseparable from non-pyloric ones, cannot be perfectly regulated. This separability property may explain differences in recovery capacity across perturbations and across individuals.

Motor unit (MU) activity during electrically or mechanically evoked reflexes is used to examine the relationship between neural inputs and MU properties. However, variations in single-MU reflex amplitudes are not fully understood and limit their reliability in determining the input-output relation of motor neurons (MNs). Using experiments and computer simulations, we investigated (i) whether MN discharge statistics and muscle activation explain the variability of reflex amplitude estimates and (ii) whether these variations are reflected differently across distinct reflex amplitude estimation methods. We analyzed MU spike trains extracted from isometric contractions of the tibialis anterior muscle at 10 % and 20 % MVC (maximum voluntary contraction). Estimating reflex amplitudes based on the peristimulus frequencygram (PSF) at 10 % MVC, the linear regression between discharge rate (DR) and reflex amplitude was always positive, with p < 0.05 in 3 out of 6 subjects; however, the linear correlation was inconsistent at 20 % MVC. We thereby observed that inter-subject variability was associated with the coefficient of variation of the interspike intervals. Furthermore, the linear correlation between DR and peristimulus time histogram (PSTH) based reflex amplitudes was inconsistent for both contraction forces. To obtain further insights into the influence of MN properties, we simulated reflexes in a heterogeneous MN population using electrical circuit models and varied MN inputs. The simulations indicate that, besides mean input current and membrane noise, MN properties also contribute to the variability of reflex amplitude estimates. The MN heterogeneity is well captured by PSF-based reflex estimates but not by PSTH-based ones. These results show that variations in amplitude estimates of individual MU reflexes are due to complex interactions between intrinsic and extrinsic factors. As PSF-based reflex amplitude estimates reflect the MN size distribution, tracking PSF-based reflex amplitudes at fixed MVC levels across individual subjects could serve as a marker for investigating spinal adaptations under (patho)physiological conditions. Author summaryMotor neurons are specialized nerve cells that control human movement. Each motor neuron activates a specific set of muscle fibers, and the functional unit consisting of a motor neuron and muscle fibers is called a motor unit. We can observe the activity of motor neurons in humans by decomposing the electrical activity of muscles (the electromyogram) into contributions from individual motor units. Reflex responses of motor units are often used to study the input-output relation of motor neurons in humans. We used a combination of experiments and computer simulations to study the factors that influence the reflex amplitude of motor units during an excitatory reflex. We found that the reflex amplitude is non-linearly influenced by a number of intrinsic and extrinsic factors, e. g., motor neuron size, but also the muscle force. Additionally, we found that these factors have different effects on the results of the two common methods used to calculate the reflex amplitude. These results provide guidance on choosing a suitable evaluation method and on interpreting reflex experiments.

Calibration of mechanistic cardiovascular models is a central barrier to their use in population analysis and patient-specific simulation, particularly in settings where key physiological variables are unobservable and multiple parameter combinations can reproduce the same haemodynamic targets. In this work, we present Embedded Gradient Descent (EGD), a calibration framework for ODE-based lumped-parameter cardiovascular models in which selected physiological parameters are promoted to dynamic states and driven toward prescribed targets through embedded controller equations. By exploiting the qualitative structure of the governing equations, EGD enforces physiologically consistent parameter-variable relationships, yielding unique calibrated solutions that are robust to initial conditions and scale efficiently with model complexity. The framework is demonstrated using a mechanistic cardiovascular model to generate virtual paediatric populations spanning normal physiology and two septic shock phenotypes (warm and cold shock), achieving low residual error across pressures, flows, and compartmental volumes. The resulting parameter distributions are consistent with known haemodynamic adaptations in paediatric sepsis, including alterations in vascular resistance, compliance, cardiac elastance, and effective blood volume. Importantly, persistent calibration residuals arise only when target combinations are structurally incompatible with the model, providing an explicit and interpretable diagnostic of feasibility limits rather than an optimisation failure. These results establish EGD as a general, scalable calibration strategy for mechanistic cardiovascular models and a practical foundation for virtual population generation and future patient-specific digital twin applications in critical care. NEW & NOTEWORTHYThis study introduces a novel, embedded gradient descent calibration framework that enables scalable generation of mechanistically interpretable virtual populations of patients from ODE-based cardiovascular models. By treating parameter inference as a dynamical extension of the governing equations and calibrating directly against cycle-derived physiological targets, the method preserves physiologically meaningful parameter-variable relationships. Applied to paediatric sepsis, the framework reproduces warm and cold shock phenotypes while exposing infeasible target combinations, while providing efficient calibration and physiological insight.

