def analysis_and_write(params,
weights_path,
fig_directory,
run_name,
no_rec_noise=True):
from matplotlib.backends.backend_pdf import PdfPages
import os
import copy
original_params = copy.deepcopy(params)
if no_rec_noise:
params['rec_noise'] = 0.0
try:
os.stat(fig_directory)
except:
os.mkdir(fig_directory)
pp = PdfPages(fig_directory + '/' + run_name + '.pdf')
generator = generate_train_trials(params)
weights = np.load(weights_path)
W = weights['W_rec']
Win = weights['W_in']
Wout = weights['W_out']
brec = weights['b_rec']
data = generator.next()
sim = Simulator(params, weights_path=weights_path)
output, states = sim.run_trial(data[0][0, :, :], t_connectivity=False)
s = np.zeros([data[0].shape[1], data[0].shape[0], 100])
for ii in range(data[0].shape[0]):
s[:, ii, :] = sim.run_trial(data[0][ii, :, :],
t_connectivity=False)[1].reshape(
[data[0].shape[1], 100])
#Figure 0 (Plot Params)
fig0 = plot_params(original_params)
pp.savefig(fig0)
#Figure 1 (Single Trial (Input Output State))
fig1 = plot_single_trial(data, states, output)
pp.savefig(fig1)
#Figure 2 (Plot structural measures of W against random matrix R)
fig2 = plot_structure_Wrec(W)
pp.savefig(fig2)
#Figure 3 (Stupid Figure where activity is sorted by time of max firing rate)
fig3 = plt.figure(figsize=(7, 3))
plot_by_max(s[:, 0, :])
plt.xlabel('Time')
plt.ylabel('Neuron')
pp.savefig(fig3)
#Figure 4 (Principal Angle Analysis)
fig4 = plot_principal_angles(W, s, data)
pp.savefig(fig4)
#Figure 5 Plot long term state activity for in, and in+go conditions
fig5 = plot_long_term_state(sim)
pp.savefig(fig5)
#Figure 6 Plot PC projection
fig6 = plot_state_pcs(sim)
pp.savefig(fig6)
#Figure 7 Hamming Distance btw Putative Fixed Points
fig7 = plot_hamming_dist(s, W, brec)
pp.savefig(fig7)
#Figure 8 Plot long delayed go cue
fig8 = plot_long_delayed_go_cue(sim)
pp.savefig(fig8)
#Figure 9 Plot angles between input mapping and output mapping
fig9 = plot_input_output_angles(Win, W, Wout, brec)
pp.savefig(fig9)
#Figure 10 Plot biclustered recurrent weights
fig10 = plot_biclustered_weights(W)
pp.savefig(fig10)
#Figure 11 Plot biases
fig11 = plot_biases(weights)
pp.savefig(fig11)
#Figure 12 Plot Eigenspectrum
fig12 = plot_eig_dist(s[:, 0, :], W)
pp.savefig(fig12)
pp.close()
sess = tf.Session()
print('first training')
model.train(sess,
generator,
learning_rate=learning_rate,
training_iters=training_iters,
weights_path=weights_path)
#print('second training')
#model.train(sess, generator, learning_rate = learning_rate, training_iters = training_iters, weights_path = weights_path, initialize_variables=False)
data = generator.next()
#output,states = model.test(sess, input, weights_path = weights_path)
W = model.W_rec.eval(session=sess)
U = model.W_in.eval(session=sess)
Z = model.W_out.eval(session=sess)
brec = model.b_rec.eval(session=sess)
bout = model.b_out.eval(session=sess)
sim = Simulator(params, weights_path=weights_path)
output, states = sim.run_trial(data[0][0, :, :], t_connectivity=False)
s = np.zeros([data[0].shape[1], data[0].shape[0], 50])
for ii in range(data[0].shape[0]):
s[:, ii, :] = sim.run_trial(data[0][ii, :, :],
t_connectivity=False)[1].reshape(
[data[0].shape[1], 50])
sess.close()
def analysis_and_write(params,weights_path):
from matplotlib.backends.backend_pdf import PdfPages
import os
try:
os.stat('demo_figures')
except:
os.mkdir('demo_figures')
pp = PdfPages('demo_figures/demo_analysis_figures.pdf')
generator = generate_train_trials(params)
weights = np.load(weights_path)
W = weights['W_rec']
brec = weights['b_rec']
data = generator.next()
sim = Simulator(params, weights_path=weights_path)
output,states = sim.run_trial(data[0][0,:,:],t_connectivity=False)
s = np.zeros([data[0].shape[1],data[0].shape[0],100])
for ii in range(data[0].shape[0]):
s[:,ii,:] = sim.run_trial(data[0][ii,:,:],t_connectivity=False)[1].reshape([data[0].shape[1],100])
#Figure 1 (Single Trial (Input Output State))
fig1 = plt.figure(figsize=(5,5))
plt.subplot(3,1,1)
plt.plot(output[:,0,:])
plt.title('Out')
plt.subplot(3,1,2)
plt.plot(states[:,0,:])
plt.title('State')
plt.subplot(3,1,3)
plt.plot(data[0][0,:,:])
plt.title('Input')
plt.tight_layout()
pp.savefig(fig1)
#Figure 2 (Plot structural measures of W against random matrix R)
N = W.shape[0]
R = np.random.randn(N,N)/float(N)
R = 1.1*R/np.max(np.abs(np.linalg.eig(R)[0]))
#calculate the norm of trained rec matrix W and random gaussian matrix R
normW = calc_norm(W)
normR = calc_norm(R)
min_norm = np.min([np.min(normW),np.min(normR)])
max_norm = np.max([np.max(normW),np.max(normR)])
xx_norm = np.linspace(min_norm,max_norm,50)
histnormW, _ = np.histogram(normW,xx_norm)
histnormR, _ = np.histogram(normR,xx_norm)
#calculate hists for angles between columns
angle_W = np.arccos(np.clip((W.T.dot(W))/np.outer(normW,normW),-1.,1.))
