type2 = -1 # OFF-type neuron
a = 1.0/0.015
c = 1.0/3.0
h_list = [[0,0],[0,0]]
h_list[0][0] = lambda t : 0
h_list[0][1] = lambda t : -c*np.exp(-a*t)*((a*t)**5/120.0-(a*t)**7/5040.0)*(t>=0)
h_list[1][0] = lambda t : c*np.exp(-a*t)*((a*t)**5/120.0-(a*t)**7/5040.0)*(t>=0)
h_list[1][1] = lambda t : 0
fig_title = 'Signal Encoded Using Coupled IAF Encoder'
print fig_title
s_list = func_timer(iaf.iaf_encode_coupled)(u, dt, [b1, b2], [d1, d2],
[k1, k2], h_list, [type1, type2])
output_count += 1
pl.plot_encoded(t, u, s_list[0], fig_title + ' #1',
output_name + str(output_count) + output_ext)
output_count += 1
pl.plot_encoded(t, u, s_list[1], fig_title + ' #2',
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Coupled IAF Decoder'
print fig_title
u_rec = func_timer(iaf.iaf_decode_coupled)(s_list, dur, dt,
[b1, b2], [d1, d2], [k1, k2],
h_list)
pl.plot_compare(t, u, u_rec, fig_title,
output_name + str(output_count) + output_ext)
b1 = 3.5 # bias
d1 = 0.7 # threshold
R1 = 10.0 # resistance
C1 = 0.01 # capacitance
b2 = 3.4 # bias
d2 = 0.8 # threshold
R2 = 9.0 # resistance
C2 = 0.01 # capacitance
output_count += 1
fig_title = 'Signal Encoded Using Leaky IAF Encoder #1'
print fig_title
s1 = func_timer(iaf.iaf_encode)(u, dt, b1, d1, R1, C1)
pl.plot_encoded(t, u, s1, fig_title,
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Encoded Using Leaky IAF Encoder #2'
print fig_title
s2 = func_timer(iaf.iaf_encode)(u, dt, b2, d2, R2, C2)
pl.plot_encoded(t, u, s2, fig_title,
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Leaky\nSpline Interpolation IAF Population Decoder'
print fig_title
u_rec = func_timer(iaf.iaf_decode_spline_pop)([s1, s2], dur, dt,
[b1, b2], [d1, d2], [R1, R2],
[C1, C2])
pl.plot_compare(t, u, u_rec, fig_title,
b = 3.5 # bias
d = 0.7 # threshold
k = 0.01 # scaling factor
M = 5 # number of bins for fast decoding algorithm
try:
asdm.asdm_recoverable(u, bw, b, d, k)
except ValueError('reconstruction condition not satisfied'):
sys.exit()
output_count += 1
fig_title = 'Signal Encoded Using ASDM Encoder'
print fig_title
s = func_timer(asdm.asdm_encode)(u, dt, b, d, k)
pl.plot_encoded(t, u, s, fig_title,
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using ASDM Decoder'
print fig_title
u_rec = func_timer(asdm.asdm_decode)(s, dur, dt, bw, b, d, k)
pl.plot_compare(t, u, u_rec, fig_title,
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Threshold-Insensitive ASDM Decoder'
print fig_title
u_rec_ins = func_timer(asdm.asdm_decode_ins)(s, dur, dt, bw, b)
pl.plot_compare(t, u, u_rec_ins, fig_title,
output_name + str(output_count) + output_ext)
a_list = map(list, np.reshape(np.random.exponential(0.003, N * M), (N, M)))
w_list = map(list, np.reshape(randu(0.5, 1.0, N * M), (N, M)))
T_block = T / 2.0
T_overlap = T_block / 3.0
fig_title = 'Signal Encoded Using Real-Time Delayed IAF Encoder'
print fig_title
s_list = func_timer(rt.iaf_encode_delay)(u_list, T_block, t_start, dt, b_list,
d_list, k_list, a_list, w_list)
for i in xrange(M):
for j in xrange(N):
fig_title_out = fig_title + '\n(Signal #' + str(i+1) + \
