Next generation neural population models

Frontiers in Applied Mathematics and Statistics, Год журнала: 2023, Номер 9

Опубликована: Фев. 28, 2023

Low-dimensional neural mass models are often invoked to model the coarse-grained activity of large populations neurons and synapses have been used help understand coordination scale brain rhythms. However, they phenomenological in nature and, although motivated by neurobiological considerations, absence a direct link an underlying biophysical reality is weakness that means may not be best suited capturing some rich behaviors seen real neuronal tissue. In this perspective article I discuss simple spiking neuron network has recently shown admit exact mean-field description for synaptic interactions. This many features coupled additional dynamical equation describes evolution population synchrony. next generation ideally understanding patterns ubiquitously neuroimaging recordings. Here review equations, way which synchrony, firing rate, average voltage intertwined, together with their application modeling. As well as natural extensions new approach modeling dynamics open mathematical challenges developing statistical neurodynamics can generalize one discussed here.

Язык: Английский

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Population spiking and bursting in next-generation neural masses with spike-frequency adaptation

Physical review. E, Год журнала: 2023, Номер 107(2)

Опубликована: Фев. 21, 2023

Spike-frequency adaptation (SFA) is a fundamental neuronal mechanism taking into account the fatigue due to spike emissions and consequent reduction of firing activity. We have studied effect this on macroscopic dynamics excitatory inhibitory networks quadratic integrate-and-fire (QIF) neurons coupled via exponentially decaying post-synaptic potentials. In particular, we population activities by employing an exact mean-field reduction, which gives rise next-generation neural mass models. This low-dimensional allows for derivation bifurcation diagrams identification possible regimes emerging both in single two identically masses. populations SFA favors emergence bursts networks, while it hinders tonic spiking ones. The symmetric coupling masses, absence adaptation, leads solutions with broken symmetry, namely, chimera-like case antiphase spikes one. addition new collective dynamical exhibiting cross-frequency (CFC) among fast synaptic timescale slow one, ranging from slow-fast nested oscillations asymmetric bursting phenomena. analysis these CFC rhythms θ-γ range has revealed that increase θ frequency joined decrease γ analogous what been reported experimentally hippocampus olfactory cortex rodents under cholinergic modulation, known reduce SFA.

Язык: Английский

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Macroscopic dynamics of neural networks with heterogeneous spiking thresholds

Physical review. E, Год журнала: 2023, Номер 107(2)

Опубликована: Фев. 16, 2023

Mean-field theory links the physiological properties of individual neurons to emergent dynamics neural population activity. These models provide an essential tool for studying brain function at different scales; however, their application populations on large scale, they need account differences between distinct neuron types. The Izhikevich single model can a broad range types and spiking patterns, thus rendering it optimal candidate mean-field theoretic treatment in heterogeneous networks. Here we derive equations networks all-to-all coupled with thresholds. Using methods from bifurcation theory, examine conditions under which accurately predicts network. To this end, focus three important features that are subject here simplifying assumptions: (i) spike-frequency adaptation, (ii) spike reset conditions, (iii) distribution single-cell thresholds across neurons. Our results indicate that, while is not exact network dynamics, faithfully captures its dynamic regimes phase transitions. We present represent dynamics. comprises biophysical state variables parameters, incorporates realistic resetting accounts heterogeneity allow applicability as well direct comparison experimental data.

Язык: Английский

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A Mean Field to Capture Asynchronous Irregular Dynamics of Conductance-Based Networks of Adaptive Quadratic Integrate-and-Fire Neuron Models

Neural Computation, Год журнала: 2024, Номер 36(7), С. 1433 - 1448

Опубликована: Май 22, 2024

Mean-field models are a class of used in computational neuroscience to study the behavior large populations neurons. These based on idea representing activity number neurons as average mean-field variables. This abstraction allows large-scale neural dynamics computationally efficient and mathematically tractable manner. One these methods, semianalytical approach, has previously been applied different types single-neuron models, but never quadratic form. In this work, we adapted method integrate-and-fire neuron with adaptation conductance-based synaptic interactions. We validated model by comparing it spiking network model. should be useful interacting synapses.

Язык: Английский

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Pulse Shape and Voltage-Dependent Synchronization in Spiking Neuron Networks

Neural Computation, Год журнала: 2024, Номер 36(8), С. 1476 - 1540

Опубликована: Июль 19, 2024

Abstract Pulse-coupled spiking neural networks are a powerful tool to gain mechanistic insights into how neurons self-organize produce coherent collective behavior. These use simple neuron models, such as the θ-neuron or quadratic integrate-and-fire (QIF) neuron, that replicate essential features of real dynamics. Interactions between modeled with infinitely narrow pulses, spikes, rather than more complex dynamics synapses. To make these biologically plausible, it has been proposed they must also account for finite width which can have significant impact on network However, derivation and interpretation pulses contradictory, pulse shape is largely unexplored. Here, I take comprehensive approach coupling in QIF θ-neurons. argue activate voltage-dependent synaptic conductances show implement them their effect last through phase after spike. Using an exact low-dimensional description globally coupled neurons, prove instantaneous interactions oscillations emerge due effective mean voltage. analyze by means family smooth functions arbitrary symmetric asymmetric shapes. For resulting voltage not very synchronizing but slightly skewed spike readily generate oscillations. The results unveil synchronization mechanism at heart emergent behavior, facilitated complementary traditional transmission networks.

