Models that contain the Model Concept : Development

Re-display model names without descriptions
    Models   Description
1.  A synapse model for developing somatosensory cortex (Manninen et al 2020)
We developed a model for an L4-L2/3 synapse in somatosensory cortex to study the role of astrocytes in modulation of t-LTD. Our model includes the one-compartmental presynaptic L4 spiny stellate cell, two-compartmental (soma and dendrite) postsynaptic L2/3 pyramidal cell, and one-compartmental fine astrocyte process.
2.  An agent-based computational model for cortical layer formation (Bauer et al 2021)
This computational model can account for layer-specific neuron numbers in various different cortical structures. It is agent-based and is initiated from a small homogeneous pool of precursor cells. The file Lamination.java includes the main function that induces the activation of multiple "modules" within a gene regulatory network.
3.  Axon growth model (Diehl et al. 2016)
The model describes the elongation over time of an axon from a small neurite to its steady-state length. The elongation depends on the availability of tubulin dimers in the growth cone. The dimers are produced in the soma and then transported along the axon to the growth cone. Mathematically the model consists of a partial differential equation coupled with two nonlinear ordinary differential equations. The code implements a spatial scaling to deal with the growing (and shrinking) domain and a temporal scaling to deal with evolutions on different time scales. Further, the numerical scheme is chosen to fully utilize the structure of the problems. To summarize, this results in fast and reliable axon growth simulations.
4.  BDNF morphological contributions to AP enhancement (Galati et al. 2016)
" ... We quantified BDNF’s effect on cultured cortical neuron morphological parameters and found that BDNF stimulates dendrite growth and addition of dendrites while increasing both excitatory and inhibitory presynaptic inputs in a spatially restricted manner. To gain insight into how these combined changes in neuron structure and synaptic input impact AP generation, we used the morphological parameters we gathered to generate computational models. Simulations suggest that BDNF-induced neuron morphologies generate more APs under a wide variety of conditions. ..."
5.  Compartmental models of growing neurites (Graham and van Ooyen 2004)
Simulator for models of neurite outgrowth. The principle model is a biophysical model of neurite outgrowth described in Graham and van Ooyen (2004). In the model, branching depends on the concentration of a branch-determining substance in each terminal segment. The substance is produced in the cell body and is transported by active transport and diffusion to the terminals. The model reveals that transport-limited effects may give rise to the same modulation of branching as indicated by the stochastic BESTL model. Different limitations arise if transport is dominated by active transport or by diffusion.
6.  Continuous time stochastic model for neurite branching (van Elburg 2011)
"In this paper we introduce a continuous time stochastic neurite branching model closely related to the discrete time stochastic BES-model. The discrete time BES-model is underlying current attempts to simulate cortical development, but is difficult to analyze. The new continuous time formulation facilitates analytical treatment thus allowing us to examine the structure of the model more closely. ..."
7.  Continuum model of tubulin-driven neurite elongation (Graham et al 2006)
This model investigates the elongation over time of a single developing neurite (axon or dendrite). Our neurite growth model describes the elongation of a single,unbranched neurite in terms of the rate of extension of the microtubule cytoskeleton. The cytoskeleton is not explicitly modelled, but its construction is assumed to depend on the available free tubulin at the growing neurite tip.
8.  Development and Binocular Matching of Orientation Selectivity in Visual Cortex (Xu et al 2020)
This model investigates the development of orientation selectivity and its binocular matching in visual cortex by implementing a neuron that has plastic synapses for its inputs from the left and right eye. The plasticity is taken to be voltage-based with homeostasis (Clopath et al 2010). The neuron is modeled as an adaptive exponential integrate-fire neuron. The uploaded model has been used in Xu, Cang & Riecke (2020) to analyze the impact of ocular dominance and orientation selectivity on the matching process. There it has been found that the matching can proceed by a slow shifting or a sudden switching of the preferred orientation.
9.  Development of orientation-selective simple cell receptive fields (Rishikesh and Venkatesh, 2003)
Implementation of a computational model for the development of simple-cell receptive fields spanning the regimes before and after eye-opening. The before eye-opening period is governed by a correlation-based rule from Miller (Miller, J. Neurosci., 1994), and the post eye-opening period is governed by a self-organizing, experience-dependent dynamics derived in the reference below.
