def plotSingleTuningCurve(dataPath, gaussianDistance, ax1=None):
b.rcParams['font.size'] = 20
averagingWindowSize = 1
nE = 1600
ymax = 1
if ax1==None:
b.figure(figsize=(8,6.5))
fig_axis = b.subplot(1,1,1)
else:
fig_axis = ax1
b.sca(ax1)
path = dataPath + '/dist'+str(gaussianDistance)+'/' +'activity/'
spikeCount = np.load(path + 'spikeCountPerExample.npy')
spikeCountSingleNeuron = (spikeCount[:,nE/2-2,0])
numMeasurements = len(spikeCount[:,0,0])
spikeCount = movingaverage(spikeCountSingleNeuron,averagingWindowSize)
spikeCount /= np.max(spikeCountSingleNeuron)
b.plot(spikeCount, color='deepskyblue', marker='o', alpha=0.6, linewidth=0, label='Output')#alpha=0.5+(0.5*float(i)/float(len(lower_peaks))),
fig_axis.set_xticks([0., numMeasurements/2, numMeasurements])
fig_axis.set_xticklabels(['0', '0.5', '1'])
fig_axis.set_yticks([0., ymax/2, ymax])
fig_axis.spines['top'].set_visible(False)
fig_axis.spines['right'].set_visible(False)
fig_axis.get_xaxis().tick_bottom()
fig_axis.get_yaxis().tick_left()
b.ylabel('Normalized response')
b.ylim(0,ymax+0.05)
# b.title('spikes:' + str(sum(spikeCount)) + ', pop. value: ' + str(computePopVector(spikeCount)))
if ax1==None:
b.legend(loc='upper left', fancybox=True, framealpha=0.0)
b.savefig(dataPath + '/tuningCurveSingleNeuron.png', dpi=300, bbox_inches='tight')
def plotActivity(dataPath, inputStrengths, ax1=None):
averagingWindowSize = 10
nE = 1600
ymax = 40
linewidth = 5
# b.rcParams['lines.color'] = 'w'
# b.rcParams['text.color'] = 'w'
# b.rcParams['xtick.color'] = 'w'
# b.rcParams['ytick.color'] = 'w'
# b.rcParams['axes.labelcolor'] = 'w'
# b.rcParams['axes.edgecolor'] = 'w'
b.rcParams['font.size'] = 20
if ax1==None:
b.figure(figsize=(8,6.5))
fig_axis = b.subplot(1,1,1)
else:
fig_axis = ax1
b.sca(ax1)
for i,inputStrength in enumerate(inputStrengths[:]):
path = dataPath + '/in_'+str(inputStrength)+'/' +'activity/'
spikeCount = np.loadtxt(path + 'spikeCountAe.txt')
inputSpikeCount = np.loadtxt(path + 'spikeCountXe.txt')
spikeCount = movingaverage(spikeCount,averagingWindowSize)
inputSpikeCount = movingaverage(inputSpikeCount,averagingWindowSize)
if i==len(inputStrengths)-1:
b.plot([0], 'w', label=' ')#
b.plot([0], 'w', label='Avg. input 20 Hz:')#
b.plot(inputSpikeCount, 'coral', alpha=0.6, linewidth=linewidth, label='Input firing rate')
b.plot(spikeCount, 'deepskyblue', alpha=0.6, linewidth=linewidth, label='Output firing rate')
elif i==1:
b.plot([0], 'w', label=' ')#
b.plot([0], 'w', label='Avg. input 6 Hz:')#
b.plot(inputSpikeCount, 'red', alpha=1., linewidth=linewidth, label='Input firing rate')
b.plot(spikeCount, 'blue', alpha=0.6, linewidth=linewidth, label='Output firing rate')
elif i==0:
b.plot([0], 'w', label='Avg. input 2 Hz:')#
b.plot(inputSpikeCount, 'darkred', alpha=1., linewidth=linewidth, label='Input firing rate')
b.plot(spikeCount, 'darkblue', alpha=1.0, linewidth=linewidth, label='Output firing rate')
else:
b.plot(spikeCount, 'k', alpha=0.2+(0.4*float(i)/float(len(inputStrengths))), linewidth=0.6)
b.legend(loc='upper right', fancybox=True, framealpha=0.0, fontsize = 17)
handles, labels = fig_axis.get_legend_handles_labels()
# fig_axis.legend(handles[::-1], labels[::-1], loc='upper left', fancybox=True, framealpha=0.0)
fig_axis.set_xticks([0., nE/2, nE])
fig_axis.set_yticks([0., ymax/2, ymax])
fig_axis.spines['top'].set_visible(False)
fig_axis.spines['right'].set_visible(False)
fig_axis.get_xaxis().tick_bottom()
fig_axis.get_yaxis().tick_left()
b.xlabel('Neuron number (resorted)')
b.ylabel('Firing rate [Hz]')
b.ylim(0,ymax)
def plotActivity(dataPath, inputStrengths, ax1=None):
averagingWindowSize = 1
nE = 1600
ymax = 30
# b.rcParams['lines.color'] = 'w'
# b.rcParams['text.color'] = 'w'
# b.rcParams['xtick.color'] = 'w'
# b.rcParams['ytick.color'] = 'w'
# b.rcParams['axes.labelcolor'] = 'w'
# b.rcParams['axes.edgecolor'] = 'w'
b.rcParams['font.size'] = 20
if ax1==None:
b.figure(figsize=(8,6.5))
fig_axis = b.subplot(1,1,1)
else:
fig_axis = ax1
b.sca(ax1)
for i,inputStrength in enumerate(inputStrengths[:]):
path = dataPath + '/peak_'+str(inputStrength)+'/' +'activity/'
spikeCount = np.loadtxt(path + 'spikeCountAe.txt')
inputSpikeCount = np.loadtxt(path + 'spikeCountXe.txt')
spikeCount = movingaverage(spikeCount,averagingWindowSize)
inputSpikeCount = movingaverage(inputSpikeCount,averagingWindowSize)
if i==len(inputStrengths)-1:
b.plot(inputSpikeCount, 'r', alpha=1., linewidth=3, label='Input')
b.plot(spikeCount, 'deepskyblue', alpha=0.6, linewidth=3, label='Output')#alpha=0.5+(0.5*float(i)/float(len(numPeaks))),
# elif i==0:
# b.plot(spikeCount, 'k--', alpha=1., linewidth=3)#
# b.plot(inputSpikeCount, 'r--', alpha=1., linewidth=3)
else:
b.plot(spikeCount, 'k', alpha=0.2+(0.4*float(i)/float(len(inputStrength))), linewidth=0.6)
b.plot(inputSpikeCount, 'r', alpha=0.2+(0.4*float(i)/float(len(inputStrength))), linewidth=0.6)
fig_axis.set_xticks([0., nE/2, nE])
fig_axis.set_xticklabels(['0', '0.5', '1'])
fig_axis.set_yticks([0., ymax/2, ymax])
fig_axis.spines['top'].set_visible(False)
fig_axis.spines['right'].set_visible(False)
fig_axis.get_xaxis().tick_bottom()
fig_axis.get_yaxis().tick_left()
b.ylabel('Firing Rate [Hz]')
b.ylim(0,ymax)
# b.title('spikes:' + str(sum(spikeCount)) + ', pop. value: ' + str(computePopVector(spikeCount)))
if ax1==None:
b.xlabel('Neuron number (resorted)')
b.legend(fancybox=True, framealpha=0.0, loc='upper left')
b.savefig(dataPath + 'CueIntegration_single.png', dpi=900, transparent=True)
def plotPopulationTuningCurve(dataPath, gaussianDistance, ax1=None):
b.rcParams['font.size'] = 20
averagingWindowSize = 1
nE = 1600
ymax = 1
if ax1==None:
b.figure(figsize=(8,6.5))
fig_axis = b.subplot(1,1,1)