', Neuron #' + str(j+1) + ')'
pl.plot_encoded(t_enc, u_list[i][k_start:k_end],
s_list[j][np.cumsum(s_list[j]) < T], fig_title_out,
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Real-Time Delayed IAF Decoder'
print fig_title
u_rec_list = func_timer(rt.iaf_decode_delay)(s_list, T_block, T_overlap, dt,
b_list, d_list, k_list, a_list,
w_list)
for i in xrange(M):
fig_title_out = fig_title + ' (Signal #' + str(i + 1) + ')'
pl.plot_compare(t_enc, u_list[i][k_start:k_end],
u_rec_list[i][0:k_end - k_start], fig_title_out,
output_name + str(output_count) + output_ext)
output_count += 1
type2 = -1 # OFF-type neuron
a = 1.0 / 0.015
c = 1.0 / 3.0
h_list = [[0, 0], [0, 0]]
h_list[0][0] = lambda t: 0
h_list[0][1] = lambda t: -c * np.exp(-a * t) * ((a * t)**5 / 120.0 -
(a * t)**7 / 5040.0) * (t >= 0)
h_list[1][0] = lambda t: c * np.exp(-a * t) * ((a * t)**5 / 120.0 -
(a * t)**7 / 5040.0) * (t >= 0)
h_list[1][1] = lambda t: 0
fig_title = 'Signal Encoded Using Coupled IAF Encoder'
print fig_title
s_list = func_timer(iaf.iaf_encode_coupled)(u, dt, [b1, b2], [d1, d2],
[k1, k2], h_list, [type1, type2])
output_count += 1
pl.plot_encoded(t, u, s_list[0], fig_title + ' #1',
output_name + str(output_count) + output_ext)
output_count += 1
pl.plot_encoded(t, u, s_list[1], fig_title + ' #2',
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Coupled IAF Decoder'
print fig_title
u_rec = func_timer(iaf.iaf_decode_coupled)(s_list, dur, dt, [b1, b2], [d1, d2],
[k1, k2], h_list)
pl.plot_compare(t, u, u_rec, fig_title,
output_name + str(output_count) + output_ext)
u_gpu = gpuarray.to_gpu(u)
b_gpu = gpuarray.to_gpu(np.array([b1, b2]))
d_gpu = gpuarray.to_gpu(np.array([d1, d2]))
R_gpu = gpuarray.to_gpu(np.array([R1, R2]))
C_gpu = gpuarray.to_gpu(np.array([C1, C2]))
output_count += 1
fig_title = 'Signal Encoded Using Leaky IAF Encoder'
print fig_title
s_gpu, ns_gpu = func_timer(iaf_trig_cuda.iaf_encode_pop)(u_gpu, dt,
b_gpu, d_gpu, R_gpu, C_gpu)
s = s_gpu.get()
ns = ns_gpu.get()
pl.plot_encoded(t, u, s[0,0:ns[0]], fig_title + ' #1',
output_name + str(output_count) + output_ext)
output_count += 1
pl.plot_encoded(t, u, s[1,0:ns[1]], fig_title + ' #2',
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Leaky IAF Population Decoder'
print fig_title
u_rec = func_timer(iaf_trig_cuda.iaf_decode_pop)(s_gpu, ns_gpu, dur, dt, bw,
b_gpu, d_gpu, R_gpu,
C_gpu, M)
pl.plot_compare(t, u, u_rec, fig_title,
output_name + str(output_count) + output_ext)
# Test ideal IAF algorithms:
u_gpu = gpuarray.to_gpu(u)
b_gpu = gpuarray.to_gpu(np.array([b1, b2]))
d_gpu = gpuarray.to_gpu(np.array([d1, d2]))
R_gpu = gpuarray.to_gpu(np.array([R1, R2]))
C_gpu = gpuarray.to_gpu(np.array([C1, C2]))
output_count += 1
fig_title = 'Signal Encoded Using Leaky IAF Encoder'
print fig_title
s_gpu, ns_gpu = func_timer(iaf_trig_cuda.iaf_encode_pop)(u_gpu, dt, b_gpu,
d_gpu, R_gpu, C_gpu)
s = s_gpu.get()
ns = ns_gpu.get()
pl.plot_encoded(t, u, s[0, 0:ns[0]], fig_title + ' #1',
output_name + str(output_count) + output_ext)
output_count += 1
pl.plot_encoded(t, u, s[1, 0:ns[1]], fig_title + ' #2',
