|
|
(Electrical synapses between cells)
| Models | Description |
| A Model of Multiple Spike Initiation Zones in the Leech C-interneuron (Crisp 2009) | |
| The leech C-interneuron and its electrical synapse with the S-interneuron exhibit unusual properties: an asymmetric delay when impulses travel from one soma to the other, and graded C-interneuron impulse amplitudes under elevated divalent cation concentrations. These properties have been simulated using a SNNAP model in which the C-interneuron has multiple, independent spike initiation zones associated with individual electrical junctions with the C-interneuron. | |
| A network model of the vertebrate retina (Publio et al. 2009) | |
| In this work, we use a minimal conductance-based model of the ON rod pathways in the vertebrate retina to study the effects of electrical synaptic coupling via gap junctions among rods and among AII amacrine cells on the dynamic range of the retina. The model is also used to study the effects of the maximum conductance of rod hyperpolarization activated current Ih on the dynamic range of the retina, allowing a study of the interrelations between this intrinsic membrane parameter with those two retina connectivity characteristics. | |
| A single column thalamocortical network model (Traub et al 2005) | |
| To better understand population phenomena in thalamocortical neuronal ensembles, we have constructed a preliminary network model with 3,560 multicompartment neurons (containing soma, branching dendrites, and a portion of axon). Types of neurons included superficial pyramids (with regular spiking [RS] and fast rhythmic bursting [FRB] firing behaviors); RS spiny stellates; fast spiking (FS) interneurons, with basket-type and axoaxonic types of connectivity, and located in superficial and deep cortical layers; low threshold spiking (LTS) interneurons, that contacted principal cell dendrites; deep pyramids, that could have RS or intrinsic bursting (IB) firing behaviors, and endowed either with non-tufted apical dendrites or with long tufted apical dendrites; thalamocortical relay (TCR) cells; and nucleus reticularis (nRT) cells. To the extent possible, both electrophysiology and synaptic connectivity were based on published data, although many arbitrary choices were necessary. | |
| Arteriolar networks: Spread of potential (Crane et al 2001) | |
| Crane, G.J., Hines, M.L., and Neild, T.O. (2001) Simulating the spread of membrane potential changes in arteriolar networks. Microcirculation 8:33-43. This model uses a gap junction density mechanism to couple arteriolar smooth muscle and endothelium in microvascular trees. | |
| Axonal gap junctions produce fast oscillations in cerebellar Purkinje cells (Traub et al. 2008) | |
| Examines how electrical coupling between proximal axons produces fast oscillations in cerebellar Purkinje cells. Traub RD, Middleton SJ, Knopfel T, Whittington MA (2008) Model of very fast (>75 Hz) network oscillations generated by electrical coupling between the proximal axons of cerebellar Purkinje cells. European Journal of Neuroscience in press. | |
| Boundary effects influence velocity in transverse propagation of cardiac APs (Sperelakis et al 2005) | |
| ... earlier experiments were carried out with 2-dimensional sheets of cells: 2 × 3, 3 × 4, and 5 × 5 models (where the first number is the number of parallel chains and the second is the number of cells in each chain). The purpose of the present study was to enlarge the model size to 7 × 7, thus enabling the transverse velocities to be compared in models of different sizes (where all circuit parameters are identical in all models). This procedure should enable the significance of the role of edge (boundary) effects in transverse propagation to be determined. See paper for more and details. | |
| CA1 oriens alveus interneurons: signaling properties (Minneci et al. 2007) | |
| The model supports the experimental findings showing that the dynamic interaction between cells with various firing patterns could differently affect GABAergic signaling, leading to a wide range of interneuronal communication within the hippocampal network. | |
| CA1 pyramidal cell: reconstructed axonal arbor and failures at weak gap junctions (Vladimirov 2011) | |
| Model of pyramidal CA1 cells connected by gap junctions in their axons. Cell geometry is based on anatomical reconstruction of rat CA1 cell (NeuroMorpho.Org ID: NMO_00927) with long axonal arbor. Model init_2cells.hoc shows failures of second spike propagation in a spike doublet, depending on conductance of an axonal gap junction. Model init_ring.hoc shows that spike failure result in reentrant oscillations of a spike in a loop of axons connected by gap junctions, where one gap junction is weak. The paper shows that in random networks of axons connected by gap junctions, oscillations are driven by single pacemaker loop of axons. The shortest loop, around which a spike can travel, is the most likely pacemaker. This principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We propose that this type of oscillations corresponds to so-called fast ripples in epileptic hippocampus. | |