else:
fig_axis = ax1
b.sca(ax1)
path = dataPath + '/dist'+str(gaussianDistance)+'/' +'activity/'
spikeCount = np.load(path + 'spikeCountPerExample.npy')
numMeasurements = len(spikeCount[:,0,0])
measurementSpace = np.arange(numMeasurements)
populationSpikeCount = np.zeros((numMeasurements))
for i in xrange(nE):
populationSpikeCount += np.roll(spikeCount[:,i,0], int(-1.*i/nE*numMeasurements+ numMeasurements/2))
populationSpikeCount = movingaverage(populationSpikeCount,averagingWindowSize)
populationSpikeCount /= np.max(populationSpikeCount)
if gaussianDistance==0.:
mean = sum(measurementSpace*populationSpikeCount)/numMeasurements #note this correction
sigma = sum(populationSpikeCount*(measurementSpace-mean)**2)/numMeasurements #note this correction
popt, _ = curve_fit(gaus,measurementSpace,populationSpikeCount,p0=[1,mean,sigma])
print 'Gaussian amplitude: ', popt[0], ', mean: ', popt[1], ', std.: ', popt[2]**2
b.plot(measurementSpace,gaus(measurementSpace,*popt),'k')
b.plot(populationSpikeCount, color='deepskyblue', marker='o', alpha=0.6, linewidth=0, label='Output')#alpha=0.5+(0.5*float(i)/float(len(lower_peaks))),
fig_axis.set_xticks([0., numMeasurements/2, numMeasurements])
fig_axis.set_xticklabels(['0', '0.5', '1'])
fig_axis.set_yticks([0., ymax/2, ymax])
fig_axis.spines['top'].set_visible(False)
fig_axis.spines['right'].set_visible(False)
fig_axis.get_xaxis().tick_bottom()
fig_axis.get_yaxis().tick_left()
# b.ylabel('Normalized response')
b.ylim(0,ymax+0.05)
# b.title('spikes:' + str(sum(spikeCount)) + ', pop. value: ' + str(computePopVector(spikeCount)))
if ax1==None:
b.legend(loc='upper left', fancybox=True, framealpha=0.0)
b.savefig(dataPath + '/tuningCurvePopulation' + str(gaussianDistance) + '.png', dpi=300, bbox_inches='tight')
def plotActivity(dataPath, ax1=None):
averagingWindowSize = 1
nE = 1600
ymax = 40
# b.rcParams['lines.color'] = 'w'
# b.rcParams['text.color'] = 'w'
# b.rcParams['xtick.color'] = 'w'
# b.rcParams['ytick.color'] = 'w'
# b.rcParams['axes.labelcolor'] = 'w'
# b.rcParams['axes.edgecolor'] = 'w'
b.rcParams['font.size'] = 20
if ax1==None:
fig = b.figure(figsize=(8,6.5))
fig_axis=b.subplot(1,1,1)
else:
fig_axis = ax1
b.sca(ax1)
path = dataPath + 'activity/'
spikeCount = np.loadtxt(path + 'spikeCountCe.txt')
popVecs = np.loadtxt(path + 'popVecs1.txt')
desiredResult = (popVecs[0] + popVecs[1])%1.*1600
resultMonitor = np.loadtxt(path + 'resultPopVecs1.txt')
actualResult = resultMonitor[2]*1600
ax1.axvline(desiredResult, color='r', linewidth=3, ymax=ymax, label='Desired result')
ax1.axvline(actualResult, color='blue', linewidth=3, ymax=ymax, label='Population vector')
print 'desiredResult', desiredResult, ', actual result', actualResult
# spikeCount = np.roll(spikeCount, 800+int(-1*desiredResult))
spikeCount = movingaverage(spikeCount,averagingWindowSize)
b.plot(spikeCount, 'deepskyblue', alpha=0.6, linewidth=3, label='Population activity')
fig_axis.set_xticks([0., nE/2, nE])
fig_axis.set_xticklabels(['0', '0.5', '1'])
fig_axis.set_yticks([0., ymax/2, ymax])
fig_axis.spines['top'].set_visible(False)
fig_axis.spines['right'].set_visible(False)
fig_axis.get_xaxis().tick_bottom()
fig_axis.get_yaxis().tick_left()
b.ylabel('Firing Rate [Hz]')
b.ylim(0,ymax)
b.legend(fancybox=True, framealpha=0.0, loc='upper left')
# b.title('spikes:' + str(sum(spikeCount)) + ', pop. value: ' + str(computePopVector(spikeCount)))
if ax1==None:
b.xlabel('Neuron number (resorted)')
b.savefig(dataPath + 'SignalRestoration.png', dpi=900, transparent=True)
def plotSingleActivity(dataPath, gaussianDistance, ax1=None):
b.rcParams['font.size'] = 20
averagingWindowSize = 30
nE = 1600
ymax = 1
if ax1==None:
b.figure(figsize=(8,6.5))
fig_axis = b.subplot(1,1,1)
else:
fig_axis = ax1
b.sca(ax1)
linewidth = 3
path = dataPath + '/dist'+str(gaussianDistance)+'/' +'activity/'
spikeCountTemp = np.load(path + 'spikeCountPerExample.npy')
spikeCount = spikeCountTemp[25,:,0]#np.loadtxt(path + 'spikeCountAe.txt')
# spikeCount = np.roll(spikeCount, 400)
inputSpikeCount = np.roll(np.loadtxt(path + 'spikeCountXe.txt'), 400)
spikeCount = movingaverage(spikeCount,averagingWindowSize)
spikeCount /= np.max(spikeCount)
inputSpikeCount = movingaverage(inputSpikeCount,averagingWindowSize)
inputSpikeCount /= np.max(inputSpikeCount)
b.plot(spikeCount, 'deepskyblue', alpha=0.6, linewidth=linewidth, label='Output')#alpha=0.5+(0.5*float(i)/float(len(lower_peaks))),
b.plot(inputSpikeCount, 'r', alpha=1., linewidth=linewidth, label='Input')
fig_axis.set_xticks([0., nE/2, nE])
fig_axis.set_xticklabels(['0', '0.5', '1'])
fig_axis.set_yticks([0., ymax/2, ymax])
fig_axis.spines['top'].set_visible(False)
fig_axis.spines['right'].set_visible(False)
fig_axis.get_xaxis().tick_bottom()
fig_axis.get_yaxis().tick_left()
b.ylabel('Normalized response')
b.ylim(0,ymax)
# b.title('spikes:' + str(sum(spikeCount)) + ', pop. value: ' + str(computePopVector(spikeCount)))
if ax1==None:
b.legend(loc='upper left', fancybox=True, framealpha=0.0)
b.savefig(dataPath + '/multipleAnglesSingleActivity.png', dpi=300)
def save_and_plot_results():
global fig_num
print '...saving results'
if not test_mode:
save_theta()
if not test_mode:
save_connections()
else:
np.save(top_level_path + 'activity/conv_patch_connectivity_activity/results_' + str(num_examples) + '_' + ending, result_monitor)
np.save(top_level_path + 'activity/conv_patch_connectivity_activity/input_numbers_' + str(num_examples) + '_' + ending, input_numbers)
if do_plot:
if rate_monitors:
b.figure(fig_num)
fig_num += 1
for i, name in enumerate(rate_monitors):
b.subplot(len(rate_monitors), 1, i + 1)
b.plot(rate_monitors[name].times / b.second, rate_monitors[name].rate, '.')
