cut_off = 1.
max_freq = np.zeros((len(ei_ratio), len(l3l5_ratio)))
freq_pow = np.zeros_like(max_freq)
for k1, k2, k3 in zip(params['k_l3l5_e'], params['k_l3l5_i'], params['k_l5l3_i']):
if not results[k1][k2][k3].isnull().any().any():
_ = plot_psd(results[k1][k2][k3], tmin=cut_off, show=False)
pow = plt.gca().get_lines()[-1].get_ydata()
freqs = plt.gca().get_lines()[-1].get_xdata()
r, c = np.argmin(np.abs(ei_ratio - k1/k2)), np.argmin(np.abs(l3l5_ratio - k2/k3))
max_freq[r, c] = freqs[np.argmax(pow)]
freq_pow[r, c] = np.max(pow)
plt.close(plt.gcf())
cm1 = cubehelix_palette(n_colors=int(len(ei_ratio)*len(l3l5_ratio)), as_cmap=True, start=2.5, rot=-0.1)
cm2 = cubehelix_palette(n_colors=int(len(ei_ratio)*len(l3l5_ratio)), as_cmap=True, start=-2.0, rot=-0.1)
cax1 = plot_connectivity(max_freq, ax=ax[0, idx], yticklabels=list(np.round(ei_ratio, decimals=2)),
xticklabels=list(np.round(l3l5_ratio, decimals=2)), cmap=cm1)
cax1.set_xlabel('L3/L5 coupling strength')
cax1.set_ylabel('exc/inh coupling strength')
cax1.set_title(f'max freq (C = {C})')
cax2 = plot_connectivity(freq_pow, ax=ax[1, idx], yticklabels=list(np.round(ei_ratio, decimals=2)),
xticklabels=list(np.round(l3l5_ratio, decimals=2)), cmap=cm2)
cax2.set_xlabel('L3/L5 coupling strength')
cax2.set_ylabel('exc/inh coupling strength')
cax2.set_title(f'freq power (C = {C})')
plt.suptitle('Cortical microcircuit sensitivity to inter-laminar coupling')
plt.tight_layout(pad=2.5, rect=(0.01, 0.01, 0.99, 0.96))
fig.savefig("/home/rgast/Documents/Studium/PhD_Leipzig/Figures/BGTCS/cmc_coupling", format="svg")
plt.show()
# target values
targets = config['targets']
# STEP 2: Go through results files and store scores
###################################################
etas, alphas, scores = [], [], []
for f in [f for f in os.listdir(path) if '.npy' in f]:
idx = int(f.split(".")[0].split("_")[-1])
alphas.append(config['alphas'][idx])
etas.append(config['etas'][idx])
scores.append(np.load(f"{path}/{f}"))
# STEP 3: Visualization
#######################
# visualization
etas_unique = np.sort(np.unique(etas))
alphas_unique = np.sort(np.unique(alphas))
scores_2d = np.zeros((len(alphas_unique), len(etas_unique)))
for eta, alpha, score in zip(etas, alphas, scores):
idx_c = np.argwhere(etas_unique == eta)
idx_r = np.argwhere(alphas_unique == alpha)
if score >= 0.0:
scores_2d[idx_r, idx_c] = score
plot_connectivity(scores_2d,
xticklabels=np.round(etas_unique, decimals=2),
yticklabels=np.round(alphas_unique, decimals=2))
show()
idx_r = np.argmin(np.abs(param_grid['d'] - r_map.at[idx, 'd']))
idx_c = np.argmin(np.abs(param_grid['s'] - r_map.at[idx, 's']))
r = r2.loc[:, ('r', f"{idx}/{out_var}")]
diff = r - r1.loc[:, 'r']
d, s = r_map.loc[idx, 'd'], r_map.loc[idx, 's']
order = int(np.round((d / s)**2))
error[idx_r, idx_c] = alpha * np.sqrt(diff.T @ diff) + (1 - alpha) * order
print(
f"delay = {d}, spread = {s}, order = {order}, rate = {order/d}, error = {error[idx_r, idx_c]}"
)
indices[idx_r.squeeze()][idx_c.squeeze()] = idx
# display error
fig, ax = plt.subplots()
ax = plot_connectivity(error,
xticklabels=param_grid['s'],
yticklabels=param_grid['d'],
ax=ax)
ax.set_xlabel('s')
ax.set_ylabel('d')
plt.tight_layout()
# display winner together with target
fig2, ax2 = plt.subplots()
winner = np.argmin(error)
idx = np.asarray(indices).flatten()[winner]
ax2.plot(r1.loc[:, 'r'])
ax2.plot(r2.loc[:, ('r', f"{idx}/{out_var}")])
plt.legend(['discrete', 'gamma'])
ax2.set_title(
f"delay = {r_map.loc[idx, 'd']}, spread = {r_map.loc[idx, 's']}, error = {error.flatten()[winner]}"
)
