def _compute_J(self):
<EOL>tm = self.model_params['<STR_LIT>']<EOL>Cm = self.model_params['<STR_LIT>']<EOL>ts = self.model_params['<STR_LIT>']<EOL>Vmax = self.PSP_e<EOL>J = Cm*Vmax*(-tm + ts)/(tm*ts*(np.exp(tm*np.log(ts/tm)/(tm - ts))<EOL>- np.exp(ts*np.log(ts/tm)/(tm - ts))))<EOL>J *= <NUM_LIT><EOL>return J<EOL>
Compute the current amplitude corresponding to the exponential
synapse model PSP amplitude

Derivation using sympy:
::
    from sympy import *
    #define symbols
    t, tm, Cm, ts, Is, Vmax = symbols('t tm Cm ts Is Vmax')

    #assume zero delay, t >= 0
    #using eq. 8.10 in Sterrat et al
    V = tm*ts*Is*(exp(-t/tm) - exp(-t/ts)) / (tm-ts) / Cm
    print 'V = %s' % V

    #find time of V == Vmax
    dVdt = diff(V, t)
    print 'dVdt = %s' % dVdt

    [t] = solve(dVdt, t)
    print 't(t@dVdT==Vmax) = %s' % t

    #solve for Is at time of maxima
    V = tm*ts*Is*(exp(-t/tm) - exp(-t/ts)) / (tm-ts) / Cm
    print 'V(%s) = %s' % (t, V)

    [Is] = solve(V-Vmax, Is)
    print 'Is = %s' % Is

resulting in:
::
    Cm*Vmax*(-tm + ts)/(tm*ts*(exp(tm*log(ts/tm)/(tm - ts))
                             - exp(ts*log(ts/tm)/(tm - ts))))