Recent computational studies of Parkinsons disease have yielded contradictory findings regarding the role of the subthalamic nucleus (STN) in pathological beta oscillations, with some models implicating STN as essential for beta generation and others suggesting that STN suppresses oscillations. This work addresses these discrepancies by systematically investigating how the specific features of the integrate-and-fire neurons used in these models influence simulated basal ganglia network dynamics. Using both rate models and spiking network simulations incorporating coupled subthalamopallidal and pallidostriatal circuits, we demonstrate that the choice between leaky integrate-and-fire (LIF) and quadratic integrate-and-fire (QIF) models to represent STN neurons fundamentally impacts the phase relationship between STN and external globus pallidus prototypical (Proto) neuron populations. QIF STN neurons establish in-phase coupling with Proto neurons, which enhances beta oscillation amplitude, while LIF STN neurons develop anti-phase relationships, which suppresses beta power. Through intervention experiments and parameter sweeps across physiologically relevant firing rates, we show that these phase-related effects persist robustly across network conditions, and we mathematically establish conditions under which these results are guaranteed to hold. Our findings reveal that the fundamental mathematical structure underlying spike generation, rather than other biophysical details, determines whether the subthalamopallidal loop acts as a beta amplifier or suppressor. This mechanistic insight reconciles contradictory findings in the literature, demonstrates that seemingly minor modeling choices can have profound consequences for understanding disease mechanisms and therapeutic targets, and offers predictions for determining which model framework reflects the biological reality. Author summarySubstantial work has explored the mechanisms underlying enhanced beta oscillations in the basal ganglia, motivated by their potential relevance to parkinsonian conditions and associated treatments. Often inferences about these mechanisms are based on simulations and reasoning that focus on features of network connectivity. We show that in fact the specific dynamical properties of the neurons in these circuits can strongly influence their emergent dynamics, with completely opposing effects arising in a given network structure depending on which neuron model is used, and we explain the factors underlying this divergence. Based on these factors, the determination of a small set of neuron properties in future biological experiments will lead to predictions about the mechanisms that can generate beta oscillations in the parkinsonian basal ganglia.

When walking, we generate rich acoustic information through our footsteps. This sound stream contains information about footfall timing, dragging, loading rate, etc. The role of this endogenous auditory feedback in gait control remains underexplored. Building on work in delayed auditory feedback (DAF) for speech and on theories of sensorimotor coupling, we investigated whether manipulating the delay and amplification of self-produced footstep sounds modulates gait dynamics. Thirty healthy young adults walked overground while receiving real-time lateralized playback of the sound close to their feet using shoe-mounted microphones, belt-worn micro-computer, and headphones, all connected with cables for minimal latency. Across conditions, auditory feedback was delivered with no delay, low delay (12.5% step duration), or high delay (25% step duration), and at either full or half amplification. There was also a masked hearing condition with pink noise. Spatiotemporal gait parameters, namely cadence, speed, stride length, and coefficient of variation were analyzed as percent change relative to baseline. We found that amplification without delay reduced variability by almost 10% on average, consistent with strengthened sensorimotor coupling via enhanced perceptual access to foot-ground interaction dynamics. A second interesting finding was that delay increased the cadence of walking instead of reducing it, contrary to our expectations. We discussed how this effect can be explained by both Bayesian predictive coding and anticipatory dynamic systems with delayed feedback. We developed a theoretical model with anticipatory dynamics, with implications for closed-loop gait rehabilitation tools.

Clavicle fractures often exhibit markedly different clinical outcomes: some patients recover acceptable function despite shortening or displacement, whereas others with apparently similar deformity develop persistent pain, functional loss, or poor healing. To explain this distinction, we propose a minimal nonlinear mechanical model for prognostic analysis of clavicle fractures. The model describes the interaction between fracture-related shortening and compensatory shoulder-girdle posture through a reduced equilibrium equation incorporating stiffness, geometric nonlinearity, and shortening-posture coupling. Within this framework, we analyze equilibrium branches, local stability, and the emergence of critical thresholds. We show that post-fracture destabilization can be interpreted as a fold bifurcation, while more complex parameter dependence gives rise to cusp-type structures and multistability. These bifurcation mechanisms provide a mathematical explanation for sudden deterioration after injury or treatment, as well as for strong inter-individual variability. We further introduce an optimization principle based on a utility functional to guide treatment planning. The analysis predicts that the optimal safe correction should lie strictly below the bifurcation threshold, thereby generating a natural safety margin. Although the model is simplified and has not yet been calibrated against patient data, it nevertheless provides a theoretical framework for understanding why fracture prognosis may deteriorate abruptly near critical mechanical conditions and offers a dynamical-systems interpretation of empirical treatment thresholds used in clinical practice.