Язык: Английский

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The Virtual Parkinsonian patient

npj Systems Biology and Applications, Год журнала: 2025, Номер 11(1)

Опубликована: Апрель 26, 2025

This study investigates the influence of pharmacological nigrostriatal dopaminergic stimulation on entire brain by analyzing EEG and deep electrodes, placed near subthalamic nuclei, from 10 Parkinsonian patients before (OFF) after (ON) L-Dopa administration. We characterize large-scale dynamics as spatio-temporal spreading aperiodic bursts. then simulate effects utilizing a novel neural-mass model that includes local dopamine concentration. Whole-brain are simulated for different tones, generating predictions expected dynamics, to be compared with empirical electrode data. To this end, we invert infer most likely tone data, correctly identifying higher Dopaminergic in ON-state, lower OFF-state, each patient. In conclusion, successfully integrating anatomical functional knowledge into physiological predictions, using solid ground truth validate our findings.

Язык: Английский

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Mechanisms for dysregulation of excitatory-inhibitory balance underlying allodynia in dorsal horn neural subcircuits

PLoS Computational Biology, Год журнала: 2025, Номер 21(1), С. e1012234 - e1012234

Опубликована: Янв. 14, 2025

Chronic pain is a wide-spread condition that debilitating and expensive to manage, costing the United States alone around $600 billion in 2010. In common symptom of chronic called allodynia, non-painful stimuli produce painful responses with highly variable presentations across individuals. While specific mechanisms remain unclear, allodynia hypothesized be caused by dysregulation excitatory-inhibitory (E-I) balance pain-processing neural circuitry dorsal horn spinal cord. this work, we analyze biophysically-motivated subcircuit structures represent motifs circuits laminae I-II horn. These are part pathways mediate two different types allodynia: static dynamic. We use firing rate models describe activity populations excitatory inhibitory interneurons within each subcircuit. By accounting for experimentally-observed under healthy conditions, specify model parameters defining subcircuits yield typical behavior normal conditions. Then, implement sensitivity analysis approach identify most likely cause allodynia-producing subcircuit’s E-I signaling. find disruption generally occurs either due downregulation signaling so neurons “released” from control, or upregulation neuron “escape” their control. Which these occur, components involved mechanism, proportion exhibiting mechanism can vary depending on structure. results suggest hypotheses about diverse may responsible thus offering predictions high interindividual variability observed identifying targets further experimental studies underlying symptom.

Язык: Английский

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Low-dimensional model for adaptive networks of spiking neurons

Physical review. E, Год журнала: 2025, Номер 111(1)

Опубликована: Янв. 24, 2025

We investigate a large ensemble of quadratic integrate-and-fire neurons with heterogeneous input currents and adaptation variables. Our analysis reveals that, for specific class adaptation, termed spike-frequency the high-dimensional system can be exactly reduced to low-dimensional ordinary differential equations, which describes dynamics three mean-field variables: population's firing rate, mean membrane potential, variable. The resulting rate equations (FREs) uncover key generic feature networks adaptation: Both center width distribution neurons' frequencies are reduced, this largely promotes emergence collective synchronization in network. findings further supported by bifurcation FREs, accurately captures spiking neuron network, including phenomena such as oscillations, bursting, macroscopic chaos.

Язык: Английский

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Biophysically inspired mean-field model of neuronal populations driven by ion exchange mechanisms

Опубликована: Фев. 3, 2025

Whole-brain simulations are a valuable tool for gaining insight into the multiscale processes that regulate brain activity. Due to complexity of brain, it is impractical include all microscopic details in simulation. Hence, researchers often simulate as network coupled neural masses, each described by mean-field model. These models capture essential features neuronal populations while approximating most biophysical details. However, may be important certain parameters significantly impact function. The concentration ions extracellular space one key factor consider, its fluctuations can associated with healthy and pathological states. In this paper, we develop new model population Hodgkin–Huxley-type neurons, retaining perspective on ion-exchange mechanisms driving This allows us maintain interpretability bridging gap between micro- macro-scale mechanisms. Our able reproduce wide range activity patterns, also observed large simulations. Specifically, slow-changing ion concentrations modulate fast neuroelectric activity, feature our validated through vitro experiments. By studying how changes ionic conditions affect whole-brain dynamics, serves foundation measure biomarkers provide potential therapeutic targets cases dysfunctions like epilepsy.

Язык: Английский

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Biophysically inspired mean-field model of neuronal populations driven by ion exchange mechanisms

Опубликована: Фев. 3, 2025

Whole-brain simulations are a valuable tool for gaining insight into the multiscale processes that regulate brain activity. Due to complexity of brain, it is impractical include all microscopic details in simulation. Hence, researchers often simulate as network coupled neural masses, each described by mean-field model. These models capture essential features neuronal populations while approximating most biophysical details. However, may be important certain parameters significantly impact function. The concentration ions extracellular space one key factor consider, its fluctuations can associated with healthy and pathological states. In this paper, we develop new model population Hodgkin–Huxley-type neurons, retaining perspective on ion-exchange mechanisms driving This allows us maintain interpretability bridging gap between micro- macro-scale mechanisms. Our able reproduce wide range activity patterns, also observed large simulations. Specifically, slow-changing ion concentrations modulate fast neuroelectric activity, feature our validated through vitro experiments. By studying how changes ionic conditions affect whole-brain dynamics, serves foundation measure biomarkers provide potential therapeutic targets cases dysfunctions like epilepsy.

Язык: Английский

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