| Cerebellar cortex oscil. robustness from Golgi cell gap jncs (Simoes de Souza and De Schutter 2011) | |
| " ... Previous one-dimensional network modeling of the cerebellar granular layer has been successfully linked with a range of cerebellar cortex oscillations observed in vivo. However, the recent discovery of gap junctions between Golgi cells (GoCs), which may cause oscillations by themselves, has raised the question of how gap-junction coupling affects GoC and granular-layer oscillations. To investigate this question, we developed a novel two-dimensional computational model of the GoC-granule cell (GC) circuit with and without gap junctions between GoCs. ..." | |
| Competing oscillator 5-cell circuit and Parameterscape plotting (Gutierrez et al. 2013) | |
| Our 5-cell model consists of competing fast and slow oscillators connected to a hub neuron with electrical and inhibitory synapses. Motivated by the Stomatogastric Ganglion (STG) circuit in the crab, we explored the patterns of coordination in the network as a function of the electrical coupling and inhibitory synapse strengths with the help of a novel visualization method that we call the "Parameterscape." The code submitted here will allow you to run circuit simulations and to produce a Parameterscape with the results. | |
| Effects of Acetyl-L-carnitine on neural transmission (Lombardo et al 2004) | |
| Acetyl-L-carnitine is known to improve many aspects of the neural activity even if its exact role in neurotransmission is still unknown. This study investigates the effects of acetyl-L-carnitine in T segmental sensory neurons of the leech Hirudo medicinalis. These neurons are involved in some forms of neural plasticity associated with learning processes. Their physiological firing is accompanied by a large afterhyperpolarization that is mainly due to the Na+/K+ ATPase activity and partially to a Ca2+-dependent K+ current. A clear-cut hyperpolarization and a significant increase of the afterhyperpolarization have been recorded in T neurons of leeches injected with 2 mM acetyl-L-carnitine some days before. Acute treatments of 50 mM acetyl-L-carnitine induced similar effects in T cells of naive animals. Moreover, in these cells, widely arborized, the afterhyperpolarization seems to play an important role in determining the action potential transmission at neuritic bifurcations. A computational model of a T cell has been previously developed considering detailed data for geometry and the modulation of the pump current. Herein, we showed that to a larger afterhyperpolarization, due to the acetyl-L-carnitine-induced effects, corresponds a decrement in the number of action potentials reaching synaptic terminals. | |
| Electrically-coupled Retzius neurons (Vazquez et al. 2009) | |
| "Dendritic electrical coupling increases the number of effective synaptic inputs onto neurons by allowing the direct spread of synaptic potentials from one neuron to another. Here we studied the summation of excitatory postsynaptic potentials (EPSPs) produced locally and arriving from the coupled neuron (transjunctional) in pairs of electrically-coupled Retzius neurons of the leech. We combined paired recordings of EPSPs, the production of artificial EPSPs (APSPs) in neuron pairs with different coupling coefficients and simulations of EPSPs produced in the coupled dendrites. ..." | |
| Enhanced Excitability in Hermissenda: modulation by 5-HT (Cai et al 2003) | |
| Serotonin (5-HT) applied to the exposed but otherwise intact nervous system results in enhanced excitability of Hermissenda type-B photoreceptors. Several ion currents in the type-B photoreceptors are modulated by 5-HT, including the A-type K+ current (IK,A), sustained Ca2+ current (ICa,S), Ca-dependent K+ current (IK,Ca), and a hyperpolarization-activated inward rectifier current (Ih). In this study,we developed a computational model that reproduces physiological characteristics of type B photoreceptors, e.g. resting membrane potential, dark-adapted spike activity, spike width, and the amplitude difference between somatic and axonal spikes. We then used the model to investigate the contribution of different ion currents modulated by 5-HT to the magnitudes of enhanced excitability produced by 5-HT. See paper for results and more details. | |
| Firing patterns in stuttering fast-spiking interneurons (Klaus et al. 2011) | |
| This is a morphologically extended version of the fast-spiking interneuron by Golomb et al. (2007). The model captures the stuttering firing pattern and subthreshold oscillations in response to step current input as observed in many cortical and striatal fast-spiking cells. | |
| Fronto-parietal visuospatial WM model with HH cells (Edin et al 2007) | |
