.. currentmodule:: brian2

.. Rothman_Manis_2003:

Example: Rothman_Manis_2003
===========================


        .. only:: html

            .. |launchbinder| image:: file:///usr/share/doc/python-brian-doc/docs/badge.svg
            .. _launchbinder: https://mybinder.org/v2/gh/brian-team/brian2-binder/master?filepath=examples/frompapers/Rothman_Manis_2003.ipynb

            .. note::
               You can launch an interactive, editable version of this
               example without installing any local files
               using the Binder service (although note that at some times this
               may be slow or fail to open): |launchbinder|_

        

Cochlear neuron model of Rothman & Manis
----------------------------------------
Rothman JS, Manis PB (2003) The roles potassium currents play in
regulating the electrical activity of ventral cochlear nucleus neurons.
J Neurophysiol 89:3097-113.

All model types differ only by the maximal conductances.

Adapted from their Neuron implementation by Romain Brette

::

    from brian2 import *
    
    #defaultclock.dt=0.025*ms # for better precision
    
    '''
    Simulation parameters: choose current amplitude and neuron type
    (from type1c, type1t, type12, type 21, type2, type2o)
    '''
    neuron_type = 'type1c'
    Ipulse = 250*pA
    
    C = 12*pF
    Eh = -43*mV
    EK = -70*mV  # -77*mV in mod file
    El = -65*mV
    ENa = 50*mV
    nf = 0.85  # proportion of n vs p kinetics
    zss = 0.5  # steady state inactivation of glt
    temp = 22.  # temperature in degree celcius
    q10 = 3. ** ((temp - 22) / 10.)
    # hcno current (octopus cell)
    frac = 0.0
    qt = 4.5 ** ((temp - 33.) / 10.)
    
    # Maximal conductances of different cell types in nS
    maximal_conductances = dict(
    type1c=(1000, 150, 0, 0, 0.5, 0, 2),
    type1t=(1000, 80, 0, 65, 0.5, 0, 2),
    type12=(1000, 150, 20, 0, 2, 0, 2),
    type21=(1000, 150, 35, 0, 3.5, 0, 2),
    type2=(1000, 150, 200, 0, 20, 0, 2),
    type2o=(1000, 150, 600, 0, 0, 40, 2) # octopus cell
    )
    gnabar, gkhtbar, gkltbar, gkabar, ghbar, gbarno, gl = [x * nS for x in maximal_conductances[neuron_type]]
    
    # Classical Na channel
    eqs_na = """
    ina = gnabar*m**3*h*(ENa-v) : amp
    dm/dt=q10*(minf-m)/mtau : 1
    dh/dt=q10*(hinf-h)/htau : 1
    minf = 1./(1+exp(-(vu + 38.) / 7.)) : 1
    hinf = 1./(1+exp((vu + 65.) / 6.)) : 1
    mtau =  ((10. / (5*exp((vu+60.) / 18.) + 36.*exp(-(vu+60.) / 25.))) + 0.04)*ms : second
    htau =  ((100. / (7*exp((vu+60.) / 11.) + 10.*exp(-(vu+60.) / 25.))) + 0.6)*ms : second
    """
    
    # KHT channel (delayed-rectifier K+)
    eqs_kht = """
    ikht = gkhtbar*(nf*n**2 + (1-nf)*p)*(EK-v) : amp
    dn/dt=q10*(ninf-n)/ntau : 1
    dp/dt=q10*(pinf-p)/ptau : 1
    ninf =   (1 + exp(-(vu + 15) / 5.))**-0.5 : 1
    pinf =  1. / (1 + exp(-(vu + 23) / 6.)) : 1
    ntau =  ((100. / (11*exp((vu+60) / 24.) + 21*exp(-(vu+60) / 23.))) + 0.7)*ms : second
    ptau = ((100. / (4*exp((vu+60) / 32.) + 5*exp(-(vu+60) / 22.))) + 5)*ms : second
    """
    