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Leaky IAF Population Decoder'
print fig_title
u_rec = func_timer(iaf_trig_cuda.iaf_decode_pop)(s_gpu, ns_gpu, dur, dt, bw,
b_gpu, d_gpu, R_gpu, C_gpu, M)
pl.plot_compare(t, u, u_rec, fig_title,
output_name + str(output_count) + output_ext)
# Test ideal IAF algorithms:
b1 = 7.5 # bias
d_list = list(randu(0.15, 0.25, N))
k_list = list(0.01*np.ones(N))
a_list = map(list, np.reshape(np.random.exponential(0.003, N*M), (N, M)))
w_list = map(list, np.reshape(randu(0.5, 1.0, N*M), (N, M)))
fig_title = 'Signal Encoded Using Delayed IAF Encoder'
print fig_title
s_list = func_timer(iaf.iaf_encode_delay)(u_list, t_start, dt, b_list, d_list,
k_list, a_list, w_list)
for i in xrange(M):
for j in xrange(N):
fig_title_out = fig_title + '\n(Signal #' + str(i+1) + \
', Neuron #' + str(j+1) + ')'
pl.plot_encoded(t_enc, u_list[i][k_start:k_end],
s_list[j][np.cumsum(s_list[j])<T],
fig_title_out,
output_name + str(output_count) + output_ext)
output_count += 1
fig_title = 'Signal Decoded Using Delayed IAF Decoder'
print fig_title
u_rec_list = func_timer(iaf.iaf_decode_delay)(s_list, T, dt,
b_list, d_list, k_list,
a_list, w_list)
for i in xrange(M):
fig_title_out = fig_title + ' (Signal #' + str(i+1) + ')'
pl.plot_compare(t_enc, u_list[i][k_start:k_end],
u_rec_list[i][0:k_end-k_start], fig_title_out,
output_name + str(output_count) + output_ext)
output_count += 1
np.random.seed(0)
noise_power = None
fig_title = 'IAF input signal'
print fig_title
u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power)
pl.plot_signal(t, u, fig_title, output_dir + 'overview_input' + output_ext)
b = 3.5 # bias
d = 0.7 # threshold
R = np.inf # resistance
C = 0.01 # capacitance
try:
iaf.iaf_recoverable(u, bw, b, d, R, C)
except ValueError('reconstruction condition not satisfied'):
sys.exit()
fig_title = 'Signal encoded by IAF neuron'
print fig_title
s = func_timer(iaf.iaf_encode)(u, dt, b, d, R, C)
pl.plot_encoded(t, u, s, fig_title,
output_dir + 'overview_encoded' + output_ext)
fig_title = 'Decoded signal and reconstruction error'
print fig_title
u_rec = func_timer(iaf.iaf_decode)(s, dur, dt, bw, b, d, R, C)
pl.plot_compare(t, u, u_rec, fig_title,
output_dir + 'overview_decoded' + output_ext)
noise_power = None
fig_title = 'IAF input signal';
print fig_title
u = func_timer(bl.gen_band_limited)(dur, dt, f, noise_power)
pl.plot_signal(t, u, fig_title, output_dir + 'overview_input' + output_ext)
b = 3.5 # bias
d = 0.7 # threshold
R = np.inf # resistance
C = 0.01 # capacitance
try:
iaf.iaf_recoverable(u, bw, b, d, R, C)
except ValueError('reconstruction condition not satisfied'):
sys.exit()
fig_title = 'Signal encoded by IAF neuron'
print fig_title
s = func_timer(iaf.iaf_encode)(u, dt, b, d, R, C)
pl.plot_encoded(t, u, s, fig_title,
output_dir + 'overview_encoded' + output_ext)
fig_title = 'Decoded signal and reconstruction error'
print fig_title
u_rec = func_timer(iaf.iaf_decode)(s, dur, dt, bw, b, d, R, C)
pl.plot_compare(t, u, u_rec, fig_title,
output_dir + 'overview_decoded' + output_ext)
