Models that contain the Model Concept : Extracellular Fields

(These models have extracellular field interactions, stimulations, or recordings within the simulations.)
Re-display model names without descriptions
    Models   Description
1.  A detailed and fast model of extracellular recordings (Camunas-Mesa & Qurioga 2013)
"We present a novel method to generate realistic simulations of extracellular recordings. The simulations were obtained by superimposing the activity of neurons placed randomly in a cube of brain tissue. Detailed models of individual neurons were used to reproduce the extracellular action potentials of close-by neurons. ..."
2.  A model for focal seizure onset, propagation, evolution, and progression (Liou et al 2020)
We developed a neural network model that can account for major elements common to human focal seizures. These include the tonic-clonic transition, slow advance of clinical semiology and corresponding seizure territory expansion, widespread EEG synchronization, and slowing of the ictal rhythm as the seizure approaches termination. These were reproduced by incorporating usage-dependent exhaustion of inhibition in an adaptive neural network that receives global feedback inhibition in addition to local recurrent projections. Our model proposes mechanisms that may underline common EEG seizure onset patterns and status epilepticus, and postulates a role for synaptic plasticity in the emergence of epileptic foci. Complex patterns of seizure activity and bi- stable seizure end-points arise when stochastic noise is included. With the rapid advancement of clinical and experimental tools, we believe that this model can provide a roadmap and potentially an in silico testbed for future explorations of seizure mechanisms and clinical therapies.
3.  A model of local field potentials generated by medial superior olive neurons (Goldwyn et al 2014)
A computational model of local field potentials generated by medial superior olive neurons. These field potentials are known as the "auditory neurophonic". MSO neuron is modeled as a soma and two dendrites (following Mathews et al, Nature Neurosci, 2010). Intracellular and a 1D extracellular domain are dynamically coupled and solved to simulate spatial-temporal patterns of membrane voltage and extracellular voltage in response to trains of synaptic inputs (monolateral or bilateral, excitation and/or inhibition). The model produces spatio-temporal patterns similar to neurophonic responses recorded in vivo, as discussed in the accompanying manuscript.
4.  Analyzing neural time series data theory and practice (Cohen 2014)
"This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals."
5.  Biophysical model for field potentials of networks of I&F neurons (beim Graben & Serafim 2013)
"... Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. ... Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. ..."
6.  CA1 pyramidal neuron: calculation of MRI signals (Cassara et al. 2008)
NEURON mod files from the paper: Cassarà AM, Hagberg GE, Bianciardi M, Migliore M, Maraviglia B. Realistic simulations of neuronal activity: A contribution to the debate on direct detection of neuronal currents by MRI. Neuroimage. 39:87-106 (2008). In this paper, we use a detailed calculation of the magnetic field produced by the neuronal currents propagating over a hippocampal CA1 pyramidal neuron placed inside a cubic MR voxel of length 1.2 mm to estimate the Magnetic Resonance signal.
7.  CA1 pyramidal neurons: effect of external electric field from power lines (Cavarretta et al. 2014)
The paper discusses the effects induced by an electric field at power lines frequency.
8.  Carbon nanotubes as electrical interfaces to neurons (Giugliano et al. 2008)
In the present NEURON model, we explore simple phenomenological models of the extracellular coupling, occurring at the neuron-metal microelectrode junction and (possibly) at the neuron-carbon nanotube junction.
9.  Contribution of the axon initial segment to APs recorded extracellularly (Telenczuk et al 2018)
"... It was recently proposed that at onset of an (Action Potential) AP the soma and the (Axon Initial Segment) AIS form a dipole. We study the extracellular signature (the extracellular action potential, EAP) generated by such a dipole. First, we demonstrate the formation of the dipole and its extracellular signature in detailed morphological models of a reconstructed pyramidal neuron. Then, we study the EAP waveform and its spatial dependence in models with axonal AP initiation and contrast it with the EAP obtained in models with somatic AP initiation. We show that in the models with axonal AP initiation the dipole forms between somatodendritic compartments and the AIS, and not between soma and dendrites as in the classical models. ..."
10.  Cortical Basal Ganglia Network Model during Closed-loop DBS (Fleming et al 2020)
We developed a computational model of the cortical basal ganglia network to investigate closed-loop control of deep brain stimulation (DBS) for Parkinson’s disease (PD). The cortical basal ganglia network model incorporates the (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode and (iv) temporal variation of network beta-band activity within the thalamo-cortico-basal ganglia loop. The model facilitates investigation of clinically-viable closed-loop DBS control approaches, modulating either DBS amplitude or frequency, using an LFP derived measure of network beta-activity.