angle_R = np.arccos(np.clip((R.T.dot(R))/np.outer(normR,normR),-1.,1.))
min_val = np.min([np.min(angle_W),np.min(angle_R)])
max_val = np.max([np.max(angle_W),np.max(angle_R)])
xx = np.linspace(min_val,max_val,50)
histW, bin_edgesW = np.histogram(angle_W[np.tril(np.ones_like(W),-1)>0],xx)
histR, bin_edgesR = np.histogram(angle_R[np.tril(np.ones_like(R),-1)>0],xx)
fig2 = plt.figure(figsize=(8,5))
plt.subplot(2,2,1)
plt.pcolormesh(angle_W)
plt.colorbar()
plt.title('$\measuredangle$ W')
plt.subplot(2,2,2)
plt.pcolormesh(angle_R)
plt.colorbar()
plt.title('$\measuredangle$ R')
plt.subplot(2,2,3)
plt.bar(xx[:-1],histW,width=bin_edgesW[1]-bin_edgesW[0])
plt.bar(xx[:-1],-histR,width=bin_edgesR[1]-bin_edgesR[0],color='g')
plt.legend(['W','Random'],fontsize=10,loc='lower left')
plt.title('Hist of Angles')
plt.subplot(2,2,4)
plt.bar(xx_norm[:-1],histnormW,width=xx_norm[1]-xx_norm[0])
plt.bar(xx_norm[:-1],-histnormR,width=xx_norm[1]-xx_norm[0],color='g')
plt.legend(['W','Random'],fontsize=10,loc='lower left')
plt.title('Hist of Norms')
plt.tight_layout()
pp.savefig(fig2)
#Figure 3 (Stupid Figure where activity is sorted by time of max firing rate)
fig3 = plt.figure(figsize=(7,3))
plot_by_max(s[:,0,:])
plt.xlabel('Time')
plt.ylabel('Neuron')
pp.savefig(fig3)
#Figure 4 (Principal Angle Analysis)
masks = s[:,0,:].T>0
max_ev = np.zeros(data[0].shape[1])
pos = []
neg = []
leading = []
for ii in range(data[0].shape[1]):
evals,evecs = np.linalg.eig(W*masks[:,ii]-np.eye(100))
max_ev[ii] = np.max(evals.real)
pos.append(evecs[:,evals>0])
neg.append(evecs[:,evals<0])
leading.append(evecs[:,np.argsort(np.abs(evals.real))[:10]]) #.reshape([100,2]))
xx = np.arange(0,data[0].shape[1],1)
pa = np.zeros([len(xx),len(xx)])
basis = leading
for ii,pre in enumerate(xx):
for jj,post in enumerate(xx):
if basis[pre].shape[1]*basis[post].shape[1]>0:
pas = principal_angle(basis[pre],basis[post])
pa[ii,jj] = np.nanmean(pas)
else:
pa[ii,jj] = 0.
fig4 = plt.figure()
plt.pcolormesh(pa,vmin=0,vmax=90)
plt.colorbar()
plt.ylim([0,pa.shape[0]])
plt.xlim([0,pa.shape[1]])
plt.title('Principal Angle Analysis')
plt.xlabel('Time')
plt.ylabel('Time')
pp.savefig(fig4)
#Figure 5 Plot long term state activity for in, and in+go conditions
d = .01*np.random.randn(2000,3)
d[50:60,0] = 1.
o_in0,s_in0 = sim.run_trial(d,t_connectivity=False)
d[50:60,1] = 1.
o_in1,s_in1 = sim.run_trial(d,t_connectivity=False)
d = .01*np.random.randn(2000,3)
d[50:60,0] = 1.
