def restriction_3(msn_rate, gpe_rate, stn_rate, n_msn, n_gpe, x, neuron_model,
syn_models, n_exp):
# 3. 30 Hz normal, GPe at 30 Hz, STN at 10 Hz
c, w = x
target_rate3 = 30.0
SNR_list = simulate_basa_line_SNr_multiple(
msn_rate,
gpe_rate,
stn_rate,
n_msn,
n_gpe,
n_stn,
neuron_model,
syn_models,
c, # This is the current to add to I_e base
sim_time,
8,
w_STN_SNR=w,
seed=n_exp,
record_vm=True,
n_neurons=n_exp)
r_SNR = []
for SNR in SNR_list:
r_SNR.append(SNR.signals['spikes'].mean_rate(1000, sim_time))
r = numpy.array(r_SNR)
mr = numpy.mean(r, axis=0)
std = numpy.std(r, axis=0)
e = mr - target_rate3
#Normalize error
e = e / target_rate3
return SNR_list[0], mr, std, e
def restriction_1(msn_rate,
gpe_rate,
stn_rate_ch,
n_msn,
n_gpe,
x,
neuron_model,
syn_models,
n_exp,
record_vm=True):
# 1. With no GPe input (still with MSN input) and STN at 10 Hz (since in the
# experiment the firing rate of STN were not recorded we can not know if it
# was increased or not/and to what spatial extend, could be that SNr recieves
# increased input due to increased STN. However fitting with constatn
# synapses STN to SNr makes it difficult the hit the target rates) to are silent) the fire at
# 312 % if baseline (30*3=90 Hz)s. REMARK: If STN at 20 then to be able to
# to come close to restriction STN needs to be depressing onto SNr
c, w = x
target_rate1 = 30 * 3.12
n_gpe_ch = 0
#stn_rate_ch=20.0
SNR_list = simulate_basa_line_SNr_multiple(
msn_rate,
gpe_rate,
stn_rate_ch,
n_msn,
n_gpe_ch,
n_stn,
neuron_model,
syn_models,
c, # This is the current to add to I_e base
sim_time,
8,
w_STN_SNR=w,
seed=n_exp,
record_vm=record_vm,
n_neurons=n_exp)
r_SNR = []
for SNR in SNR_list:
r_SNR.append(SNR.signals['spikes'].mean_rate(1000, sim_time))
r = numpy.array(r_SNR)
mr = numpy.mean(r, axis=0)
std = numpy.std(r, axis=0)
e = mr - target_rate1
#Normalize error
e = e / target_rate1
return SNR_list[0], mr, std, e
def restriction_2(msn_rate,
gpe_rate,
stn_rate,
n_msn,
n_gpe,
x,
neuron_model,
syn_models,
n_exp,
record_vm=True):
# 2. 15 Hz without STN. OBS Since stn is of the GPE should fire at 15 Hz
c, w = x
target_rate2 = 15.0
stn_rate_ch = 0.
gpe_rate_ch = 15.0 # Since stn is of the GPE should fire at 15 Hz
r_SNR = []
SNR_list = simulate_basa_line_SNr_multiple(
msn_rate,
gpe_rate_ch,
stn_rate_ch,
n_msn,
n_gpe,
n_stn,
neuron_model,
syn_models,
c, # This is the current to add to I_e base
sim_time,
8,
w_STN_SNR=w,
seed=n_exp,
record_vm=record_vm,
n_neurons=n_exp)
r_SNR = []
for SNR in SNR_list:
r_SNR.append(SNR.signals['spikes'].mean_rate(1000, sim_time))
r = numpy.array(r_SNR)
mr = numpy.mean(r, axis=0)
std = numpy.std(r, axis=0)
e = mr - target_rate2
#Normalize error
e = e / target_rate2
return SNR_list[0], mr, std, e