10.  DG adult-born granule cell: nonlinear a5-GABAARs control AP firing (Lodge et al, accepted)
GABA can depolarize immature neurons close to the action potential (AP) threshold in development and adult neurogenesis. Nevertheless, GABAergic synapses effectively inhibit AP firing in newborn granule cells of the adult hippocampus as early as 2 weeks post mitosis. Parvalbumin and dendrite-targeting somatostatin interneurons activate a5-subunit containing GABAA receptors (a5-GABAARs) in young neurons, which show a voltage dependent conductance profile with increasing conductance around the AP threshold. The present computational models show that the depolarized GABA reversal potential promotes NMDA receptor activation. However, the voltage-dependent conductance of a5-GABAARs in young neurons is crucial for inhibition of AP firing to generate balanced and sparse firing activity.
11.  Diameter, Myelination and Na/K pump interactions affect axonal resilience to high frequency spiking
12.  Differential interactions between Notch and ID factors control neurogenesis (Boareto et al 2017)
"During embryonic and adult neurogenesis, neural stem cells (NSCs) generate the correct number and types of neurons in a temporospatial fashion. Control of NSC activity and fate is crucial for brain formation and homeostasis. Neurogenesis in the embryonic and adult brain differ considerably, but Notch signaling and inhibitor of DNA-binding (ID) factors are pivotal in both. Notch and ID factors regulate NSC maintenance; however, it has been difficult to evaluate how these pathways potentially interact. Here, we combined mathematical modeling with analysis of single-cell transcriptomic data to elucidate unforeseen interactions between the Notch and ID factor pathways. ..."
13.  Disrupted information processing in Fmr1-KO mouse layer 4 barrel cortex (Domanski et al 2019)
"Sensory hypersensitivity is a common and debilitating feature of neurodevelopmental disorders such as Fragile X Syndrome (FXS). How developmental changes in neuronal function culminate in network dysfunction that underlies sensory hypersensitivities is unknown. By systematically studying cellular and synaptic properties of layer 4 neurons combined with cellular and network simulations, we explored how the array of phenotypes in Fmr1-knockout (KO) mice produce circuit pathology during development. We show that many of the cellular and synaptic pathologies in Fmr1-KO mice are antagonistic, mitigating circuit dysfunction, and hence may be compensatory to the primary pathology. Overall, the layer 4 network in the Fmr1-KO exhibits significant alterations in spike output in response to thalamocortical input and distorted sensory encoding. This developmental loss of layer 4 sensory encoding precision would contribute to subsequent developmental alterations in layer 4-to-layer 2/3 connectivity and plasticity observed in Fmr1-KO mice, and circuit dysfunction underlying sensory hypersensitivity."
14.  Human somatosensory and motor axon pair to compare thresholds (Gaines et al 2018)
These motor and sensory axon models are based on the MRG axon model and the Howells motor and sensory compartment models. They take into account known differences in the channel properties between sensory and motor neurons.
15.  Late emergence of the whisker direction selectivity map in rat barrel cortex (Kremer et al. 2011)
"... We discovered that the emergence of a direction map in rat barrel cortex occurs long after all known critical periods in the somatosensory system. This map is remarkably specific, taking a pinwheel-like form centered near the barrel center and aligned to the barrel cortex somatotopy. We suggest that this map may arise from intracortical mechanisms and demonstrate by simulation that the combination of spike-timing-dependent plasticity at synapses between layer 4 and layer 2/3 and realistic pad stimulation is sufficient to produce such a map. ..."
16.  Lillie Transition: onset of saltatory conduction in myelinating axons (Young et al. 2013)
Included are the NEURON (.hoc) files needed to generate the data used in our Young, Castelfranco, Hartline (2013) paper. The resulting .dat files are in the same folder as the MATLAB (.m) files that are used to sort the data.
17.  Mechanisms for stable, robust, and adaptive development of orientation maps (Stevens et al. 2013)
GCAL (Gain Control, Adaptation, Laterally connected). Simple but robust single-population V1 orientation map model.
18.  Medial reticular formation of the brainstem: anatomy and dynamics (Humphries et al. 2006, 2007)
A set of models to study the medial reticular formation (mRF) of the brainstem. We developed a collection of algorithms to derive the adult-state wiring of the model: one set a stochastic model; the other set mimicking the developmental process. We found that the anatomical models had small-world properties, irrespective of the choice of algorithm; and that the cluster-like organisation of the mRF may have arisen to minimise wiring costs. (The model code includes options to be run as dynamic models; papers examining these dynamics are included in the .zip file).
19.  Modeling hebbian and homeostatic plasticity (Toyoizumi et al. 2014)
"... We propose a model in which synaptic strength is the product of a synapse-specific Hebbian factor and a postsynaptic- cell-specific homeostatic factor, with each factor separately arriving at a stable inactive state. This model captures ODP dynamics and has plausible biophysical substrates. We confirm model predictions experimentally that plasticity is inactive at stable states and that synaptic strength overshoots during recovery from visual deprivation. ..."
