NewsomeTask.py 文件源码

def analytic_twopath(self,p,rate1,rate2):
        '''
        Population of neurons receive input from two pathways, the first path is gated-on
        rate1 and rate2 are the input rate of each pathway
        First we need to convert the input rate into conductance,
        the dend_IO(exc, inh) function takes total excitatory and inhibitory
        conductances as inputs
        '''
        # number of synapses
        num_syn = 15
        g_exc = p['g_exc']*num_syn
        # gating variable
        s1 = MCM.meansNMDA(rate1)
        s2 = MCM.meansNMDA(rate2)

        # Total conductance input
        Exc1 = self.Exc1_raw*s1*g_exc # nS
        Exc2 = self.Exc2_raw*s2*g_exc # nS

        Exc = Exc1+Exc2

        #frac_proj = 0.1 # fraction projection
        N_proj = p['frac_proj']*self.params['n_pyr']
        N_proj0 = np.floor(N_proj)
        N_proj0 = min((N_proj0,self.params['n_pyr']-1))
        N_proj0 = max((N_proj0,0))

        DendV = dend_IO(Exc[:(N_proj0+1)*self.params['n_dend_each']],
                            self.Inh1[:(N_proj0+1)*self.params['n_dend_each']])
        meanDendV = DendV.reshape(N_proj0+1,self.params['n_dend_each']).mean(axis=1)
        SomaR = soma_fv(meanDendV)

        # Make sure firing rate depend smoothly on frac_proj
        rboth = (SomaR[:N_proj0].sum()+SomaR[N_proj0]*(N_proj-N_proj0))/N_proj

        return rboth