Models that contain the Region : Mouse

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    Models   Description
1.  Acetylcholine-modulated plasticity in reward-driven navigation (Zannone et al 2018)
"Neuromodulation plays a fundamental role in the acquisition of new behaviours. In previous experimental work, we showed that acetylcholine biases hippocampal synaptic plasticity towards depression, and the subsequent application of dopamine can retroactively convert depression into potentiation. We also demonstrated that incorporating this sequentially neuromodulated Spike- Timing-Dependent Plasticity (STDP) rule in a network model of navigation yields effective learning of changing reward locations. Here, we employ computational modelling to further characterize the effects of cholinergic depression on behaviour. We find that acetylcholine, by allowing learning from negative outcomes, enhances exploration over the action space. We show that this results in a variety of effects, depending on the structure of the model, the environment and the task. Interestingly, sequentially neuromodulated STDP also yields flexible learning, surpassing the performance of other reward-modulated plasticity rules."
2.  CA1 network model for place cell dynamics (Turi et al 2019)
Biophysical model of CA1 hippocampal region. The model simulates place cells/fields and explores the place cell dynamics as function of VIP+ interneurons.
3.  CA1 pyramidal neuron: nonlinear a5-GABAAR controls synaptic NMDAR activation (Schulz et al 2018)
The study shows that IPSCs mediated by a5-subunit containing GABAA receptors are strongly outward-rectifying generating 4-fold larger conductances above -50?mV than at rest. Experiments and modeling show that synaptic activation of these receptors can very effectively control voltage-dependent NMDA-receptor activation in a spatiotemporally controlled manner in fine dendrites of CA1 pyramidal cells. The files contain the NEURON code for Fig.8, Fig.S8 and Fig.S9 of the paper. The model is based on the model published by Bloss et al., 2017. Physiological properties of GABA synapses were modified as determined by optogenetic activation of inputs during voltage-clamp recordings in Schulz et al. 2018. Other changes include stochastic synaptic release and short-term synaptic plasticity. All changes of mechanisms and parameters are detailed in the Methods of the paper. Simulation can be run by starting start_simulation.hoc after running mknrndll. The files that model the individual figures have to be uncommented in start_simulation.hoc beforehand.
4.  Compartmental differences in cAMP signaling pathways in hippocam. CA1 pyr. cells (Luczak et al 2017)
Model of cAMP signaling pathways in hippocampal CA1 pyramidal neurons investigate mechanisms underlying the experimentally observed difference in cAMP and PKA FRET between proximal and distal dendrites. Simulations show that compartmental difference in PKA activity required enrichment of protein phosphatase in small compartments; neither reduced PKA subunits nor increased PKA substrates were sufficient.
5.  DRG neuron models investigate how ion channel levels regulate firing properties (Zheng et al 2019)
We present computational models for an Abeta-LTMR (low-threshold mechanoreceptor) and a C-LTMR expressing four Na channels and four K channels to investigate how the expression level of Kv1 and Kv4 regulate number of spikes (repetitive firing) and onset latency to action potentials in Abeta-LTMRs and C-LTMRs, respectively.
6.  Factors contribution to GDP-induced [Cl-]i transients (Lombardi et al 2019)
This models are used to evaluate which factors influence the GDP (giant depolarizing potential) induced [Cl-]I transients based on a initial model of P. Jedlicka
7.  L4 cortical barrel NN model receiving thalamic input during whisking or touch (Gutnisky et al. 2017)
Excitatory neurons in layer 4 (L4) in the barrel cortex respond relatively strongly to touch but not to whisker movement (Yu et al., Nat. Neurosci. 2016). The model explains the mechanism underlying this effect. The network is settled to filter out most stationary inputs. Brief touch input passes through because it takes time until feed-forward inhibition silences excitatory neurons receiving brief and strong thalamic excitation.
8.  Model for concentration invariant odor coding based on primacy hypothesis (Wilson et al 2017)
"... Here we propose that, in olfaction, a small and relatively stable set comprised of the earliest activated receptors forms a code for concentration-invariant odor identity. One prediction of this “primacy coding” scheme is that decisions based on odor identity can be made solely using early odor-evoked neural activity. Using an optogenetic masking paradigm, we define the sensory integration time necessary for odor identification and demonstrate that animals can use information occurring <100ms after inhalation onset to identify odors. ... We propose a computational model demonstrating how such a code can be read by neural circuits of the olfactory system."
9.  Role of the AIS in the control of spontaneous frequency of dopaminergic neurons (Meza et al 2017)
Computational modeling showed that the size of the Axon Initial Segment (AIS), but not its position within the somatodendritic domain, is the major causal determinant of the tonic firing rate in the intact model, by virtue of the higher intrinsic frequency of the isolated AIS. Further mechanistic analysis of the relationship between neuronal morphology and firing rate showed that dopaminergic neurons function as a coupled oscillator whose frequency of discharge results from a compromise between AIS and somatodendritic oscillators.
10.  The Virtual Mouse Brain (TVMB) (Melozzi et al. 2017)
"Connectome-based modeling of large-scale brain network dynamics enables causal in silico interrogation of the brain’s structure-function relationship, necessitating the close integration of diverse neuroinformatics fields. Here we extend the open-source simulation software The Virtual Brain to whole mouse brain network modeling based on individual diffusion Magnetic Resonance Imaging (dMRI)-based or tracer-based detailed mouse connectomes. We provide practical examples on how to use The Virtual Mouse Brain to simulate brain activity, such as seizure propagation and the switching behavior of the resting state dynamics in health and disease. The Virtual Mouse Brain enables theoretically driven experimental planning and ways to test predictions in the numerous strains of mice available to study brain function in normal and pathological conditions."

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