Optical stimulation of a channelrhodopsin-2 positive pyramidal neuron model (Foutz et al 2012)

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A computational tool to explore the underlying principles of optogenetic neural stimulation. This "light-neuron" model consists of theoretical representations of the light dynamics generated by a fiber optic in brain tissue, coupled to a multicompartment cable model of a cortical pyramidal neuron (Hu et al. 2009, ModelDB #123897) embedded with channelrhodopsin-2 (ChR2) membrane dynamics. Simulations predict that the activation threshold is sensitive to many of the properties of ChR2 (density, conductivity, and kinetics), tissue medium (scattering and absorbance), and the fiber-optic light source (diameter and numerical aperture). This model system represents a scientific instrument to characterize the effects of optogenetic neuromodulation, as well as an engineering design tool to help guide future development of optogenetic technology.
1 . Foutz TJ, Arlow RL, McIntyre CC (2012) Theoretical principles underlying optical stimulation of a channelrhodopsin-2 positive pyramidal neuron. J Neurophysiol 107:3235-45 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Axon; Channel/Receptor; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I K; I M; I K,Ca; I Sodium; I Calcium; Channelrhodopsin (ChR);
Gap Junctions:
Simulation Environment: NEURON; Python;
Model Concept(s): Detailed Neuronal Models; Action Potentials; Parameter sensitivity; Intracortical Microstimulation; Neuromodulation;
Implementer(s): MacIntyre, CC [ccm4 at case.edu]; Foutz, Thomas J [tfoutz at uw.edu]; Arlow, Richard L [richard.arlow at case.edu];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; I K; I M; I K,Ca; I Sodium; I Calcium; Channelrhodopsin (ChR);
/* ---------------------------------------------------------------------------------------
    Define the Spines Geometry
    Based on the "Folding factor" described in Jack et al (1989), Major et al (1994)
    Note, this assumes active channels are present in spines at same density as dendrites

spine_dens = 1    // just using a simple spine density model due to lack of data on some neuron types.

spine_area = 0.83 // um^2  -- K Harris

proc add_spines() {  local a
  forsec $o1 {
    a =0
    for(x) a=a+area(x)

    F = (L*spine_area*spine_dens + a)/a

    L = L * F^(2/3)
    for(x) diam(x) = diam(x) * F^(1/3)

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