本文整理汇总了Python中brian2.NeuronGroup.I_stim方法的典型用法代码示例。如果您正苦于以下问题：Python NeuronGroup.I_stim方法的具体用法？Python NeuronGroup.I_stim怎么用？Python NeuronGroup.I_stim使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类brian2.NeuronGroup的用法示例。
在下文中一共展示了NeuronGroup.I_stim方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: simulate_wm
# 需要导入模块: from brian2 import NeuronGroup [as 别名]
# 或者: from brian2.NeuronGroup import I_stim [as 别名]
#.........这里部分代码省略.........
weight_profile_45 = deque(presyn_weight_kernel)
rot_dist = int(round(len(weight_profile_45) / 8))
weight_profile_45.rotate(rot_dist)
# define the inhibitory population
inhib_lif_dynamics = """
s_NMDA_total : 1 # the post synaptic sum of s. compare with s_NMDA_presyn
dv/dt = (
- G_leak_inhib * (v-E_leak_inhib)
- G_extern2inhib * s_AMPA * (v-E_AMPA)
- G_inhib2inhib * s_GABA * (v-E_GABA)
- G_excit2inhib * s_NMDA_total * (v-E_NMDA)/(1.0+1.0*exp(-0.062*v/volt)/3.57)
)/Cm_inhib : volt (unless refractory)
ds_AMPA/dt = -s_AMPA/tau_AMPA : 1
ds_GABA/dt = -s_GABA/tau_GABA : 1
"""
inhib_pop = NeuronGroup(
N_inhibitory, model=inhib_lif_dynamics,
threshold="v>v_firing_threshold_inhib", reset="v=v_reset_inhib", refractory=t_abs_refract_inhib,
method="rk2")
# initialize with random voltages:
inhib_pop.v = numpy.random.uniform(v_reset_inhib / b2.mV, high=v_firing_threshold_inhib / b2.mV,
size=N_inhibitory) * b2.mV
# set the connections: inhib2inhib
syn_inhib2inhib = Synapses(inhib_pop, target=inhib_pop, on_pre="s_GABA += 1.0", delay=0.0 * b2.ms)
syn_inhib2inhib.connect(condition="i!=j", p=1.0)
# set the connections: extern2inhib
input_ext2inhib = PoissonInput(target=inhib_pop, target_var="s_AMPA",
N=N_extern_poisson, rate=poisson_firing_rate, weight=1.0)
# specify the excitatory population:
excit_lif_dynamics = """
I_stim : amp
s_NMDA_total : 1 # the post synaptic sum of s. compare with s_NMDA_presyn
dv/dt = (
- G_leak_excit * (v-E_leak_excit)
- G_extern2excit * s_AMPA * (v-E_AMPA)
- G_inhib2excit * s_GABA * (v-E_GABA)
- G_excit2excit * s_NMDA_total * (v-E_NMDA)/(1.0+1.0*exp(-0.062*v/volt)/3.57)
+ I_stim
)/Cm_excit : volt (unless refractory)
ds_AMPA/dt = -s_AMPA/tau_AMPA : 1
ds_GABA/dt = -s_GABA/tau_GABA : 1
ds_NMDA/dt = -s_NMDA/tau_NMDA_s + alpha_NMDA * x * (1-s_NMDA) : 1
dx/dt = -x/tau_NMDA_x : 1
"""
excit_pop = NeuronGroup(N_excitatory, model=excit_lif_dynamics,
threshold="v>v_firing_threshold_excit", reset="v=v_reset_excit; x+=1.0",
refractory=t_abs_refract_excit, method="rk2")
# initialize with random voltages:
excit_pop.v = numpy.random.uniform(v_reset_excit / b2.mV, high=v_firing_threshold_excit / b2.mV,
size=N_excitatory) * b2.mV
excit_pop.I_stim = 0. * b2.namp
# set the connections: extern2excit
input_ext2excit = PoissonInput(target=excit_pop, target_var="s_AMPA",
N=N_extern_poisson, rate=poisson_firing_rate, weight=1.0)
# set the connections: inhibitory to excitatory
syn_inhib2excit = Synapses(inhib_pop, target=excit_pop, on_pre="s_GABA += 1.0")
syn_inhib2excit.connect(p=1.0)
# set the connections: excitatory to inhibitory NMDA connections
syn_excit2inhib = Synapses(excit_pop, inhib_pop,
model="s_NMDA_total_post = s_NMDA_pre : 1 (summed)", method="rk2")