20.  Models of visual topographic map alignment in the Superior Colliculus (Tikidji-Hamburyan et al 2016)
We develop two novel computational models of visual map alignment in the SC that incorporate distinct activity-dependent components. First, a Correlational Model assumes that V1 inputs achieve alignment with established retinal inputs through simple correlative firing mechanisms. A second Integrational Model assumes that V1 inputs contribute to the firing of SC neurons during alignment. Both models accurately replicate in vivo findings in wild type, transgenic and combination mutant mouse models, suggesting either activity-dependent mechanism is plausible.
21.  Neural field model to reconcile structure with function in V1 (Rankin & Chavane 2017)
"Voltage-sensitive dye imaging experiments in primary visual cortex (V1) have shown that local, oriented visual stimuli elicit stable orientation-selective activation within the stimulus retinotopic footprint. The cortical activation dynamically extends far beyond the retinotopic footprint, but the peripheral spread stays non-selective—a surprising finding given a number of anatomo-functional studies showing the orientation specificity of long-range connections. Here we use a computational model to investigate this apparent discrepancy by studying the expected population response using known published anatomical constraints. The dynamics of input-driven localized states were simulated in a planar neural field model with multiple sub-populations encoding orientation. The realistic connectivity profile has parameters controlling the clustering of long-range connections and their orientation bias. We found substantial overlap between the anatomically relevant parameter range and a steep decay in orientation selective activation that is consistent with the imaging experiments. In this way our study reconciles the reported orientation bias of long-range connections with the functional expression of orientation selective neural activity. Our results demonstrate this sharp decay is contingent on three factors, that long-range connections are sufficiently diffuse, that the orientation bias of these connections is in an intermediate range (consistent with anatomy) and that excitation is sufficiently balanced by inhibition. Conversely, our modelling results predict that, for reduced inhibition strength, spurious orientation selective activation could be generated through long-range lateral connections. Furthermore, if the orientation bias of lateral connections is very strong, or if inhibition is particularly weak, the network operates close to an instability leading to unbounded cortical activation. ..."
22.  pre-Bötzinger complex variability (Fietkiewicz et al. 2016)
" ... Based on experimental observations, we developed a computational model that can be embedded in more comprehensive models of respiratory and cardiovascular autonomic control. Our simulation results successfully reproduce the variability we observed experimentally. The in silico model suggests that age-dependent variability may be due to a developmental increase in mean synaptic conductance between preBötC neurons. We also used simulations to explore the effects of stochastic spiking in sensory relay neurons. Our results suggest that stochastic spiking may actually stabilize modulation of both respiratory rate and its variability when the rate changes due to physiological demand. "
23.  Quantitative assessment of computational models for retinotopic map formation (Hjorth et al. 2015)
"Molecular and activity-based cues acting together are thought to guide retinal axons to their terminal sites in vertebrate optic tectum or superior colliculus (SC) to form an ordered map of connections. The details of mechanisms involved, and the degree to which they might interact, are still not well understood. We have developed a framework within which existing computational models can be assessed in an unbiased and quantitative manner against a set of experimental data curated from the mouse retinocollicular system. ..."
24.  Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
Phenomenological spiking model of the cat early visual system. We show how natural vision can drive spike time correlations on sufficiently fast time scales to lead to the acquisition of orientation-selective V1 neurons through STDP. This is possible without reference times such as stimulus onsets, or saccade landing times. But even when such reference times are available, we demonstrate that the relative spike times encode the images more robustly than the absolute ones.
25.  Resource competition in growing neurites (Hjorth et al 2014)
Computer model of neurite outgrowth in a simplified neuron. A growth limiting resource is produced in the soma, transported through the neurites and consumed at the growth cones.
26.  Retinal ganglion cells responses and activity (Tsai et al 2012, Guo et al 2016)
From the abstracts: "Retinal ganglion cells (RGCs), which survive in large numbers following neurodegenerative diseases, could be stimulated with extracellular electric pulses to elicit artificial percepts. How do the RGCs respond to electrical stimulation at the sub-cellular level under different stimulus configurations, and how does this influence the whole-cell response? At the population level, why have experiments yielded conflicting evidence regarding the extent of passing axon activation? We addressed these questions through simulations of morphologically and biophysically detailed computational RGC models on high performance computing clusters. We conducted the analyses on both large-field RGCs and small-field midget RGCs. ...", "... In this study, an existing RGC ionic model was extended by including a hyperpolarization activated non-selective cationic current as well as a T-type calcium current identified in recent experimental findings. Biophysically-defined model parameters were simultaneously optimized against multiple experimental recordings from ON and OFF RGCs. ...