b.title('Rates of population ' + name)
if spike_monitors:
b.figure(fig_num)
fig_num += 1
for i, name in enumerate(spike_monitors):
b.subplot(len(spike_monitors), 1, i + 1)
b.raster_plot(spike_monitors[name])
b.title('Spikes of population ' + name)
if spike_counters:
b.figure(fig_num)
fig_num += 1
for i, name in enumerate(spike_counters):
b.subplot(len(spike_counters), 1, i + 1)
b.plot(np.asarray(spike_counters['Ae'].count[:]))
b.title('Spike count of population ' + name)
plot_2d_input_weights()
plot_patch_weights()
b.ioff()
b.show()
class ConvolutionalSpikingNN(object):
'''
Object which represents a convolutional spiking neural network.
'''
def __init__(mode, connectivity, weight_dependence, post_pre, conv_size, conv_stride, conv_features, weight_sharing, lattice_structure, random_inhibition_prob, top_percent):
'''
Network initialization.
'''
# setting input parameters
this.mode = mode
this.connectivity = connectivity
this.weight_dependence = weight_dependence
this.post_pre = post_pre
this.conv_size = conv_size
this.conv_features = conv_features
this.weight_sharing = weight_sharing
this.lattice_structure = lattice_structure
this.random_inhibition_prob = random_inhibition_prob
# load training or testing data
if mode == 'train':
start = time.time()
this.data = get_labeled_data(MNIST_data_path + 'training')
end = time.time()
print 'time needed to load training set:', end - start
else:
start = time.time()
this.data = get_labeled_data(MNIST_data_path + 'testing', bTrain = False)
end = time.time()
print 'time needed to load test set:', end - start
# set parameters for simulation based on train / test mode
if test_mode:
weight_path = top_level_path + 'weights/conv_patch_connectivity_weights/'
this.num_examples = 10000 * 1
this.do_plot_performance = False
ee_STDP_on = False
else:
weight_path = top_level_path + 'random/conv_patch_connectivity_random/'
this.num_examples = 60000 * 1
this.do_plot_performance = True
ee_STDP_on = True
# plotting or not
do_plot = True
# number of inputs to the network
this.n_input = 784
this.n_input_sqrt = int(math.sqrt(n_input))
# number of neurons parameters
this.n_e = ((n_input_sqrt - conv_size) / conv_stride + 1) ** 2
this.n_e_total = n_e * conv_features
this.n_e_sqrt = int(math.sqrt(n_e))
this.n_i = n_e
this.conv_features_sqrt = int(math.sqrt(conv_features))
# time (in seconds) per data example presentation and rest period in between, used to calculate total runtime
this.single_example_time = 0.35 * b.second
this.resting_time = 0.15 * b.second
runtime = num_examples * (single_example_time + resting_time)
# set the update interval
if test_mode:
this.update_interval = num_examples
else:
this.update_interval = 100
# rest potential parameters, reset potential parameters, threshold potential parameters, and refractory periods
v_rest_e, v_rest_i = -65. * b.mV, -60. * b.mV
v_reset_e, v_reset_i = -65. * b.mV, -45. * b.mV
v_thresh_e, v_thresh_i = -52. * b.mV, -40. * b.mV
refrac_e, refrac_i = 5. * b.ms, 2. * b.ms
# dictionaries for weights and delays
weight, delay = {}, {}
# populations, connections, saved connections, etc.
input_population_names = [ 'X' ]
population_names = [ 'A' ]
input_connection_names = [ 'XA' ]
save_conns = [ 'XeAe', 'AeAe' ]
# weird and bad names for variables, I think
input_conn_names = [ 'ee_input' ]
recurrent_conn_names = [ 'ei', 'ie', 'ee' ]
# setting weight, delay, and intensity parameters
weight['ee_input'] = (conv_size ** 2) * 0.175
delay['ee_input'] = (0 * b.ms, 10 * b.ms)
delay['ei_input'] = (0 * b.ms, 5 * b.ms)
