| | Models | Description |
| 1. |
Carbon nanotubes as electrical interfaces to neurons (Giugliano et al. 2008)
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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. |
| 2. |
Emergence of Connectivity Motifs in Networks of Model Neurons (Vasilaki, Giugliano 2014)
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Recent evidence suggests that short-term dynamics of excitatory synaptic transmission is correlated to stereotypical connectivity motifs.
We show that these connectivity motifs emerge in networks of model neurons, from the interactions between short-term synaptic dynamics (SD) and long-term spike-timing dependent plasticity (STDP). |
| 3. |
Huntington`s disease model (Gambazzi et al. 2010)
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"Although previous studies of Huntington’s disease (HD) have addressed many potential mechanisms of striatal neuron dysfunction and death, it is also known based on clinical findings that cortical function is dramatically disrupted in HD. With respect to disease etiology, however, the specific molecular and neuronal circuit bases for the cortical effects of mutant huntingtin (htt) have remained largely unknown.
In the present work we studied the relation between the molecular effects of mutant htt fragments in cortical cells and the corresponding behavior of cortical neuron microcircuits using a novel cellular model of HD. We observed that a transcript-selective diminution in activity-dependent BDNF expression preceded the onset of a synaptic connectivity deficit in ex vivo cortical networks, which manifested as decreased spontaneous collective burst-firing behavior measured by multi-electrode array substrates. Decreased BDNF expression was determined to be a significant contributor to network-level dysfunction, as shown by the ability of exogenous BDNF to ameliorate cortical microcircuit burst firing.
The molecular determinants of the dysregulation of activity-dependent BDNF expression by mutant htt appear to be distinct from previously elucidated mechanisms, as they do not involve known NRSF/REST-regulated promoter sequences, but instead result from dysregulation of BDNF exon IV and VI transcription. These data elucidate a novel HD-related deficit in BDNF gene regulation as a plausible mechanism of cortical neuron hypoconnectivity and cortical function deficits in HD. Moreover, the novel model paradigm established here is well-suited to further mechanistic and drug screening research applications.
A simple mathematical model is proposed to interpret the observations and to explore the impact of specific synaptic dysfunctions on network activity. Interestingly, the model predicts a decrease in synaptic connectivity to be an early effect of mutant huntingtin in cortical neurons, supporting the hypothesis of decreased, rather than increased, synchronized cortical firing in HD." |
| 4. |
Hyperconnectivity, slow synapses in PFC mental retardation and autism model (Testa-Silva et al 2011)
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The subdirectory 'matlab' contains MATLAB scripts (The Mathworks, USA) that can be used to reproduce the panels of Figures 4-5.
This directory contains files to reproduce sample computer simulations presented in the 2011 paper authored by
Meredith, R., Testa-Silva, G., Loebel, A., Giugliano, M., de Kock, C.; Mansvelder, H.
"Hyperconnectivity and slow synapses in prefrontal cortex of a model for mental retardation and autism".
ABSTRACT
"... We propose that these findings are tightly linked: using a network model, we show that slower synapses are essential to counterbalance hyperconnectivity in order to maintain a dynamic range of excitatory activity. However, the slow synaptic time constants induce decreased responsiveness to low frequency stimulation, which may explain deficits in integration and information processing in attentional neuronal networks in neurodevelopmental disorders." |
| 5. |
Inferring connection proximity in electrically coupled networks (Cali et al. 2007)
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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.
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| 6. |
Pipette and membrane patch geometry effects on GABAa currents patch-clamp exps (Moroni et al. 2011)
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Ion currents, mediated by GABAa-receptors in outside-out membrane patches, may alter the concentration of Chloride ions inside the pipette and the membrane patch.
GABAa-receptors are in fact ionotropic synaptic receptors, selective to Chloride ions. Therefore, chloride fluxes across the membrane patch correlate to GABAa-receptor opening.
Chloride ions accumulation, depletion and diffusion, inside the pipette and the membrane patch, affect by definition the Chloride equilibrium (i.e. Nernst) electrical potential. This in turn changes the ionic driving force underlying GABAa-mediated currents.
It follows that, in case of very small volumes and confined geometries, voltage-clamp recordings of GABAa-receptor currents carry information on both i) Chloride diffusion and ii) receptor kinetics.
The relevance of (i) and (ii) have been studied numerically by defining a 1-dimensional biophysical model, released here to the interested user. |
| 7. |
Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008)
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Neocortical neurons are classified by current–frequency relationship. This is a static description and it may be inadequate to interpret neuronal responses to time-varying stimuli.
Theoretical studies (Brunel et al., 2001; Fourcaud-Trocmé et al. 2003; Fourcaud-Trocmé and Brunel 2005; Naundorf et al. 2005) suggested that single-cell dynamical response properties are necessary to interpret ensemble responses to fast input transients. Further, it was shown that input-noise linearizes and boosts the response bandwidth, and that the interplay between the barrage of noisy synaptic currents and the spike-initiation mechanisms determine the dynamical properties of the firing rate.
In order to allow a reader to explore such simulations, we prepared a simple NEURON implementation of the experiments performed in Köndgen et al., 2008 (see also Fourcaud-Trocmé al. 2003; Fourcaud-Trocmé and Brunel 2005).
In addition, we provide sample MATLAB routines for exploring the sandwich model proposed in Köndgen et al., 2008, employing a simple frequdency-domain filtering.
The simulations and the MATLAB routines are based on the linear response properties of layer 5 pyramidal cells estimated by injecting a superposition of a small-amplitude sinusoidal wave and a background noise, as in Köndgen et al., 2008. |
| 8. |
Voltage and light-sensitive Channelrhodopsin-2 model (ChR2-H134R) (Williams et al. 2013) (NEURON)
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" ... Focusing on one of the most widely used ChR2 mutants (H134R) with enhanced current, we collected a comprehensive experimental data set of the response of this ion channel to different irradiances and voltages, and used these data to develop a model of ChR2 with empirically-derived voltage- and irradiance- dependence, where parameters were fine-tuned via simulated annealing optimization. This ChR2 model offers: 1) accurate inward rectification in the current-voltage response across irradiances; 2) empirically-derived voltage- and light-dependent kinetics (activation, deactivation and recovery from inactivation); and 3) accurate amplitude and morphology of the response across voltage and irradiance settings. Temperature-scaling factors (Q10) were derived and model kinetics was adjusted to physiological temperatures. ... " |