27.  SCN1A gain-of-function in early infantile encephalopathy (Berecki et al 2019)
"OBJECTIVE: To elucidate the biophysical basis underlying the distinct and severe clinical presentation in patients with the recurrent missense SCN1A variant, p.Thr226Met. Patients with this variant show a well-defined genotype-phenotype correlation and present with developmental and early infantile epileptic encephalopathy that is far more severe than typical SCN1A Dravet syndrome. METHODS: Whole cell patch clamp and dynamic action potential clamp were used to study T226M Nav 1.1 channels expressed in mammalian cells. Computational modeling was used to explore the neuronal scale mechanisms that account for altered action potential firing. RESULTS: T226M channels exhibited hyperpolarizing shifts of the activation and inactivation curves and enhanced fast inactivation. Dynamic action potential clamp hybrid simulation showed that model neurons containing T226M conductance displayed a left shift in rheobase relative to control. At current stimulation levels that produced repetitive action potential firing in control model neurons, depolarization block and cessation of action potential firing occurred in T226M model neurons. Fully computationally simulated neuron models recapitulated the findings from dynamic action potential clamp and showed that heterozygous T226M models were also more susceptible to depolarization block. ..."
28.  Self-organization of cortical areas in development and evolution of neocortex (Imam & Finlay 2021)
"Using physical parameters representing primary and secondary visual areas as they vary from monkey to mouse, we derived a network growth model to explore if characteristic features of secondary areas could be produced from correlated activity patterns arising from V1 alone."
29.  Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014)
"... In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. ... Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. ... After training with natural images, the neurons display heightened sensitivity to specific colors. ..."
30.  Spatial constrains of GABAergic rheobase shift (Lombardi et al., accepted)
In this models we investigated how the threshold eGABA, at which GABAergic inhibition switches to excitation, depends on the spatiotemporal constrains in a ball-and-stick neurons and a neurons with a topology derived from an reconstructed neuron.
31.  Spatial coupling tunes NMDA receptor responses via Ca2+ diffusion (Iacobucci and Popescu 2019)
This code implements a coupled markov model for analysis of positive or negative ion channel coupling from measured unitary currents in patch clamp recordings see our paper: Spatial Coupling Tunes NMDA Receptor Responses via Ca2+ Diffusion Gary J. Iacobucci and Gabriela K. Popescu Journal of Neuroscience 6 November 2019, 39 (45) 8831-8844; DOI: https://doi.org/10.1523/JNEUROSCI.0901-19.2019
32.  Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013)
Development of spiking grid cells and place cells in the entorhinal-hippocampal system to represent positions in large spaces
33.  Universal feature of developing networks (Tabak et al 2010)
"Spontaneous episodic activity is a fundamental mode of operation of developing networks. Surprisingly, the duration of an episode of activity correlates with the length of the silent interval that precedes it, but not with the interval that follows. ... We thus developed simple models incorporating excitatory coupling between heterogeneous neurons and activity-dependent synaptic depression. These models robustly generated episodic activity with the correct correlation pattern. The correlation pattern resulted from episodes being triggered at random levels of recovery from depression while they terminated around the same level of depression. To explain this fundamental difference between episode onset and termination, we used a mean field model, where only average activity and average level of recovery from synaptic depression are considered. ... This work further shows that networks with widely different architectures, different cell types, and different functions all operate according to the same general mechanism early in their development."
34.  Universal feature of developing networks (Tabak et al 2010) (CellML)
"Spontaneous episodic activity is a fundamental mode of operation of developing networks. Surprisingly, the duration of an episode of activity correlates with the length of the silent interval that precedes it, but not with the interval that follows. ... We thus developed simple models incorporating excitatory coupling between heterogeneous neurons and activity-dependent synaptic depression. These models robustly generated episodic activity with the correct correlation pattern. The correlation pattern resulted from episodes being triggered at random levels of recovery from depression while they terminated around the same level of depression. To explain this fundamental difference between episode onset and termination, we used a mean field model, where only average activity and average level of recovery from synaptic depression are considered. ... This work further shows that networks with widely different architectures, different cell types, and different functions all operate according to the same general mechanism early in their development." This modeldb entry only has the mean field model as networks are not implementable currently in CellML.

Re-display model names without descriptions