Cerebral blood flow (CBF) control is essential for normal brain function and is disrupted in pathological conditions. Arterial diameters are tightly regulated to provide on demand increases in blood flow in regions of neuronal activity. Pericytes (PCs) exhibit robust myogenic tone and may also respond to neuronal activity to fine-tune local resistance and blood flow. Thus, mural control of microcirculatory resistance may extend beyond arteries and arterioles. Yet, PCs electrophysiology and contractility have not been thoroughly characterized, and this prohibits an integrated view of brain blood flow control. In this study, we develop a detailed mathematical model of mural cell electrophysiology, Ca2+ dynamics and biomechanics. The model is informed by electrophysiological data in smooth muscle cells (SMCs) or PCs and predictions are compared against pressure-induced responses in isolated arterioles and capillaries, respectively. Simulations recapitulate myogenic constrictions and examine differences in contractile dynamics as we move from arterioles to proximal and distal capillaries. In arteriole-to-capillary transitional (ACT) zone PCs, increased mechanosensitivity, more Ca2+ influx through non-selective cation (NSC) channels and/or a higher sensitivity of the contractile apparatus to Ca2+ can compensate for reduced L-type voltage-operated (VOCC) Ca2+ influx and allow for robust constrictions at the lower operating pressures of capillaries relative to the arterioles. A significant Ca2+ influx through NSC relative to VOCC, however, can decouple the PCs contractile apparatus from electrical signaling. Vasoactivity to chemomechanical stimuli along the arteriole to capillary axis is progressively driven by VOCC-independent Ca2+ influx and Ca2+ sensitization with slow kinetics. The proposed cell model can form the basis for detailed multiscale and multicellular models that will examine physiological function at a single vessel or vascular network levels and investigate CBF control in health and in disease. Key pointsO_LIA mural cell model of electrophysiology, calcium (Ca2+) dynamics and biomechanics is informed by data and adapted for modeling cerebral arteriole smooth muscle cells and capillary pericytes. C_LIO_LIIon channel activities are characterized by patch-clamp electrophysiology in isolated cerebral smooth muscle cell and pericytes, and capillary and arteriole electromechanical responses to transmural pressure changes are assessed using novel ex vivo preparations. C_LIO_LIMyogenic constrictions in arterioles can be reproduced by pressure-induced non-selective cation channel (NSC) activation that depolarizes the cell, opens L-type Ca2+ channels (VOCCs) and increases Ca2+ influx. C_LIO_LIRobust myogenic constrictions in arteriole-to-capillary transition (ACT) zone pericytes may reflect significant Ca2+ influx through NSC, increased mechanosensitivity, or higher sensitivity of the contractile apparatus to Ca2+, potentially compensating for reduced VOCC density relative to arteriolar smooth muscle. C_LIO_LIA significant contribution of NSC relative to VOCC in Ca2+ influx, can decouple the contractile apparatus from electrical signaling. C_LIO_LIThe model shows how gradients in ionic activities, mechanosensitivity and/or Ca2+ sensitivity can alter contractile phenotype and electromechanical coupling along the arteriole to capillary continuum. C_LIO_LIThe proposed model can form the basis for detailed multiscale and multicellular models that will investigate cerebral blood flow control in health and in disease. C_LI

Many existing models of computation in recurrent neural networks assume dense, unconstrained initial connectivity, where any pair of neurons may be coupled to generate the rich dynamics needed for learning complex temporal patterns. Inspired by invertebrate circuits that often exhibit ring-like connectivity, we show that computation can occur in ultra-sparse spiking and rate reservoirs that are initially coupled as simple unidirectional rings. In contrast to standard recurrent networks, the total number of network parameters in these ring networks scales only linearly with network size, while still producing rich feature sets. We demonstrate that such networks can successfully reproduce a range of dynamical systems tasks, including oscillations, multi-stable switches, and low-dimensional chaotic attractors. Our findings show that structured spatio-temporal dynamics naturally arising from large ring topologies, often observed in invertebrate circuits, are a sufficient mechanism for learning different types of attractors.

Human walking is often considered an inverted pendulum during single support, suggesting conservative dynamics. Gait consists of discrete steps connected by mechanically costly transitions. We examine how step length, walking speed, and work capacity jointly constrain walking mechanics. Using a powered simple walking model, minimum speed required to complete a step of given length is derived based on gravitational work; below this threshold, forward progression becomes mechanically infeasible, and the next heel-strike occurs early, producing shorter steps. Comparisons with empirical step length-speed relationships show that humans walk at higher speeds and require greater push-off work, indicating energy dissipation. We extend pendular dynamics by incorporating hip torque, a linearized axial force model, and muscle intervention. This framework reproduces key GRF features, including the M-shaped profile, without prescribing force trajectories a priori. Fitted parameters suggest reduced average loading (CBaseline < 1), active mid-stance unloading (Am < 0), and narrowly timed muscle action (small{sigma} m). Parameter studies show that increasing step length or speed increases transition work and peak forces, while hip torque timing indicates mechanical cost is minimized when energy modulation occurs after mid-stance. These findings indicate that preferred walking speed emerges from feasibility and work-capacity constraints, not energetic optimality alone.

In "Experiment-free exoskeleton assistance via learning in simulation", Luo et al. [1] present an ambitious framework for developing exoskeleton controllers through reinforcement learning exclusively in computer simulation. The authors report that a control policy trained on a small dataset from one subject was directly transferred to physical hardware, reducing human metabolic cost during walking, running, and stair climbing by more than any prior device. If confirmed, this would represent a major breakthrough for the field of wearable robotics and their clinical applications. However, a close examination of the published materials casts doubt on these claims. The reported experimental results violate physiological limits on the relationship between mechanical power and muscle energy use during gait2,3,4. The algorithmic claims are surprising and cannot be verified; in contrast with established replicability standards in machine learning5,6, executable code has not been made available. We conclude that the goals of this study have not yet been verifiably achieved and make recommendations for avoiding publication errors of this type in the future.