| 1) J Cogn Neurosci: 3 structural mechanisms that had been hypothesized to underlie vsWM development during childhood were evaluated by simulating the model and comparing results to fMRI. It was concluded that inter-regional synaptic connection strength cause vsWM development. 2) J Integr Neurosci: Given the importance of fronto-parietal connections, we tested whether connection asymmetry affected resistance to distraction. We drew the conclusion that stronger frontal connections are benefiction. By comparing model results to EEG, we concluded that the brain indeed has stronger frontal-to-parietal connections than vice versa. | |
| Gamma oscillations in hippocampal interneuron networks (Bartos et al 2002) | |
| To examine whether an interneuron network with fast inhibitory synapses can act as a gamma frequency oscillator, we developed an interneuron network model based on experimentally determined properties. In comparison to previous interneuron network models, our model was able to generate oscillatory activity with higher coherence over a broad range of frequencies (20-110 Hz). In this model, high coherence and flexibility in frequency control emerge from the combination of synaptic properties, network structure, and electrical coupling. | |
| Gap junction coupled network of striatal fast spiking interneurons (Hjorth et al. 2009) | |
| Gap junctions between striatal FS neurons has very weak ability to synchronise spiking. Input uncorrelated between neighbouring neurons is shunted, while correlated input is not. | |
| Gap-junction coupled network activity depends on coupled dendrites diameter (Gansert et al. 2007) | |
| "... We have previously shown that the amplitude of electrical signals propagating across gap-junctionally coupled passive cables is maximized at a unique diameter. This suggests that threshold-dependent signals may propagate through gap junctions for a finite range of diameters around this optimal value. Here we examine the diameter dependence of action potential propagation across model networks of dendro-dendritically coupled neurons. The neurons in these models have passive soma and dendrites and an action potential-generating axon. We show that propagation of action potentials across gap junctions occurs only over a finite range of dendritic diameters and that propagation delay depends on this diameter. ...". See paper for more and details. | |
| Grid cell oscillatory interference with noisy network oscillators (Zilli and Hasselmo 2010) | |
| To examine whether an oscillatory interference model of grid cell activity could work if the oscillators were noisy neurons, we implemented these simulations. Here the oscillators are networks (either synaptically- or gap-junction--coupled) of one or more noisy neurons (either Izhikevich's simple model or a Hodgkin-Huxley--type biophysical model) which drive a postsynaptic cell (which may be integrate-and-fire, resonate-and-fire, or the simple model) which should fire spatially as a grid cell if the simulation is successful. | |
| High frequency oscillations in a hippocampal computational model (Stacey et al. 2009) | |
| "... Using a physiological computer model of hippocampus, we investigate random synaptic activity (noise) as a potential initiator of HFOs (high-frequency oscillations). We explore parameters necessary to produce these oscillations and quantify the response using the tools of stochastic resonance (SR) and coherence resonance (CR). ... Our results show that, under normal coupling conditions, synaptic noise was able to produce gamma (30–100 Hz) frequency oscillations. Synaptic noise generated HFOs in the ripple range (100–200 Hz) when the network had parameters similar to pathological findings in epilepsy: increased gap junctions or recurrent synaptic connections, loss of inhibitory interneurons such as basket cells, and increased synaptic noise. ... We propose that increased synaptic noise and physiological coupling mechanisms are sufficient to generate gamma oscillations and that pathologic changes in noise and coupling similar to those in epilepsy can produce abnormal ripples." | |
| High frequency oscillations induced in three gap-junction coupled neurons (Tseng et al. 2008) | |
| Here we showed experimentally that high frequency oscillations (up to 600 Hz) were easily induced in a purely gap-junction coupled network by simple two stimuli with very short interval. The root cause is that the second elicited spike suffered from slow propagation speed and failure to transmit through a low-conductance junction. Similiar results were also obtained in these simulation. | |
| Hippocampal basket cell gap junction network dynamics (Saraga et al. 2006) | |
| 2 cell network of hippocampal basket cells connected by gap junctions. Paper explores how distal gap junctions and active dendrites can tune network dynamics. | |
| Inferring connection proximity in electrically coupled networks (Cali et al. 2007) | |