d[150:160,2] = 1.
o_go0,s_go0 = sim.run_trial(d,t_connectivity=False)
d[50:60,1] = 1.
d[150:160,2] = 1.
o_go1,s_go1 = sim.run_trial(d,t_connectivity=False)
fig5 = plt.figure(figsize=(8,6))
plt.subplot(4,2,1)
plt.plot(s_in1[:500,0,:]);
plt.title('Long Input 1')
plt.subplot(4,2,3)
plt.plot(s_in0[:500,0,:]);
plt.title('Long Input 2')
plt.subplot(4,2,5)
plt.plot(s_in0[:500,0,:] - s_in1[:500,0,:]);
plt.title('Difference')
plt.subplot(4,2,7)
plt.plot(o_in1[:500,0,:]);
plt.plot(o_in0[:500,0,:]);
plt.title('Output')
plt.subplot(4,2,2)
plt.plot(s_go0[:500,0,:]);
plt.title('Long Input 1 + Go Cue')
plt.subplot(4,2,4)
plt.plot(s_go1[:500,0,:]);
plt.title('Long Input 2 + Go Cue')
plt.subplot(4,2,6)
plt.plot(s_go0[:500,0,:] - s_go1[:500,0,:]);
plt.title('Difference')
plt.subplot(4,2,8)
plt.plot(o_go0[:500,0,:]);
plt.plot(o_go1[:500,0,:]);
plt.title('Output')
plt.tight_layout()
pp.savefig(fig5)
#Figure 6 Plot PC projection
s_pca = np.concatenate((s_go0[:500,0,:],s_go1[:500,0,:]),axis=0)
s_pca = demean(s_pca)
c_pca = np.cov(s_pca.T)
evals,evecs = np.linalg.eig(c_pca)
fig6 = plt.figure()
plt.plot(s_go0[:,0,:].dot(evecs[:,0:1]),s_go0[:,0,:].dot(evecs[:,1:2]),'g',alpha=.5)
plt.plot(s_go1[:,0,:].dot(evecs[:,0:1]),s_go1[:,0,:].dot(evecs[:,1:2]),'b',alpha=.5)
plt.plot(s_in0[:,0,:].dot(evecs[:,0:1]),s_in0[:,0,:].dot(evecs[:,1:2]),'c',alpha=.5)
plt.plot(s_in1[:,0,:].dot(evecs[:,0:1]),s_in1[:,0,:].dot(evecs[:,1:2]),'r',alpha=.5)
plt.plot(s_go1[:,0,:].dot(evecs[:,0:1])[0],s_go1[:,0,:].dot(evecs[:,1:2])[0],'kx',markersize=10)
plt.plot(s_go0[:,0,:].dot(evecs[:,0:1])[49],s_go0[:,0,:].dot(evecs[:,1:2])[49],'og',markersize=5)
plt.plot(s_go0[:,0,:].dot(evecs[:,0:1])[149],s_go0[:,0,:].dot(evecs[:,1:2])[149],'og',markersize=5)
plt.plot(s_go1[:,0,:].dot(evecs[:,0:1])[49],s_go1[:,0,:].dot(evecs[:,1:2])[49],'xb',markersize=8)
plt.plot(s_go1[:,0,:].dot(evecs[:,0:1])[149],s_go1[:,0,:].dot(evecs[:,1:2])[149],'xb',markersize=8)
plt.xlabel('pc1')
plt.ylabel('pc2')
plt.legend(['in_go_0','in_go_1','in_0','in_1'],loc='lower left',fontsize=8)
pp.savefig(fig6)
#Figure 7 Hamming Distance btw Putative Fixed Points
masks = s[:,0,:].T>0
x_hat = np.zeros(masks.shape)
for ii in range(masks.shape[1]):
Weff = W*masks[:,ii]
x_hat[:,ii] = np.linalg.inv(np.eye(100)-Weff).dot(brec)
fig7 = plt.figure()
plt.pcolormesh(squareform(pdist(np.sign(x_hat[:,:]).T,metric='hamming'))) #,vmax=.3)
plt.colorbar()
plt.ylim([0,x_hat.shape[1]])
plt.xlim([0,x_hat.shape[1]])
plt.title('Hamming Distance Between Putative FPs')
plt.ylabel('Time')
plt.xlabel('Time')
pp.savefig(fig7)
pp.close()
def analysis_and_write(params,weights_path,fig_directory,run_name,no_rec_noise=True):
from matplotlib.backends.backend_pdf import PdfPages
import os
import copy
original_params = copy.deepcopy(params)
if no_rec_noise:
params['rec_noise'] = 0.0
try:
os.stat(fig_directory)
except:
os.mkdir(fig_directory)
pp = PdfPages(fig_directory + '/' + run_name + '.pdf')
params['sample_size'] = 2000
generator = generate_train_trials(params)
weights = np.load(weights_path)
W = weights['W_rec']
Win = weights['W_in']
Wout = weights['W_out']
brec = weights['b_rec']
#Generate Input Data
data = generator.next()
#Find Input/Target One-Hot
inp = np.argmax(data[0][:,40,:],axis=1)
sim = Simulator(params, weights_path=weights_path)
output,states = sim.run_trial(data[0][0,:,:],t_connectivity=False)
