Sinaps: A Python library to simulate voltage dynamics and ionic electrodiffusion in neurons (original) (raw)
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A Software Tool for Modelling and Simulation of Ion Channels
Background: Ion channels are proteins in the cell membrane that facilitate the diffusion of selected ions through biological membranes. Measuring ionic current has been made possible using the giga-seal patchclamp technique [13]. Modelling and simulation provide a better understanding that complements experimental results. Several tools are available. New modelling standards based on markup languages are being developed in order to promote collaborative research and model sharing. This paper presents a webbased channel simulation tool that uses a database of models described by the NeuroML language [6]. The tool currently focuses on the simulation of voltagegated ion channels and can be used as a prototype for a more generalized, standard-based and Web accessible neuronal modelling and simulation engine. Results: The Ion Channel Simulation tool reads NeuroML models from either a database or a usersupplied file and simulates the forward and backward rates variations with voltage, the channel open probability and time constant variations with voltage, and the gate and channel conductance and channel current density variations with membrane voltage and time. Tests performed include the Na and K squid axon model by Hodgkin and Huxley [8], and the Ca3 hippocampal pyramidal neuron model by Traub et al. [14]. Conclusion: The Ion Channel Simulation tool is a prototype for a user-friendly neural simulation engine that promotes collaborative research and model sharing via a Web-based interface. Future research di
A New Computer Software for Simulation of Neurons
In this paper, a new computer software package 'Simulron' for simulation of neurons is introduced. Excitable membranes with voltage-gated ionic channels can be modeled by using the software, and current clamp and voltage clamp experiments can be simulated. The program allows user to determine the ionic channel count and set the rate functions of the channels. If the rate functions are not known, the program enables the user to set steady-state and time constant functions. First-order differential equations used to define dynamics of the gate and membrane potential are solved using forward Euler method of integration with variable time steps. Outputs of the simulations are shown on spreadsheet template allowing flexible data manipulation and can be graphically displayed.
A computer software for simulating single-compartmental model of neurons
Computer methods and programs in biomedicine, 2004
In this paper, a new computer software package, Yalzer, is introduced for simulating single-compartmental model of neurons. Passive or excitable membranes with voltage-gated ion channels can be modeled, and current clamp and voltage clamp experiments can be simulated. In the Yalzer, first-order differential equations used to define the dynamics of the gate variables and the membrane potential are solved by two separate integration methods with variable time steps: forward Euler and exponential Euler methods. Outputs of the simulation are shown on a spreadsheet template for allowing flexible data manipulation and can be graphically displayed. The user can define the model in detail, and examine the excitability of the model and the dynamics of voltage-gated ion channels. The software package addresses to ones who want to run simple simulations of neurons without need to any programming language skills or expensive software. It can also be used for educational purposes.
Modeling stochastic calcium dynamics in the dendritic spines: a hybrid algorithm
BMC Neuroscience, 2008
Welcome to CNS*2008! The international Computational Neuroscience meeting (CNS) has been a premier forum for presenting experimental and theoretical results exploring the biology of computation in the nervous system for the last 17 years. The meeting is organized by the Organization for Computational Neurosciences (OCNS), a non-profit organization governed by an international executive committee and board of directors. A separate program committee is responsible for the scientific program of the meeting. Participants at the meeting are from academia and industry. The meeting not only provides a venue for research presentation and discussion by senior scientists but actively offers a forum for promoting and supporting young scientists and students from around the world.
Computer simulations of neuron-glia interactions mediated by ion flux
Journal of Computational Neuroscience, 2008
Extracellular potassium concentration, [K + ] o , and intracellular calcium, [Ca 2+ ] i , rise during neuron excitation, seizures and spreading depression. Astrocytes probably restrain the rise of K + in a way that is only partly understood. To examine the effect of glial K + uptake, we used a model neuron equipped with Na + , K + , Ca 2+ and Cl − conductances, ion pumps and ion exchangers, surrounded by interstitial space and glia. The glial membrane was either "passive", incorporating only leak channels and an ion exchange pump, or it had rectifying K + channels. We computed ion fluxes, concentration changes and osmotic volume changes. Increase of [K + ] o stimulated the glial uptake by the glial 3Na/2K ion pump. The [K + ] o flux through glial leak and rectifier channels was outward as long as the driving potential was outwardly directed, but it turned inward when rising [K + ] o /[K + ] i ratio reversed the driving potential. Adjustments of glial membrane parameters influenced the neuronal firing patterns, the length of paroxysmal afterdischarge and the ignition point of spreading depression. We conclude that voltage gated K + currents can boost the effectiveness of the glial "potassium buffer" and that this buffer function is important even at moderate or low levels of excitation, but especially so in pathological states.
Mapping the function of neuronal ion channels in model and experiment
eLife, 2017
Ion channel models are the building blocks of computational neuron models. Their biological fidelity is therefore crucial for the interpretation of simulations. However, the number of published models, and the lack of standardization, make the comparison of ion channel models with one another and with experimental data difficult. Here, we present a framework for the automated large-scale classification of ion channel models. Using annotated metadata and responses to a set of voltage-clamp protocols, we assigned 2378 models of voltage- and calcium-gated ion channels coded inNEURONto 211 clusters. TheIonChannelGenealogy(ICGenealogy) web interface provides an interactive resource for the categorization of new and existing models and experimental recordings. It enables quantitative comparisons of simulated and/or measured ion channel kinetics, and facilitates field-wide standardization of experimentally-constrained modeling.