11.  Deconstruction of cortical evoked potentials generated by subthalamic DBS (Kumaravelu et al 2018)
"... High frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) suppresses parkinsonian motor symptoms and modulates cortical activity. ... Cortical evoked potentials (cEP) generated by STN DBS reflect the response of cortex to subcortical stimulation, and the goal was to determine the neural origin of cEP using a two-step approach. First, we recorded cEP over ipsilateral primary motor cortex during different frequencies of STN DBS in awake healthy and unilateral 6-OHDA lesioned parkinsonian rats. Second, we used a biophysically-based model of the thalamocortical network to deconstruct the neural origin of the cEP. The in vivo cEP included short (R1), intermediate (R2) and long-latency (R3) responses. Model-based cortical responses to simulated STN DBS matched remarkably well the in vivo responses. R1 was generated by antidromic activation of layer 5 pyramidal neurons, while recurrent activation of layer 5 pyramidal neurons via excitatory axon collaterals reproduced R2. R3 was generated by polysynaptic activation of layer 2/3 pyramidal neurons via the cortico-thalamic-cortical pathway. Antidromic activation of the hyperdirect pathway and subsequent intracortical and cortico-thalamo-cortical synaptic interactions were sufficient to generate cEP by STN DBS, and orthodromic activation through basal ganglia-thalamus-cortex pathways was not required. These results demonstrate the utility of cEP to determine the neural elements activated by STN DBS that might modulate cortical activity and contribute to the suppression of parkinsonian symptoms."
12.  Determinants of the intracellular and extracellular waveforms in DA neurons (Lopez-Jury et al 2018)
To systematically address the contribution of AIS, dendritic and somatic compartments to shaping the two-component action potentials (APs), we modeled APs of male mouse and rat dopaminergic neurons. A parsimonious two-domain model, with high (AIS) and lower (dendro-somatic) Na+ conductance, reproduced the notch in the temporal derivatives, but not in the extracellular APs, regardless of morphology. The notch was only revealed when somatic active currents were reduced, constraining the model to three domains. Thus, an initial AIS spike is followed by an actively generated spike by the axon-bearing dendrite (ABD), in turn followed mostly passively by the soma. Larger AISs and thinner ABD (but not soma-to-AIS distance) accentuate the AIS component.
13.  Double cable myelinated axon (Layer 5 pyramidal neuron; Cohen et al 2020)
The periaxonal space in myelinated axons is conductive (~50 ohm cm). Together with a rapidly charging myelin sheath and relatively sealed paranodes, periaxonal conduction shapes the saltating voltage profiles of transaxonal (Vm), transmyelin (Vmy) and transfibre (Vmym) potentials. This model exemplifies double cable saltatory conduction across both time and space, and is the same cell (#6) as seen in Movie S4 of Cohen et al. 2020. This model version allows one to visualize and manipulate the controlling parameters of a propagating action potential. Further notes: The corresponding potentials in NEURON to those named above are v, vext (or vext[0]) and v+vext, respectively. The loaded biophysical parameters were those optimized for this cell (Cohen et al. 2020).
14.  Effect of ionic diffusion on extracellular potentials (Halnes et al 2016)
"Recorded potentials in the extracellular space (ECS) of the brain is a standard measure of population activity in neural tissue. Computational models that simulate the relationship between the ECS potential and its underlying neurophysiological processes are commonly used in the interpretation of such measurements. Standard methods, such as volume-conductor theory and current-source density theory, assume that diffusion has a negligible effect on the ECS potential, at least in the range of frequencies picked up by most recording systems. This assumption remains to be verified. We here present a hybrid simulation framework that accounts for diffusive effects on the ECS potential. ..."
15.  Effects of electric fields on cognitive functions (Migliore et al 2016)
The paper discusses the effects induced by an electric field at power lines frequency on neuronal activity during cognitive processes.
16.  Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010)
"Selective optogenetic drive of fast-spiking (FS) interneurons (INs) leads to enhanced local field potential (LFP) power across the traditional “gamma” frequency band (20–80 Hz; Cardin et al., 2009). In contrast, drive to regular-spiking (RS) pyramidal cells enhances power at lower frequencies, with a peak at 8 Hz. The first result is consistent with previous computational studies emphasizing the role of FS and the time constant of GABAA synaptic inhibition in gamma rhythmicity. However, the same theoretical models do not typically predict low-frequency LFP enhancement with RS drive. To develop hypotheses as to how the same network can support these contrasting behaviors, we constructed a biophysically principled network model of primary somatosensory neocortex containing FS, RS, and low-threshold spiking (LTS) INs. ..."