n_in = n_out = data[0].shape[2]
n_rec = W.shape[0]
#generate trials
s = np.zeros([data[0].shape[1],data[0].shape[0],W.shape[0]])
for ii in range(data[0].shape[0]):
s[:,ii,:] = sim.run_trial(data[0][ii,:,:],t_connectivity=False)[1].reshape([data[0].shape[1],W.shape[0]])
#generate long duration trials
long_in = np.zeros([10000,n_in,n_in])
for ii in range(n_in):
long_in[10:80,ii,ii] = 1
s_long = np.zeros([long_in.shape[0],long_in.shape[1],W.shape[0]])
for ii in range(n_in):
s_long[:,ii,:] = sim.run_trial(long_in[:,ii,:],t_connectivity=False)[1].reshape([long_in.shape[0],W.shape[0]])
#Figure 0 (Plot Params)
fig0 = plot_params(original_params)
pp.savefig(fig0)
#Figure 1 (Single Trial (Input Output State))
fig1 = plot_single_trial(data,states,output)
pp.savefig(fig1)
#Figure 2 (plot fixed points - activity at end of trial)
fig2 = plot_fps_vs_activity(s,W,brec)
pp.savefig(fig2)
#Figure 3 (Plot output activity)
try:
fig3 = plot_outputs_by_input(s,data,weights,n=Win.shape[1])
pp.savefig(fig3)
except Exception:
pass
#Figure 4 (Plot 2D PCA projection)
fig4 = pca_plot(n_in,s_long,s,inp,brec)
pp.savefig(fig4)
#Figure5 (Plot Long Output)
fig5 = plot_long_output_by_input(n_in,n_rec,s_long,weights)
pp.savefig(fig5)
#Figure6 (Plot ablation analysis)
fig6 = ablation_analysis(n_rec,n_in,weights,sim)
pp.savefig(fig6)
#Figure7 (Plot W Structure)
fig7 = plot_structure_Wrec(W)
pp.savefig(fig7)
#Figure8 (Bar Plot of distance to nearest partition)
fig8 = plot_dist_to_fp(s_long)
pp.savefig(fig8)
fig9 = plot_fp_partitions(s_long)
pp.savefig(fig9)
pp.close()
model.train(sess,
generator,
learning_rate=learning_rate,
training_iters=training_iters,
weights_path=weights_path,
display_step=display_step)
data = generator.next()
#W = model.W_rec.eval(session=sess)
#U = model.W_in.eval(session=sess)
#Z = model.W_out.eval(session=sess)
#brec = model.b_rec.eval(session=sess)
#bout = model.b_out.eval(session=sess)
sim = Simulator(params, weights_path=weights_path)
output, states = sim.run_trial(data[0][0, :, :], t_connectivity=False)
x_test, y_test, mask = build_test_trials(params)
mup, mdown, choice, resp = white_noise_test(sim, x_test)
coh_out = coherence_test(sim, np.arange(-.2, .2, .01))
for i in range(5):
trial = data[0][i, :, :]
points = analysis.hahnloser_fixed_point(sim, trial)
analysis.plot_states(states=states, I=points)
sess.close()
generator,
learning_rate=learning_rate,
training_iters=training_iters,
weights_path=weights_path,
display_step=display_step)
data = generator.next()
#output,states = model.test(sess, input, weights_path = weights_path)
W = model.W_rec.eval(session=sess)
U = model.W_in.eval(session=sess)
Z = model.W_out.eval(session=sess)
brec = model.b_rec.eval(session=sess)
bout = model.b_out.eval(session=sess)
sim = Simulator(params, weights_path=weights_path)
output, states = sim.run_trial(data[0][0, :, :], t_connectivity=False)
s = np.zeros([states.shape[0], batch_size, n_hidden])
for ii in range(batch_size):
s[:, ii, :] = sim.run_trial(data[0][ii, :, :],
t_connectivity=False)[1].reshape(
[states.shape[0], n_hidden])
n_noise = 1000
s_noise = np.zeros([states.shape[0], n_noise, n_hidden])
data_noise = stim_noise * np.random.randn(n_noise, states.shape[0], n_in)
for ii in range(n_noise):
s_noise[:, ii, :] = sim.run_trial(data_noise[ii, :, :],
t_connectivity=False)[1].reshape(
[states.shape[0], n_hidden])