input_intensity = start_input_intensity = 2.0
# time constants, learning rates, max weights, weight dependence, etc.
tc_pre_ee, tc_post_ee = 20 * b.ms, 20 * b.ms
nu_ee_pre, nu_ee_post = 0.0001, 0.01
wmax_ee = 1.0
exp_ee_post = exp_ee_pre = 0.2
w_mu_pre, w_mu_post = 0.2, 0.2
# setting up differential equations (depending on train / test mode)
if test_mode:
scr_e = 'v = v_reset_e; timer = 0*ms'
else:
tc_theta = 1e7 * b.ms
theta_plus_e = 0.05 * b.mV
scr_e = 'v = v_reset_e; theta += theta_plus_e; timer = 0*ms'
offset = 20.0 * b.mV
v_thresh_e = '(v>(theta - offset + ' + str(v_thresh_e) + ')) * (timer>refrac_e)'
# equations for neurons
neuron_eqs_e = '''
dv/dt = ((v_rest_e - v) + (I_synE + I_synI) / nS) / (100 * ms) : volt
I_synE = ge * nS * -v : amp
I_synI = gi * nS * (-100.*mV-v) : amp
dge/dt = -ge/(1.0*ms) : 1
dgi/dt = -gi/(2.0*ms) : 1
'''
if test_mode:
neuron_eqs_e += '\n theta :volt'
else:
neuron_eqs_e += '\n dtheta/dt = -theta / (tc_theta) : volt'
neuron_eqs_e += '\n dtimer/dt = 100.0 : ms'
neuron_eqs_i = '''
dv/dt = ((v_rest_i - v) + (I_synE + I_synI) / nS) / (10*ms) : volt
I_synE = ge * nS * -v : amp
I_synI = gi * nS * (-85.*mV-v) : amp
dge/dt = -ge/(1.0*ms) : 1
dgi/dt = -gi/(2.0*ms) : 1
'''
# creating dictionaries for various objects
this.neuron_groups = {}
this.input_groups = {}
this.connections = {}
this.input_connections = {}
this.stdp_methods = {}
this.rate_monitors = {}
this.spike_monitors = {}
this.spike_counters = {}
# creating excitatory, inhibitory populations
this.neuron_groups['e'] = b.NeuronGroup(n_e_total, neuron_eqs_e, threshold=v_thresh_e, refractory=refrac_e, reset=scr_e, compile=True, freeze=True)
this.neuron_groups['i'] = b.NeuronGroup(n_e_total, neuron_eqs_i, threshold=v_thresh_i, refractory=refrac_i, reset=v_reset_i, compile=True, freeze=True)
# creating subpopulations of excitatory, inhibitory neurons
for name in population_names:
print '...creating neuron group:', name
# get a subgroup of size 'n_e' from all exc
neuron_groups[name + 'e'] = neuron_groups['e'].subgroup(conv_features * n_e)
# get a subgroup of size 'n_i' from the inhibitory layer
neuron_groups[name + 'i'] = neuron_groups['i'].subgroup(conv_features * n_e)
# start the membrane potentials of these groups 40mV below their resting potentials
neuron_groups[name + 'e'].v = v_rest_e - 40. * b.mV
neuron_groups[name + 'i'].v = v_rest_i - 40. * b.mV
print '...creating recurrent connections'
for name in population_names:
# if we're in test mode / using some stored weights
if mode == 'test' or weight_path[-8:] == 'weights/conv_patch_connectivity_weights/':
# load up adaptive threshold parameters
neuron_groups['e'].theta = np.load(weight_path + 'theta_A' + '_' + ending +'.npy')
else:
# otherwise, set the adaptive additive threshold parameter at 20mV
neuron_groups['e'].theta = np.ones((n_e_total)) * 20.0 * b.mV
for conn_type in recurrent_conn_names:
if conn_type == 'ei':
# create connection name (composed of population and connection types)
conn_name = name + conn_type[0] + name + conn_type[1]
# create a connection from the first group in conn_name with the second group
connections[conn_name] = b.Connection(neuron_groups[conn_name[0:2]], neuron_groups[conn_name[2:4]], structure='sparse', state='g' + conn_type[0])
# instantiate the created connection
for feature in xrange(conv_features):
for n in xrange(n_e):
connections[conn_name][feature * n_e + n, feature * n_e + n] = 10.4
elif conn_type == 'ie':
# create connection name (composed of population and connection types)
conn_name = name + conn_type[0] + name + conn_type[1]
# create a connection from the first group in conn_name with the second group
connections[conn_name] = b.Connection(neuron_groups[conn_name[0:2]], neuron_groups[conn_name[2:4]], structure='sparse', state='g' + conn_type[0])
# instantiate the created connection
for feature in xrange(conv_features):
for other_feature in xrange(conv_features):
if feature != other_feature:
for n in xrange(n_e):
connections[conn_name][feature * n_e + n, other_feature * n_e + n] = 17.4
if random_inhibition_prob != 0.0:
for feature in xrange(conv_features):
for other_feature in xrange(conv_features):
for n_this in xrange(n_e):
for n_other in xrange(n_e):
if n_this != n_other:
if b.random() < random_inhibition_prob:
connections[conn_name][feature * n_e + n_this, other_feature * n_e + n_other] = 17.4
elif conn_type == 'ee':
# create connection name (composed of population and connection types)
conn_name = name + conn_type[0] + name + conn_type[1]
# get weights from file if we are in test mode
if mode == 'test':
weight_matrix = get_matrix_from_file(weight_path + conn_name + '_' + ending + '.npy', conv_features * n_e, conv_features * n_e)
# create a connection from the first group in conn_name with the second group
connections[conn_name] = b.Connection(neuron_groups[conn_name[0:2]], neuron_groups[conn_name[2:4]], structure='sparse', state='g' + conn_type[0])
# instantiate the created connection
if connectivity == 'all':
for feature in xrange(conv_features):
for other_feature in xrange(conv_features):
if feature != other_feature:
for this_n in xrange(n_e):
for other_n in xrange(n_e):
if is_lattice_connection(n_e_sqrt, this_n, other_n):
if mode == 'test':
connections[conn_name][feature * n_e + this_n, other_feature * n_e + other_n] = weight_matrix[feature * n_e + this_n, other_feature * n_e + other_n]
else:
connections[conn_name][feature * n_e + this_n, other_feature * n_e + other_n] = (b.random() + 0.01) * 0.3
elif connectivity == 'pairs':
for feature in xrange(conv_features):
if feature % 2 == 0:
for this_n in xrange(n_e):
for other_n in xrange(n_e):
if is_lattice_connection(n_e_sqrt, this_n, other_n):
if mode == 'test':
connections[conn_name][feature * n_e + this_n, (feature + 1) * n_e + other_n] = weight_matrix[feature * n_e + this_n, (feature + 1) * n_e + other_n]
else:
connections[conn_name][feature * n_e + this_n, (feature + 1) * n_e + other_n] = (b.random() + 0.01) * 0.3
elif feature % 2 == 1:
for this_n in xrange(n_e):
for other_n in xrange(n_e):
if is_lattimode == 'test'ce_connection(n_e_sqrt, this_n, other_n):
if mode == 'test':
connections[conn_name][feature * n_e + this_n, (feature - 1) * n_e + other_n] = weight_matrix[feature * n_e + this_n, (feature - 1) * n_e + other_n]
else:
connections[conn_name][feature * n_e + this_n, (feature - 1) * n_e + other_n] = (b.random() + 0.01) * 0.3
elif connectivity == 'none':
pass
# if STDP from excitatory -> excitatory is on and this connection is excitatory -> excitatory
if ee_STDP_on and 'ee' in recurrent_conn_names:
stdp_methods[name + 'e' + name + 'e'] = b.STDP(connections[name + 'e' + name + 'e'], eqs=eqs_stdp_ee, pre=eqs_stdp_pre_ee, post=eqs_stdp_post_ee, wmin=0., wmax=wmax_ee)