17.  Ephaptic coupling in passive cable and MSO neuron models (Goldwyn & Rinzel 2016)
Simulation code to explore how the synchronous activity of a bundle of neurons generates extracellular voltage, and how this extracellular voltage influences the membrane potential of "nearby" neurons. A non-synaptic mechanism known as ephaptic coupling. A model of a passive cable population (including user-friendly matlab GUI) and a model of medial superior olive neurons are included.
18.  Ephaptic interactions in olfactory nerve (Bokil et al 2001)
Bokil, H., Laaris, N., Blinder, K., Ennis, M., and Keller, A. (2001) Ephaptic interactions in the mammalian olfactory system. J. Neurosci. 21:RC173(1-5)
19.  Extracellular Action Potential Simulations (Gold et al 2007)
This package recreates the the principal experiments described in (Gold, Henze and Koch, 2007) and includes the core code necessary to create your own Extracellular Action Potential Simulations.
20.  Extracellular fields for a three-dimensional network of cells using NEURON (Appukuttan et al 2017)
" ... In the present work, we demonstrate a technique to couple the extracellular fields of individual cells within the NEURON simulation environment. The existing features of the simulator are extended by explicitly defining current balance equations, resulting in the coupling of the extracellular fields of adjacent cells. ..."
21.  Extracellular stimulation of myelinated axon (Reilly 2016)
This is an implementation of an established "electrostimulation model" subjected to a set of stimulation protocols. Such models and protocols are used to predict the response of neural tissue to stimulation by electromagnetic fields or direct application of extracellular current in order to "evaluate the efficacy and safety of medical devices, or to develop guidelines or standards on acceptible incidental exposure that may not be related to patient exposure for medical purposes."
22.  GPi/GPe neuron models (Johnson and McIntyre 2008)
Model files for two types of non-human primate neurons used in the paper: simplified versions of 1) a GPi neuron and 2) a GPe axon collateralizing in GPi en route to STN.
23.  Impedance spectrum in cortical tissue: implications for LFP signal propagation (Miceli et al. 2017)
" ... Here, we performed a detailed investigation of the frequency dependence of the conductivity within cortical tissue at microscopic distances using small current amplitudes within the typical (neuro)physiological micrometer and sub-nanoampere range. We investigated the propagation of LFPs, induced by extracellular electrical current injections via patch-pipettes, in acute rat brain slice preparations containing the somatosensory cortex in vitro using multielectrode arrays. Based on our data, we determined the cortical tissue conductivity over a 100-fold increase in signal frequency (5-500 Hz). Our results imply at most very weak frequency-dependent effects within the frequency range of physiological LFPs. Using biophysical modeling, we estimated the impact of different putative impedance spectra. Our results indicate that frequency dependencies of the order measured here and in most other studies have negligible impact on the typical analysis and modeling of LFP signals from extracellular brain recordings."
24.  Kernel method to calculate LFPs from networks of point neurons (Telenczuk et al 2020)
"The local field potential (LFP) is usually calculated from current sources arising from transmembrane currents, in particular in asymmetric cellular morphologies such as pyramidal neurons. Here, we adopt a different point of view and relate the spiking of neurons to the LFP through efferent synaptic connections and provide a method to calculate LFPs. We show that the so-called unitary LFPs (uLFP) provide the key to such a calculation. We show experimental measurements and simulations of uLFPs in neocortex and hippocampus, for both excitatory and inhibitory neurons. We fit a “kernel” function to measurements of uLFPs, and we estimate its spatial and temporal spread by using simulations of morphologically detailed reconstructions of hippocampal pyramidal neurons. Assuming that LFPs are the sum of uLFPs generated by every neuron in the network, the LFP generated by excitatory and inhibitory neurons can be calculated by convolving the trains of action potentials with the kernels estimated from uLFPs. This provides a method to calculate the LFP from networks of spiking neurons, even for point neurons for which the LFP is not easily defined. We show examples of LFPs calculated from networks of point neurons."