max_freq[i, j] = freqs[np.argmax(pow)]
freq_pow[i, j] = np.max(pow)
plt.close('all')
fig, ax = plt.subplots(ncols=2, figsize=(15, 5), gridspec_kw={})
cm1 = cubehelix_palette(n_colors=int(len(params['H_e']) * len(params['H_i'])),
as_cmap=True,
start=2.5,
rot=-0.1)
cm2 = cubehelix_palette(n_colors=int(len(params['H_e']) * len(params['H_i'])),
as_cmap=True,
start=-2.0,
rot=-0.1)
cax1 = plot_connectivity(max_freq,
ax=ax[0],
yticklabels=list(np.round(params['H_e'], decimals=2)),
xticklabels=list(np.round(params['H_i'], decimals=2)),
cmap=cm1)
cax1.set_xlabel('H_i')
cax1.set_ylabel('H_e')
cax1.set_title(f'max freq')
cax2 = plot_connectivity(freq_pow,
ax=ax[1],
yticklabels=list(np.round(params['H_e'], decimals=2)),
xticklabels=list(np.round(params['H_i'], decimals=2)),
cmap=cm2)
cax2.set_xlabel('H_i')
cax2.set_ylabel('H_e')
cax2.set_title(f'freq pow')
plt.suptitle('EI-circuit sensitivity to synaptic efficacies (H)')
plt.tight_layout(pad=2.5, rect=(0.01, 0.01, 0.99, 0.96))
# load data
path = "/home/rgast/MatlabProjects/STN_GPe/PAC_bistable.mat"
data = scio.loadmat(path)
# extract from data
alpha = data['stim_amps_unique']
omega = data['stim_freqs_unique']
MI = data['PAC_max']
PAA_osc = data['PAA_osc']
PAA_env = data['PAA_env']
PAA = PAA_osc / PAA_env
# plot MI
cmap = create_cmap("pyrates_blue", n_colors=64, as_cmap=False, reverse=False)
ax = plot_connectivity(MI, cmap=cmap)
ax.set_xticks(ax.get_xticks()[0::2])
ax.set_yticks(ax.get_yticks()[0::2])
ax.set_xticklabels(np.round(omega.squeeze(), decimals=1)[0::2],
rotation='horizontal')
ax.set_yticklabels(np.round(alpha.squeeze(), decimals=1)[0::2],
rotation='horizontal')
ax.set_xlabel('omega')
ax.set_ylabel('alpha')
ax.invert_yaxis()
plt.tight_layout()
plt.savefig('MI_bs.svg')
plt.show()
# plot PAA
cmap = create_cmap("pyrates_blue", n_colors=64, as_cmap=False, reverse=False)
# calculate order parameter (sync)
w = np.pi * rates.values + 1.0j * potentials.values
sync = (1 - w) / (1 + w)
# store results in 2D arrays
col = np.argmin(np.abs(etas - eta))
row = np.argmin(np.abs(alphas - alpha))
sync_mean_2D[row, col] = np.mean(np.abs(sync))
sync_var_2D[row, col] = np.var(np.abs(sync))
# visualization of kuramoto order parameter
###########################################
# 2D
fig2, ax2 = plt.subplots(ncols=2, figsize=(14, 6))
ax = ax2[0]
ax = plot_connectivity(sync_mean_2D,
yticklabels=np.round(alphas, decimals=1),
xticklabels=np.round(etas, decimals=1),
ax=ax)
ax.set_title('mean of synchrony')
ax = ax2[1]
ax = plot_connectivity(sync_var_2D,
yticklabels=np.round(alphas, decimals=1),
xticklabels=np.round(etas, decimals=1),
ax=ax)
ax.set_title('var of synchrony')
plt.tight_layout()
plt.show()
param_map = {'eta': {'vars': ['Op_sfa_syns_noise/eta'], 'nodes': ['qif']},
'Js': {'vars': ['Op_sfa_syns_noise/J'], 'nodes': ['qif']}
}
# simulation
############
results, result_map = grid_search(circuit_template="qifs/QIF_sfa_syns_noise",
param_grid=params,
param_map=param_map,
inputs={},
outputs={"r": "qif/Op_sfa_syns_noise/r", "v": "qif/Op_sfa_syns_noise/v"},
step_size=dt, simulation_time=T, permute_grid=True, sampling_step_size=dts,
method='RK45')
# visualization
###############
sigmas = np.zeros((len(Js), len(etas)))
for i, J in enumerate(Js[::-1]):
for j, eta in enumerate(etas):
idx = result_map[result_map['eta'] == eta and result_map['J'] == J]
r = results.loc[:, ('r', idx)]
v = results.loc[:, ('v', idx)]
w = np.complex(real=np.pi*r, imag=-v)
sigmas[i, j] = (1-w)/(1+w)
plot_connectivity(sigmas, xticklabels=np.round(etas, decimals=2),
yticklabels=np.round(Js[::-1], decimals=2))
show()