print '...creating monitors for:', name
# spike rate monitors for excitatory and inhibitory neuron populations
rate_monitors[name + 'e'] = b.PopulationRateMonitor(neuron_groups[name + 'e'], bin=(single_example_time + resting_time) / b.second)
rate_monitors[name + 'i'] = b.PopulationRateMonitor(neuron_groups[name + 'i'], bin=(single_example_time + resting_time) / b.second)
spike_counters[name + 'e'] = b.SpikeCounter(neuron_groups[name + 'e'])
# record neuron population spikes if specified
spike_monitors[name + 'e'] = b.SpikeMonitor(neuron_groups[name + 'e'])
spike_monitors[name + 'i'] = b.SpikeMonitor(neuron_groups[name + 'i'])
if do_plot:
b.figure(fig_num)
fig_num += 1
b.ion()
b.subplot(211)
b.raster_plot(spike_monitors['Ae'], refresh=1000 * b.ms, showlast=1000 * b.ms)
b.subplot(212)
b.raster_plot(spike_monitors['Ai'], refresh=1000 * b.ms, showlast=1000 * b.ms)
# creating lattice locations for each patch
if connectivity == 'all':
lattice_locations = {}
for this_n in xrange(conv_features * n_e):
lattice_locations[this_n] = [ other_n for other_n in xrange(conv_features * n_e) if is_lattice_connection(n_e_sqrt, this_n % n_e, other_n % n_e) ]
elif connectivity == 'pairs':
lattice_locations = {}
for this_n in xrange(conv_features * n_e):
lattice_locations[this_n] = []
for other_n in xrange(conv_features * n_e):
if this_n // n_e % 2 == 0:
if is_lattice_connection(n_e_sqrt, this_n % n_e, other_n % n_e) and other_n // n_e == this_n // n_e + 1:
lattice_locations[this_n].append(other_n)
elif this_n // n_e % 2 == 1:
if is_lattice_connection(n_e_sqrt, this_n % n_e, other_n % n_e) and other_n // n_e == this_n // n_e - 1:
lattice_locations[this_n].append(other_n)
elif connectivity == 'none':
lattice_locations = {}
# setting up parameters for weight normalization between patches
num_lattice_connections = sum([ len(value) for value in lattice_locations.values() ])
weight['ee_recurr'] = (num_lattice_connections / conv_features) * 0.15
# creating Poission spike train from input image (784 vector, 28x28 image)
for name in input_population_names:
input_groups[name + 'e'] = b.PoissonGroup(n_input, 0)
rate_monitors[name + 'e'] = b.PopulationRateMonitor(input_groups[name + 'e'], bin=(single_example_time + resting_time) / b.second)
# creating connections from input Poisson spike train to convolution patch populations
for name in input_connection_names:
print '\n...creating connections between', name[0], 'and', name[1]
# for each of the input connection types (in this case, excitatory -> excitatory)
for conn_type in input_conn_names:
# saved connection name
conn_name = name[0] + conn_type[0] + name[1] + conn_type[1]
# get weight matrix depending on training or test phase
if mode == 'test':
weight_matrix = get_matrix_from_file(weight_path + conn_name + '_' + ending + '.npy', n_input, conv_features * n_e)
# create connections from the windows of the input group to the neuron population
input_connections[conn_name] = b.Connection(input_groups['Xe'], neuron_groups[name[1] + conn_type[1]], structure='sparse', state='g' + conn_type[0], delay=True, max_delay=delay[conn_type][1])
if mode == 'test':
for feature in xrange(conv_features):
for n in xrange(n_e):
for idx in xrange(conv_size ** 2):
input_connections[conn_name][convolution_locations[n][idx], feature * n_e + n] = weight_matrix[convolution_locations[n][idx], feature * n_e + n]
else:
for feature in xrange(conv_features):
for n in xrange(n_e):
for idx in xrange(conv_size ** 2):
input_connections[conn_name][convolution_locations[n][idx], feature * n_e + n] = (b.random() + 0.01) * 0.3
# if excitatory -> excitatory STDP is specified, add it here (input to excitatory populations)
if ee_STDP_on:
print '...creating STDP for connection', name
# STDP connection name
conn_name = name[0] + conn_type[0] + name[1] + conn_type[1]
# create the STDP object
stdp_methods[conn_name] = b.STDP(input_connections[conn_name], eqs=eqs_stdp_ee, pre=eqs_stdp_pre_ee, post=eqs_stdp_post_ee, wmin=0., wmax=wmax_ee)
print '\n'
post = eqs_stdp_post_ee, wmin=0., wmax= wmax_ee)
print 'create monitors for', name
rate_monitors[name+'e'] = b.PopulationRateMonitor(neuron_groups[name+'e'], bin = (single_example_time+resting_time)/b.second)
rate_monitors[name+'i'] = b.PopulationRateMonitor(neuron_groups[name+'i'], bin = (single_example_time+resting_time)/b.second)
spike_counters[name+'e'] = b.SpikeCounter(neuron_groups[name+'e'])
if record_spikes:
spike_monitors[name+'e'] = b.SpikeMonitor(neuron_groups[name+'e'])
spike_monitors[name+'i'] = b.SpikeMonitor(neuron_groups[name+'i'])
if record_spikes:
b.figure(fig_num)
fig_num += 1
b.ion()
b.subplot(211)
b.raster_plot(spike_monitors['Ae'], refresh=1000*b.ms, showlast=1000*b.ms)
b.subplot(212)
b.raster_plot(spike_monitors['Ai'], refresh=1000*b.ms, showlast=1000*b.ms)
#------------------------------------------------------------------------------
# create input population and connections from input populations
#------------------------------------------------------------------------------
pop_values = [0,0,0]
for i,name in enumerate(input_population_names):
input_groups[name+'e'] = b.PoissonGroup(n_input, 0)
rate_monitors[name+'e'] = b.PopulationRateMonitor(input_groups[name+'e'], bin = (single_example_time+resting_time)/b.second)
for name in input_connection_names:
print 'create connections between', name[0], 'and', name[1]
def build_network():
global fig_num, assignments
neuron_groups['e'] = b.NeuronGroup(n_e_total,
neuron_eqs_e,
threshold=v_thresh_e,
refractory=refrac_e,
reset=scr_e,
compile=True,
freeze=True)
neuron_groups['i'] = b.NeuronGroup(n_e_total,
neuron_eqs_i,
threshold=v_thresh_i,
refractory=refrac_i,
reset=v_reset_i,
compile=True,
freeze=True)
for name in population_names:
print '...Creating neuron group:', name
# get a subgroup of size 'n_e' from all exc
neuron_groups[name + 'e'] = neuron_groups['e'].subgroup(conv_features *
n_e)
# get a subgroup of size 'n_i' from the inhibitory layer
neuron_groups[name + 'i'] = neuron_groups['i'].subgroup(conv_features *
n_e)
# start the membrane potentials of these groups 40mV below their resting potentials
neuron_groups[name + 'e'].v = v_rest_e - 40. * b.mV
neuron_groups[name + 'i'].v = v_rest_i - 40. * b.mV
print '...Creating recurrent connections'
for name in population_names:
# if we're in test mode / using some stored weights
if test_mode:
# load up adaptive threshold parameters
if save_best_model:
neuron_groups['e'].theta = np.load(
os.path.join(best_weights_dir,
'_'.join(['theta_A', ending + '_best.npy'])))
else:
neuron_groups['e'].theta = np.load(
os.path.join(end_weights_dir,
'_'.join(['theta_A', ending + '_end.npy'])))
else:
# otherwise, set the adaptive additive threshold parameter at 20mV
neuron_groups['e'].theta = np.ones((n_e_total)) * 20.0 * b.mV
for conn_type in recurrent_conn_names:
if conn_type == 'ei':
# create connection name (composed of population and connection types)
conn_name = name + conn_type[0] + name + conn_type[1]
# create a connection from the first group in conn_name with the second group
connections[conn_name] = b.Connection(
neuron_groups[conn_name[0:2]],
neuron_groups[conn_name[2:4]],
structure='sparse',
state='g' + conn_type[0])
# instantiate the created connection
for feature in xrange(conv_features):
for n in xrange(n_e):
connections[conn_name][feature * n_e + n,
feature * n_e + n] = 10.4
elif conn_type == 'ie':
# create connection name (composed of population and connections types)
conn_name = name + conn_type[0] + name + conn_type[
1] + '_' + ending
# create a connection from the first group in conn_name with the second group
connections[conn_name] = b.Connection(
neuron_groups[conn_name[0:2]],
neuron_groups[conn_name[2:4]],
structure='sparse',
state='g' + conn_type[0])
# instantiate the created connection with the 'weightMatrix' loaded from file
for feature in xrange(conv_features):
for other_feature in xrange(conv_features):
if feature != other_feature:
for n in xrange(n_e):
connections[conn_name][feature * n_e + n,
other_feature * n_e +
n] = 17.4
print '...Creating monitors for:', name
# spike rate monitors for excitatory and inhibitory neuron populations
rate_monitors[name + 'e'] = b.PopulationRateMonitor(
neuron_groups[name + 'e'],
bin=(single_example_time + resting_time) / b.second)
rate_monitors[name + 'i'] = b.PopulationRateMonitor(
neuron_groups[name + 'i'],
bin=(single_example_time + resting_time) / b.second)
spike_counters[name + 'e'] = b.SpikeCounter(neuron_groups[name + 'e'])
# record neuron population spikes if specified
if record_spikes or plot:
spike_monitors[name + 'e'] = b.SpikeMonitor(neuron_groups[name +
'e'])
spike_monitors[name + 'i'] = b.SpikeMonitor(neuron_groups[name +
'i'])
if record_spikes and plot:
b.figure(fig_num, figsize=(8, 6))
fig_num += 1
b.ion()
b.subplot(211)
b.raster_plot(spike_monitors['Ae'],
refresh=1000 * b.ms,
showlast=1000 * b.ms,
title='Excitatory spikes per neuron')
b.subplot(212)
b.raster_plot(spike_monitors['Ai'],
refresh=1000 * b.ms,
showlast=1000 * b.ms,
title='Inhibitory spikes per neuron')
b.tight_layout()
# creating Poission spike train from input image (784 vector, 28x28 image)
for name in input_population_names:
input_groups[name + 'e'] = b.PoissonGroup(n_input, 0)
rate_monitors[name + 'e'] = b.PopulationRateMonitor(
input_groups[name + 'e'],
bin=(single_example_time + resting_time) / b.second)
# creating connections from input Poisson spike train to excitatory neuron population(s)
for name in input_connection_names:
print '\n...Creating connections between', name[0], 'and', name[1]
# for each of the input connection types (in this case, excitatory -> excitatory)
for conn_type in input_conn_names:
# saved connection name
conn_name = name[0] + conn_type[0] + name[1] + conn_type[1]
# get weight matrix depending on training or test phase
if test_mode:
if save_best_model:
weight_matrix = np.load(
os.path.join(
best_weights_dir,
'_'.join([conn_name, ending + '_best.npy'])))
else:
weight_matrix = np.load(
os.path.join(
end_weights_dir,
'_'.join([conn_name, ending + '_end.npy'])))
# create connections from the windows of the input group to the neuron population
input_connections[conn_name] = b.Connection(input_groups['Xe'], neuron_groups[name[1] + conn_type[1]], \
structure='sparse', state='g' + conn_type[0], delay=True, max_delay=delay[conn_type][1])
if test_mode:
for feature in xrange(conv_features):
for n in xrange(n_e):
for idx in xrange(conv_size**2):
input_connections[conn_name][convolution_locations[n][idx], feature * n_e + n] = \
weight_matrix[convolution_locations[n][idx], feature * n_e + n]
else:
for feature in xrange(conv_features):
for n in xrange(n_e):
for idx in xrange(conv_size**2):
input_connections[conn_name][
convolution_locations[n][idx],
feature * n_e + n] = (b.random() + 0.01) * 0.3
if test_mode:
if plot:
plot_weights_and_assignments(assignments)
fig_num += 1
# if excitatory -> excitatory STDP is specified, add it here (input to excitatory populations)
if not test_mode:
print '...Creating STDP for connection', name
# STDP connection name
conn_name = name[0] + conn_type[0] + name[1] + conn_type[1]
# create the STDP object
stdp_methods[conn_name] = b.STDP(input_connections[conn_name], eqs=eqs_stdp_ee, \
pre=eqs_stdp_pre_ee, post=eqs_stdp_post_ee, wmin=0., wmax=wmax_ee)
print '\n'
def __init__(self,
n_input=784,
conv_size=16,
conv_stride=4,
conv_features=50,
connectivity='all',
weight_dependence=False,
post_pre=True,
weight_sharing=False,
lattice_structure='4',
random_lattice_prob=0.0,
random_inhibition_prob=0.0):
'''
Constructor for the spiking convolutional neural network model.
n_input: (flattened) dimensionality of the input data
conv_size: side length of convolution windows used
conv_stride: stride (horizontal and vertical) of convolution windows used
conv_features: number of convolution features (or patches) used
connectivity: connection style between patches; one of 'none', 'pairs', all'; more to be added
weight_dependence: whether to use weight STDP with weight dependence
post_pre: whether to use STDP with both post- and pre-synpatic traces
weight_sharing: whether to impose that all neurons within a convolution patch share a common set of weights
lattice_structure: lattice connectivity pattern between patches; one of 'none', '4', '8', and 'all'
random_lattice_prob: probability of adding random additional lattice connections between patches
random_inhibition_prob: probability of adding random additional inhibition edges from the inhibitory to excitatory population
'''
self.n_input, self.conv_size, self.conv_stride, self.conv_features, self.connectivity, self.weight_dependence, \
self.post_pre, self.weight_sharing, self.lattice_structure, self.random_lattice_prob, self.random_inhibition_prob = \
n_input, conv_size, conv_stride, conv_features, connectivity, weight_dependence, post_pre, weight_sharing, lattice_structure, \
random_lattice_prob, random_inhibition_prob
# number of inputs to the network
self.n_input_sqrt = int(math.sqrt(self.n_input))
self.n_excitatory_patch = (
(self.n_input_sqrt - self.conv_size) / self.conv_stride + 1)**2
self.n_excitatory = self.n_excitatory_patch * self.conv_features
self.n_excitatory_patch_sqrt = int(math.sqrt(self.n_excitatory_patch))
self.n_inhibitory_patch = self.n_excitatory_patch
self.n_inhibitory = self.n_excitatory
self.conv_features_sqrt = int(math.ceil(math.sqrt(self.conv_features)))