25.  Large scale neocortical model for PGENESIS (Crone et al 2019)
This is model code for a large scale neocortical model based on Traub et al. (2005), modified to run on PGENESIS on supercomputing resources. "In this paper (Crone et al 2019), we evaluate the computational performance of the GEneral NEural SImulation System (GENESIS) for large scale simulations of neural networks. While many benchmark studies have been performed for large scale simulations with leaky integrate-and-fire neurons or neuronal models with only a few compartments, this work focuses on higher fidelity neuronal models represented by 50–74 compartments per neuron. ..."
26.  LFP in striatum (Tanaka & Nakamura 2019)
The numerical simulations of LFP generation by cortical pyramidal neuron and medium-sized spiny neurons.
27.  LFP signature of monosynaptic thalamocortical connection (Hagen et al 2017)
"A resurgence has taken place in recent years in the use of the extracellularly recorded local field potential (LFP) to investigate neural network activity. To probe monosynaptic thalamic activation of cortical postsynaptic target cells, so called spike-trigger-averaged LFP (stLFP) signatures have been measured. In these experiments, the cortical LFP is measured by multielectrodes covering several cortical lamina and averaged on spontaneous spikes of thalamocortical (TC) cells. Using a well established forward-modeling scheme, we investigated the biophysical origin of this stLFP signature with simultaneous synaptic activation of cortical layer-4 neurons, mimicking the effect of a single afferent spike from a single TC neuron. ..."
28.  MATLAB for brain and cognitive scientists (Cohen 2017)
" ... MATLAB for Brain and Cognitive Scientists takes readers from beginning to intermediate and advanced levels of MATLAB programming, helping them gain real expertise in applications that they will use in their work. The book offers a mix of instructive text and rigorous explanations of MATLAB code along with programming tips and tricks. The goal is to teach the reader how to program data analyses in neuroscience and psychology. Readers will learn not only how to but also how not to program, with examples of bad code that they are invited to correct or improve. Chapters end with exercises that test and develop the skills taught in each chapter. Interviews with neuroscientists and cognitive scientists who have made significant contributions to their field using MATLAB appear throughout the book. ..."
29.  Measuring neuronal identification quality in ensemble recordings (isoitools) (Neymotin et al. 2011)
"... Here we describe information theoretic measures of action potential waveform isolation applicable to any dataset, that have an intuitive, universal interpretation, and that are not dependent on the methods or choice of parameters for single unit isolation, and that have been validated using a dataset."
30.  Modeling conductivity profiles in the deep neocortical pyramidal neuron (Wang K et al. 2013)
"With the rapid increase in the number of technologies aimed at observing electric activity inside the brain, scientists have felt the urge to create proper links between intracellular- and extracellular-based experimental approaches. Biophysical models at both physical scales have been formalized under assumptions that impede the creation of such links. In this work, we address this issue by proposing amulticompartment model that allows the introduction of complex extracellular and intracellular resistivity profiles. This model accounts for the geometrical and electrotonic properties of any type of neuron through the combination of four devices: the integrator, the propagator, the 3D connector, and the collector. ..."
31.  Modeling extracellular electrical stimulation (Tahayori et al. 2012)
"The validity of approximate equations describing the membrane potential under extracellular electrical stimulation (Meffin et al 2012 J. Neural Eng. 9 065005) is investigated through finite element analysis in this paper. To this end, the finite element method is used to simulate a cylindrical neurite under extracellular stimulation. Laplace's equations with appropriate boundary conditions are solved numerically in three dimensions and the results are compared to the approximate analytic solutions. ..."
32.  Modeling local field potentials (Bedard et al. 2004)
This demo simulates a model of local field potentials (LFP) with variable resistivity. This model reproduces the low-pass frequency filtering properties of extracellular potentials. The model considers inhomogeneous spatial profiles of conductivity and permittivity, which result from the multiple media (fluids, membranes, vessels, ...) composing the extracellular space around neurons. Including non-constant profiles of conductivity enables the model to display frequency filtering properties, ie slow events such as EPSPs/IPSPs are less attenuated than fast events such as action potentials. The demo simulates Fig 6 of the paper.
33.  Modeling single neuron LFPs and extracellular potentials with LFPsim (Parasuram et al. 2016)
LFPsim - Simulation scripts to compute Local Field Potentials (LFP) from cable compartmental models of neurons and networks implemented in the NEURON simulation environment.