    # Ih channel (subthreshold adaptive, non-inactivating)
    eqs_ih = """
    ih = ghbar*r*(Eh-v) : amp
    dr/dt=q10*(rinf-r)/rtau : 1
    rinf = 1. / (1+exp((vu + 76.) / 7.)) : 1
    rtau = ((100000. / (237.*exp((vu+60.) / 12.) + 17.*exp(-(vu+60.) / 14.))) + 25.)*ms : second
    """
    
    # KLT channel (low threshold K+)
    eqs_klt = """
    iklt = gkltbar*w**4*z*(EK-v) : amp
    dw/dt=q10*(winf-w)/wtau : 1
    dz/dt=q10*(zinf-z)/ztau : 1
    winf = (1. / (1 + exp(-(vu + 48.) / 6.)))**0.25 : 1
    zinf = zss + ((1.-zss) / (1 + exp((vu + 71.) / 10.))) : 1
    wtau = ((100. / (6.*exp((vu+60.) / 6.) + 16.*exp(-(vu+60.) / 45.))) + 1.5)*ms : second
    ztau = ((1000. / (exp((vu+60.) / 20.) + exp(-(vu+60.) / 8.))) + 50)*ms : second
    """
    
    # Ka channel (transient K+)
    eqs_ka = """
    ika = gkabar*a**4*b*c*(EK-v): amp
    da/dt=q10*(ainf-a)/atau : 1
    db/dt=q10*(binf-b)/btau : 1
    dc/dt=q10*(cinf-c)/ctau : 1
    ainf = (1. / (1 + exp(-(vu + 31) / 6.)))**0.25 : 1
    binf = 1. / (1 + exp((vu + 66) / 7.))**0.5 : 1
    cinf = 1. / (1 + exp((vu + 66) / 7.))**0.5 : 1
    atau =  ((100. / (7*exp((vu+60) / 14.) + 29*exp(-(vu+60) / 24.))) + 0.1)*ms : second
    btau =  ((1000. / (14*exp((vu+60) / 27.) + 29*exp(-(vu+60) / 24.))) + 1)*ms : second
    ctau = ((90. / (1 + exp((-66-vu) / 17.))) + 10)*ms : second
    """
    
    # Leak
    eqs_leak = """
    ileak = gl*(El-v) : amp
    """
    
    # h current for octopus cells
    eqs_hcno = """
    ihcno = gbarno*(h1*frac + h2*(1-frac))*(Eh-v) : amp
    dh1/dt=(hinfno-h1)/tau1 : 1
    dh2/dt=(hinfno-h2)/tau2 : 1
    hinfno = 1./(1+exp((vu+66.)/7.)) : 1
    tau1 = bet1/(qt*0.008*(1+alp1))*ms : second
    tau2 = bet2/(qt*0.0029*(1+alp2))*ms : second
    alp1 = exp(1e-3*3*(vu+50)*9.648e4/(8.315*(273.16+temp))) : 1
    bet1 = exp(1e-3*3*0.3*(vu+50)*9.648e4/(8.315*(273.16+temp))) : 1
    alp2 = exp(1e-3*3*(vu+84)*9.648e4/(8.315*(273.16+temp))) : 1
    bet2 = exp(1e-3*3*0.6*(vu+84)*9.648e4/(8.315*(273.16+temp))) : 1
    """
    
    eqs = """
    dv/dt = (ileak + ina + ikht + iklt + ika + ih + ihcno + I)/C : volt
    vu = v/mV : 1  # unitless v
    I : amp
    """
    eqs += eqs_leak + eqs_ka + eqs_na + eqs_ih + eqs_klt + eqs_kht + eqs_hcno
    
    neuron = NeuronGroup(1, eqs, method='exponential_euler')
    neuron.v = El
    
    run(50*ms, report='text')  # Go to rest
    
    M = StateMonitor(neuron, 'v', record=0)
    neuron.I = Ipulse
    
    run(100*ms, report='text')
    
    plot(M.t / ms, M[0].v / mV)
    xlabel('t (ms)')
    ylabel('v (mV)')
    show()
    