# time (in seconds) per data example presentation and rest period in between
self.single_example_time = 0.35 * b.second
self.resting_time = 0.15 * b.second
# set update intervals
self.update_interval = 100
self.weight_update_interval = 10
self.print_progress_interval = 10
# rest potential parameters, reset potential parameters, threshold potential parameters, and refractory periods
v_rest_e, v_rest_i = -65. * b.mV, -60. * b.mV
v_reset_e, v_reset_i = -65. * b.mV, -45. * b.mV
v_thresh_e, v_thresh_i = -52. * b.mV, -40. * b.mV
refrac_e, refrac_i = 5. * b.ms, 2. * b.ms
# time constants, learning rates, max weights, weight dependence, etc.
tc_pre_ee, tc_post_ee = 20 * b.ms, 20 * b.ms
nu_ee_pre, nu_ee_post = 0.0001, 0.01
exp_ee_post = exp_ee_pre = 0.2
w_mu_pre, w_mu_post = 0.2, 0.2
# parameters for neuron equations
tc_theta = 1e7 * b.ms
theta_plus = 0.05 * b.mV
scr_e = 'v = v_reset_e; theta += theta_plus; timer = 0*ms'
offset = 20.0 * b.mV
v_thresh_e = '(v>(theta - offset + ' + str(
v_thresh_e) + ')) * (timer>refrac_e)'
# equations for neurons
neuron_eqs_e = '''
dv / dt = ((v_rest_e - v) + (I_synE + I_synI) / nS) / (100 * ms) : volt
I_synE = ge * nS * - v : amp
I_synI = gi * nS * (-100. * mV - v) : amp
dge / dt = -ge / (1.0*ms) : 1
dgi / dt = -gi / (2.0*ms) : 1
dtheta / dt = -theta / (tc_theta) : volt
dtimer / dt = 100.0 : ms
'''
neuron_eqs_i = '''
dv/dt = ((v_rest_i - v) + (I_synE + I_synI) / nS) / (10*ms) : volt
I_synE = ge * nS * -v : amp
I_synI = gi * nS * (-85.*mV-v) : amp
dge/dt = -ge/(1.0*ms) : 1
dgi/dt = -gi/(2.0*ms) : 1
'''
# STDP synaptic traces
eqs_stdp_ee = '''
dpre / dt = -pre / tc_pre_ee : 1.0
dpost / dt = -post / tc_post_ee : 1.0
'''
# dictionaries for weights and delays
self.weight, self.delay = {}, {}
# setting weight, delay, and intensity parameters
self.weight['ee_input'] = (conv_size**2) * 0.175
self.delay['ee_input'] = (0 * b.ms, 10 * b.ms)
self.delay['ei_input'] = (0 * b.ms, 5 * b.ms)
self.input_intensity = self.start_input_intensity = 2.0
self.wmax_ee = 1.0
# populations, connections, saved connections, etc.
self.input_population_names = ['X']
self.population_names = ['A']
self.input_connection_names = ['XA']
self.save_connections = ['XeAe', 'AeAe']
self.input_connection_names = ['ee_input']
self.recurrent_connection_names = ['ei', 'ie', 'ee']
# setting STDP update rule
if weight_dependence:
if post_pre:
eqs_stdp_pre_ee = 'pre = 1.; w -= nu_ee_pre * post * w ** exp_ee_pre'
eqs_stdp_post_ee = 'w += nu_ee_post * pre * (wmax_ee - w) ** exp_ee_post; post = 1.'
else:
eqs_stdp_pre_ee = 'pre = 1.'
eqs_stdp_post_ee = 'w += nu_ee_post * pre * (wmax_ee - w) ** exp_ee_post; post = 1.'
else:
if post_pre:
eqs_stdp_pre_ee = 'pre = 1.; w -= nu_ee_pre * post'
eqs_stdp_post_ee = 'w += nu_ee_post * pre; post = 1.'
else:
eqs_stdp_pre_ee = 'pre = 1.'
eqs_stdp_post_ee = 'w += nu_ee_post * pre; post = 1.'
print '\n'
# for filesaving purposes
stdp_input = ''
if self.weight_dependence:
stdp_input += 'weight_dependence_'
else:
stdp_input += 'no_weight_dependence_'
if self.post_pre:
stdp_input += 'post_pre'
else:
stdp_input += 'no_post_pre'
if self.weight_sharing:
use_weight_sharing = 'weight_sharing'
else:
use_weight_sharing = 'no_weight_sharing'
# set ending of filename saves
self.ending = self.connectivity + '_' + str(self.conv_size) + '_' + str(self.conv_stride) + '_' + str(self.conv_features) + \
'_' + str(self.n_excitatory_patch) + '_' + stdp_input + '_' + \
use_weight_sharing + '_' + str(self.lattice_structure) + '_' + str(self.random_lattice_prob) + \
'_' + str(self.random_inhibition_prob)
self.fig_num = 1
# creating dictionaries for various objects
self.neuron_groups, self.input_groups, self.connections, self.input_connections, self.stdp_methods, self.rate_monitors, \
self.spike_monitors, self.spike_counters, self.output_numbers = {}, {}, {}, {}, {}, {}, {}, {}, {}
# creating convolution locations inside the input image
self.convolution_locations = {}
for n in xrange(self.n_excitatory_patch):
self.convolution_locations[n] = [ ((n % self.n_excitatory_patch_sqrt) * self.conv_stride + (n // self.n_excitatory_patch_sqrt) \
* self.n_input_sqrt * self.conv_stride) + (x * self.n_input_sqrt) + y \
for y in xrange(self.conv_size) for x in xrange(self.conv_size) ]
# instantiating neuron spike / votes monitor
self.result_monitor = np.zeros(
(self.update_interval, self.conv_features,
self.n_excitatory_patch))
# creating overarching neuron populations
self.neuron_groups['e'] = b.NeuronGroup(self.n_excitatory, neuron_eqs_e, threshold=v_thresh_e, \
refractory=refrac_e, reset=scr_e, compile=True, freeze=True)
self.neuron_groups['i'] = b.NeuronGroup(self.n_inhibitory, neuron_eqs_i, threshold=v_thresh_i, \
refractory=refrac_i, reset=v_reset_i, compile=True, freeze=True)
# create neuron subpopulations
for name in self.population_names:
print '...creating neuron group:', name
# get a subgroup of size 'n_e' from all exc
self.neuron_groups[name + 'e'] = self.neuron_groups['e'].subgroup(
self.conv_features * self.n_excitatory_patch)
# get a subgroup of size 'n_i' from the inhibitory layer
self.neuron_groups[name + 'i'] = self.neuron_groups['i'].subgroup(
self.conv_features * self.n_excitatory_patch)
# start the membrane potentials of these groups 40mV below their resting potentials
self.neuron_groups[name + 'e'].v = v_rest_e - 40. * b.mV
self.neuron_groups[name + 'i'].v = v_rest_i - 40. * b.mV
print '...creating recurrent connections'
for name in self.population_names:
# set the adaptive additive threshold parameter at 20mV
self.neuron_groups['e'].theta = np.ones(
(self.n_excitatory)) * 20.0 * b.mV
for connection_type in self.recurrent_connection_names:
if connection_type == 'ei':
# create connection name (composed of population and connection types)
connection_name = name + connection_type[
0] + name + connection_type[1]
# create a connection from the first group in conn_name with the second group
self.connections[connection_name] = b.Connection(self.neuron_groups[connection_name[0:2]], \
self.neuron_groups[connection_name[2:4]], structure='sparse', state='g' + conn_type[0])
# instantiate the created connection
for feature in xrange(self.conv_features):
for n in xrange(self.n_excitatory_patch):