| In order to explore electrical coupling in the nervous system and its network-level organization, it is imperative to map the electrical synaptic microcircuits, in analogy with in vitro studies on monosynaptic and disynaptic chemical coupling. However, “walking” from cell to cell over large distances with a glass pipette is challenging, and microinjection of (fluorescent) dyes diffusing through gap-junctions remains so far the only method available to decipher such microcircuits even though technical limitations exist. Based on circuit theory, we derived analytical descriptions of the AC electrical coupling in networks of isopotential cells. We then proposed an operative electrophysiological protocol to distinguish between direct electrical connections and connections involving one or more intermediate cells. This method allows inferring the number of intermediate cells, generalizing the conventional coupling coefficient, which provides limited information. We provide here some analysis and simulation scripts that used to test our method through computer simulations, in vitro recordings, theoretical and numerical methods. Key words: Gap-Junctions; Electrical Coupling; Networks; ZAP current; Impedance. | |
| Mechanisms of very fast oscillations in axon networks coupled by gap junctions (Munro, Borgers 2010) | |
| Axons connected by gap junctions can produce very fast oscillations (VFOs, > 80 Hz) when stimulated randomly at a low rate. The models here explore the mechanisms of VFOs that can be seen in an axonal plexus, (Munro & Borgers, 2009): a large network model of an axonal plexus, small network models of axons connected by gap junctions, and an implementation of the model underlying figure 12 in Traub et al. (1999) . The large network model consists of 3,072 5-compartment axons connected in a random network. The 5-compartment axons are the 5 axonal compartments from the CA3 pyramidal cell model in Traub et al. (1994) with a fixed somatic voltage. The random network has the same parameters as the random network in Traub et al. (1999), and axons are stimulated randomly via a Poisson process with a rate of 2/s/axon. The small network models simulate waves propagating through small networks of axons connected by gap junctions to study how local connectivity affects the refractory period. | |
| Neocort. pyramidal cells subthreshold somatic voltage controls spike propagation (Munro Kopell 2012) | |
| There is suggestive evidence that pyramidal cell axons in neocortex may be coupled by gap junctions into an ``axonal plexus" capable of generating Very Fast Oscillations (VFOs) with frequencies exceeding 80 Hz. It is not obvious, however, how a pyramidal cell in such a network could control its output when action potentials are free to propagate from the axons of other pyramidal cells into its own axon. We address this problem by means of simulations based on 3D reconstructions of pyramidal cells from rat somatosensory cortex. We show that somatic depolarization enables propagation via gap junctions into the initial segment and main axon, while somatic hyperpolarization disables it. We show further that somatic voltage cannot effectively control action potential propagation through gap junctions on minor collaterals; action potentials may therefore propagate freely from such collaterals regardless of somatic voltage. In previous work, VFOs are all but abolished during the hyperpolarization phase of slow-oscillations induced by anesthesia in vivo. This finding constrains the density of gap junctions on collaterals in our model and suggests that axonal sprouting due to cortical lesions may result in abnormally high gap junction density on collaterals, leading in turn to excessive VFO activity and hence to epilepsy via kindling. | |
| Network recruitment to coherent oscillations in a hippocampal model (Stacey et al. 2011) | |
| "... Here we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of Stochastic Resonance and Coherence Resonance. We develop a novel statistical method to quantify recruitment using several measures of network synchrony. This measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise. ..." | |
| Olfactory bulb mitral cell gap junction NN model: burst firing and synchrony (O`Connor et al. 2012) | |
| In a network of 6 mitral cells connected by gap junction in the apical dendrite tuft, continuous current injections of 0.06 nA are injected into 20 locations in the apical tufts of two of the mitral cells. The current injections into one of the cells starts 10 ms after the other to generate asynchronous firing in the cells (Migliore et al. 2005 protocol). Firing of the cells is asynchronous for the first 120 ms. However after the burst firing phase is completed the firing in all cells becomes synchronous. | |
| Olfactory bulb mitral cell: synchronization by gap junctions (Migliore et al 2005) | |
| In a realistic model of two electrically connected mitral cells, the paper shows that the somatically-measured experimental properties of Gap Junctions (GJs) may correspond to a variety of different local coupling strengths and dendritic distributions of GJs in the tuft. The model suggests that the propagation of the GJ-induced local tuft depolarization is a major mechanim for intraglomerular synchronization of mitral cells. | |