Journal of …, 2003
Conventionally, the parameters of neuronal models are hand-tuned using trial-and-error searches to produce a desired behavior. Here, we present an alternative approach. We have generated a database of ~1.7 million single-compartment model neurons by independently varying eight maximal membrane conductances based on measurements from lobster stomatogastric neurons. We classified the spontaneous electrical activity of each model neuron and its responsiveness to inputs during runtime with an adaptive algorithm and saved a reduced version of each neuron's activity pattern. Our analysis of the distribution of different activity types (silent, spiking, bursting, irregular) in the eight-dimensional conductance space indicates that the coarse grid of conductance values we chose is sufficient to capture the salient features of the distribution. The database can be searched for different combinations of neuron properties such as activity type, spike or burst freque ncy, resting potential, frequency-current relation, and phase response curve. We demonstrate how the database can be screened for models that reproduce the behavior of a specific biological neuron and show that the contents of the database can give insight into the way a neuron's membrane conductances determine its activity pattern and response properties. Similar databases can be constructed to explore parameter spaces in multicompartmental models or small networks, or to examine the effects of changes in the voltage-dependence of currents. In all cases, database searches can provide insight into how neuronal and network properties depend on the values of the parameters in the models.
Frontiers in Neuroinformatics, 2014
Electrical extracellular recordings, i.e., recordings of the electrical potentials in the extracellular medium between cells, have been a main work-horse in electrophysiology for almost a century. The high-frequency part of the signal (500 Hz), i.e., the multi-unit activity (MUA), contains information about the firing of action potentials in surrounding neurons, while the low-frequency part, the local field potential (LFP), contains information about how these neurons integrate synaptic inputs. As the recorded extracellular signals arise from multiple neural processes, their interpretation is typically ambiguous and difficult. Fortunately, a precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established: the extracellular potential can be calculated as a weighted sum of all transmembrane currents in all cells located in the vicinity of the electrode. This computational scheme can considerably aid the modeling and analysis of MUA and LFP signals. Here, we describe LFPy, an open source Python package for numerical simulations of extracellular potentials. LFPy consists of a set of easy-to-use classes for defining cells, synapses and recording electrodes as Python objects, implementing this biophysical modeling scheme. It runs on top of the widely used NEURON simulation environment, which allows for flexible usage of both new and existing cell models. Further, calculation of extracellular potentials using the line-source-method is efficiently implemented. We describe the theoretical framework underlying the extracellular potential calculations and illustrate by examples how LFPy can be used both for simulating LFPs, i.e., synaptic contributions from single cells as well a populations of cells, and MUAs, i.e., extracellular signatures of action potentials.
Modeling Calcium Dynamics in Dendritic Spines
SIAM Journal on Applied Mathematics, 2005
Dendritic spines are microstructures located on dendrites of neurons, where calcium can be compartmentalized. They are usually the postsynaptic parts of synapses and may contain anywhere from a few up to thousands of calcium ions at a time. Initiated by an action potential, a back-propagating action potential, or a synaptic stimulation, calcium ions enter spines and are known to bring about their fast contractions (twitching), which in turn affect calcium dynamics. In this paper, we propose a coarse-grained reaction-diffusion (RD) model of a Langevin simulation of calcium dynamics with twitching and relate the biochemical changes induced by calcium to structural changes occurring at the spine level. The RD equations model the contraction of proteins as chemical events and serve to describe how changes in spine structure affect calcium signaling. Calcium ions induce contraction of actin-myosin-type proteins and produce a flow of the cytoplasmic fluid in the direction of the dendritic shaft, thus speeding up the time course of calcium dynamics in the spine, relative to pure diffusion. Experimental and simulation results reveal two time periods in spine calcium dynamics. Simulations [D. Holcman, Z. Schuss, and E. Korkotian, Biophysical Journal, to appear] show that in the first period, calcium motion is mainly driven by the hydrodynamics, while in the second period it is diffusion. The coarse-grained RD model also gives this result, and the analysis reveals how the two time constants depend on spine geometry. The model's prediction, that there are not two time periods in the diffusion of inert molecules in the spine, has been verified experimentally.
Journal of Computational Neuroscience, 2009
The large number of variables involved in many biophysical models can conceal potentially simple dynamical mechanisms governing the properties of its solutions and the transitions between them as parameters are varied. To address this issue, we extend a novel model reduction method, based on “scales of dominance,” to multi-compartment models. We use this method to systematically reduce the dimension of a two-compartment conductance-based model of a crustacean pyloric dilator (PD) neuron that exhibits distinct modes of oscillation—tonic spiking, intermediate bursting and strong bursting. We divide trajectories into intervals dominated by a smaller number of variables, resulting in a locally reduced hybrid model whose dimension varies between two and six in different temporal regimes. The reduced model exhibits the same modes of oscillation as the 16 dimensional model over a comparable parameter range, and requires fewer ad hoc simplifications than a more traditional reduction to a single, globally valid model. The hybrid model highlights low-dimensional organizing structure in the dynamics of the PD neuron, and the dependence of its oscillations on parameters such as the maximal conductances of calcium currents. Our technique could be used to build hybrid low-dimensional models from any large multi-compartment conductance-based model in order to analyze the interactions between different modes of activity.