34.  Modelling large scale electrodiffusion near morphologically detailed neurons (Solbra et al 2018)
" ... Here, we present the 3-D Kirchhoff-Nernst-Planck (KNP) framework, tailored to explore electrodiffusive effects on large spatiotemporal scales. By assuming electroneutrality, the KNP-framework circumvents charge-relaxation processes on the spatiotemporal scales of nanometers and nanoseconds, and makes it feasible to run simulations on the spatiotemporal scales of millimeters and seconds on a standard desktop computer. In the present work, we use the 3-D KNP framework to simulate the dynamics of ion concentrations and the electrical potential surrounding a morphologically detailed pyramidal cell. ..."
35.  Non-Weak E-Fields Pyramidal Neurons (Reznik et. al.,2015)
Effect of Polarization Induced by Non-Weak Electric Fields on the Excitability of Elongated Neurons With Active Dendrite. In response to polarization, the active currents in the dendrites of pyramidal neurons play a pivotal role in the excitability of elongated neurons. Depending on a number of parameters either hyperpolarizing or depolarizing currents in the dendrite dominate as polarization is increased. Furthermore, the impact that these active dendrite channels (Ca, KAHP, etc) occur when only a small fraction of their channels are open.
36.  Olfactory Bulb mitral-granule network generates beta oscillations (Osinski & Kay 2016)
This model of the dendrodendritic mitral-granule synaptic network generates gamma and beta oscillations as a function of the granule cell excitability, which is represented by the granule cell resting membrane potential.
37.  Phase-locking analysis with transcranial magneto-acoustical stimulation (Yuan et al 2017)
"Transcranial magneto-acoustical stimulation (TMAS) uses ultrasonic waves and a static magnetic field to generate electric current in nerve tissues for the purpose of modulating neuronal activities. It has the advantage of high spatial resolution and penetration depth. Neuronal firing rhythms carry and transmit nerve information in neural systems. In this study, we investigated the phase-locking characteristics of neuronal firing rhythms with TMAS based on the Hodgkin-Huxley neuron model. The simulation results indicate that the modulation frequency of ultrasound can affect the phase-locking behaviors. The results of this study may help us to explain the potential firing mechanism of TMAS."
38.  Reconstructing cerebellar granule layer evoked LFP using convolution (ReConv) (Diwakar et al. 2011)
The model allows reconstruction of evoked local field potentials as seen in the cerebellar granular layer. The approach uses a detailed model of cerebellar granule neuron to generate data traces and then uses a "ReConv" or jittered repetitive convolution technique to reproduce post-synaptic local field potentials in the granular layer. The algorithm was used to generate both in vitro and in vivo evoked LFP and reflected the changes seen during LTP and LTD, when such changes were induced in the underlying neurons by modulating release probability of synapses and sodium channel regulated intrinsic excitability of the cells.
39.  Simulations of oscillations in piriform cortex (Wilson & Bower 1992)
"1. A large-scale computer model of the piriform cortex was constructed on the basis of the known anatomic and physiological organization of this region. 2. The oscillatory field potential and electroencephalographic (EEG) activity generated by the model was compared with actual physiological results. The model was able to produce patterns of activity similar to those recorded physiologically in response to both weak and strong electrical shocks to the afferent input. The model also generated activity patterns similar to EEGs recorded in behaving animals. 3. ..."
40.  Spinal Motor Neuron (McIntyre et al 2002)
Simulation of peripheral nervous system (PNS) mylelinated axon. This model is described in detail in: McIntyre CC, Richardson AG, and Grill WM.(2002)
41.  Subiculum network model with dynamic chloride/potassium homeostasis (Buchin et al 2016)
This is the code implementing the single neuron and spiking neural network dynamics. The network has the dynamic ion concentrations of extracellular potassium and intracellular chloride. The code contains multiple parameter variations to study various mechanisms of the neural excitability in the context of chloride homeostasis.
42.  Theoretical reconstrucion of field potentials and dendrodendritic synaptic...(Rall & Shepherd 1968)
This was the first application of compartmental modeling using the Rall approach to brain neurons. It combined multicompartmental representation of a mitral cell and a granule cell with the first Hodgkin-Huxley-like action potential to model antidromic activation of the mitral cell, followed by synaptic excitation of the granule cell and synaptic inhibition of the mitral cell. Combined with reconstruction of the field potentials generated around these neurons, and detailed comparisons with single cell recordings, it led to prediction of dendrodendritic interactions mediating self and lateral inhibition of the mitral cells by the granule cells. It has been regarded as the first computational model of a brain microcircuit (see also Shepherd and Brayton, 1979). Recreation of the model is pending.

Re-display model names without descriptions