| Parametric computation and persistent gamma in a cortical model (Chambers et al. 2012) | |
| Using the Traub et al (2005) model of the cortex we determined how 33 synaptic strength parameters control gamma oscillations. We used fractional factorial design to reduce the number of runs required to 4096. We found an expected multiplicative interaction between parameters. | |
| Principles of Computational Modelling in Neuroscience (Book) (Sterratt et al. 2011) | |
| "... This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signaling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modeling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience." | |
| Rapid desynchronization of an electrically coupled Golgi cell network (Vervaeke et al. 2010) | |
| Electrical synapses between interneurons contribute to synchronized firing and network oscillations in the brain. However, little is known about how such networks respond to excitatory synaptic input. In addition to detailed electrophysiological recordings and histological investigations of electrically coupled Golgi cells in the cerebellum, a detailed network model of these cells was created. The cell models are based on reconstructed Golgi cell morphologies and the active conductances are taken from an earlier abstract Golgi cell model (Solinas et al 2007, accession no. 112685). Our results show that gap junction coupling can sometimes be inhibitory and either promote network synchronization or trigger rapid network desynchronization depending on the synaptic input. The model is available as a neuroConstruct project and can executable scripts can be generated for the NEURON simulator. | |
| Regulation of a slow STG rhythm (Nadim et al 1998) | |
| Frequency regulation of a slow rhythm by a fast periodic input. Nadim, F., Manor, Y., Nusbaum, M. P., Marder, E. (1998) J. Neurosci. 18: 5053-5067 | |
| S cell network (Moss et al 2005) | |
| Excerpts from the abstract: S cells form a chain of electrically coupled neurons that extends the length of the leech CNS and plays a critical role in sensitization during whole-body shortening. ... Serotonin ... increasedAP latency across the electrical synapse, suggesting that serotonin reduced coupling between S cells. ... Serotonin modulated instantaneous AP frequency when APs were initiated in separate S cells and in a computational model of S cell activity following mechanosensory input. Thus, serotonergic modulation of S cell electrical synapses may contribute to changes in the pattern of activity in the S cell network. See paper for more. | |
| Spike propagation and bouton activation in terminal arborizations (Luscher, Shiner 1990) | |
| Action potential propagation in axons with bifurcations involving short collaterals with synaptic boutons has been simulated ... The architecture of the terminal arborizations has a profound effect on the activation pattern of synapses, suggesting that terminal arborizations not only distribute neural information to postsynaptic cells but may also be able to process neural information presynaptically. Please see paper for details. | |
| Striatal GABAergic microcircuit, dopamine-modulated cell assemblies (Humphries et al. 2009) | |
| To begin identifying potential dynamically-defined computational elements within the striatum, we constructed a new three-dimensional model of the striatal microcircuit's connectivity, and instantiated this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs was introduced and tuned to experimental data. We introduced a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We found that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appeared, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation was strongly dependent on the simulated concentration of dopamine. We also showed that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs. | |
| Striatal GABAergic microcircuit, spatial scales of dynamics (Humphries et al, 2010) | |
| The main thrust of this paper was the development of the 3D anatomical network of the striatum's GABAergic microcircuit. We grew dendrite and axon models for the MSNs and FSIs and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. These networks were examined for their predictions for the distributions of the numbers and distances of connections for all the connections in the microcircuit. We then combined the neuron models from a previous model (Humphries et al, 2009; ModelDB ID: 128874) with the new anatomical model. We used this new complete striatal model to examine the impact of the anatomical network on the firing properties of the MSN and FSI populations, and to study the influence of all the inputs to one MSN within the network. | |
ModelDB Home SenseLab Home Help
Questions, comments, problems? Email the ModelDB Administrator
How to cite ModelDB
This site is Copyright 2012 Shepherd Lab, Yale University