self.connections[conn_name][feature * self.n_excitatory_patch + n, \
feature * self.n_excitatory_patch + n] = 10.4
elif connection_type == 'ie':
# create connection name (composed of population and connection types)
connection_name = name + connection_type[
0] + name + connection_type[1]
# create a connection from the first group in conn_name with the second group
self.connections[connection_name] = b.Connection(self.neuron_groups[connection_name[0:2]], \
self.neuron_groups[connection_name[2:4]], structure='sparse', state='g' + conn_type[0])
# instantiate the created connection
for feature in xrange(self.conv_features):
for other_feature in xrange(self.conv_features):
if feature != other_feature:
for n in xrange(self.n_excitatory_patch):
self.connections[connection_name][feature * self.n_excitatory_patch + n, \
other_feature * self.n_excitatory_patch + n] = 17.4
# adding random inhibitory connections as specified
if self.random_inhibition_prob != 0.0:
for feature in xrange(self.conv_features):
for other_feature in xrange(self.conv_features):
for n_this in xrange(self.n_excitatory_patch):
for n_other in xrange(
self.n_excitatory_patch):
if n_this != n_other:
if b.random(
) < self.random_inhibition_prob:
self.connections[connection_name][feature * self.n_excitatory_patch + n_this, \
other_feature * self.n_excitatory_patch + n_other] = 17.4
elif connection_type == 'ee':
# create connection name (composed of population and connection types)
connection_name = name + connection_type[
0] + name + connection_type[1]
# create a connection from the first group in conn_name with the second group
self.connections[connection_name] = b.Connection(self.neuron_groups[connection_name[0:2]], \
self.neuron_groups[connection_name[2:4]], structure='sparse', state='g' + connection_type[0])
# instantiate the created connection
if self.connectivity == 'all':
for feature in xrange(self.conv_features):
for other_feature in xrange(self.conv_features):
if feature != other_feature:
for this_n in xrange(
self.n_excitatory_patch):
for other_n in xrange(
self.n_excitatory_patch):
if is_lattice_connection(
self.
n_excitatory_patch_sqrt,
this_n, other_n):
self.connections[connection_name][feature * self.n_excitatory_patch + this_n, \
other_feature * self.n_excitatory_patch + other_n] = \
(b.random() + 0.01) * 0.3
elif self.connectivity == 'pairs':
for feature in xrange(self.conv_features):
if feature % 2 == 0:
for this_n in xrange(self.n_excitatory_patch):
for other_n in xrange(
self.n_excitatory_patch):
if is_lattice_connection(
self.n_excitatory_patch_sqrt,
this_n, other_n):
self.connections[connection_name][feature * self.n_excitatory_patch + this_n, \
(feature + 1) * self.n_excitatory_patch + other_n] = (b.random() + 0.01) * 0.3
elif feature % 2 == 1:
for this_n in xrange(self.n_excitatory_patch):
for other_n in xrange(
self.n_excitatory_patch):
if is_lattice_connection(
self.n_excitatory_patch_patch,
this_n, other_n):
self.connections[connection_name][feature * self.n_excitatory_patch + this_n, \
(feature - 1) * self.n_excitatory_patch + other_n] = (b.random() + 0.01) * 0.3
elif connectivity == 'linear':
for feature in xrange(self.conv_features):
if feature != self.conv_features - 1:
for this_n in xrange(self.n_excitatory_patch):
for other_n in xrange(
self.n_excitatory_patch):
if is_lattice_connection(
self.n_excitatory_patch_sqrt,
this_n, other_n):
self.connections[connection_name][feature * self.n_excitatory_patch + this_n, \
(feature + 1) * self.n_excitatory_patch + other_n] = \
(b.random() + 0.01) * 0.3
if feature != 0:
for this_n in xrange(self.n_excitatory_patch):
for other_n in xrange(
self.n_excitatory_patch):
if is_lattice_connection(
self.n_excitatory_patch_sqrt,
this_n, other_n):
self.connections[connection_name][feature * self.n_excitatory_patch + this_n, \
(feature - 1) * self.n_excitatory_patch + other_n] = \
(b.random() + 0.01) * 0.3
elif self.connectivity == 'none':
pass
# if STDP from excitatory -> excitatory is on and this connection is excitatory -> excitatory
if 'ee' in self.recurrent_conn_names:
self.stdp_methods[name + 'e' + name + 'e'] = b.STDP(self.connections[name + 'e' + name + 'e'], \
eqs=eqs_stdp_ee, pre=eqs_stdp_pre_ee, \
post=eqs_stdp_post_ee, wmin=0., wmax=self.wmax_ee)
print '...creating monitors for:', name
# spike rate monitors for excitatory and inhibitory neuron populations
self.rate_monitors[name + 'e'] = b.PopulationRateMonitor(self.neuron_groups[name + 'e'], \
bin=(self.single_example_time + self.resting_time) / b.second)
self.rate_monitors[name + 'i'] = b.PopulationRateMonitor(self.neuron_groups[name + 'i'], \
bin=(self.single_example_time + self.resting_time) / b.second)
self.spike_counters[name + 'e'] = b.SpikeCounter(
self.neuron_groups[name + 'e'])
# record neuron population spikes
self.spike_monitors[name + 'e'] = b.SpikeMonitor(
self.neuron_groups[name + 'e'])
self.spike_monitors[name + 'i'] = b.SpikeMonitor(
self.neuron_groups[name + 'i'])
if do_plot:
b.figure(self.fig_num)
fig_num += 1
b.ion()
b.subplot(211)
b.raster_plot(self.spike_monitors['Ae'],
refresh=1000 * b.ms,
showlast=1000 * b.ms)
b.subplot(212)
b.raster_plot(self.spike_monitors['Ai'],
refresh=1000 * b.ms,
showlast=1000 * b.ms)
# specifying locations of lattice connections
self.lattice_locations = {}
if self.connectivity == 'all':
for this_n in xrange(self.conv_features * self.n_excitatory_patch):
self.lattice_locations[this_n] = [ other_n for other_n in xrange(self.conv_features * self.n_excitatory_patch) \
if is_lattice_connection(self.n_excitatory_patch_sqrt, \
this_n % self.n_excitatory_patch, other_n % self.n_excitatory_patch) ]
elif self.connectivity == 'pairs':
for this_n in xrange(self.conv_features * self.n_excitatory_patch):
self.lattice_locations[this_n] = []
for other_n in xrange(self.conv_features *
self.n_excitatory_patch):
if this_n // self.n_excitatory_patch % 2 == 0:
if is_lattice_connection(self.n_excitatory_patch_sqrt, this_n % self.n_excitatory_patch, \
other_n % self.n_excitatory_patch) and \
other_n // self.n_excitatory_patch == this_n // self.n_excitatory_patch + 1:
self.lattice_locations[this_n].append(other_n)
elif this_n // self.n_excitatory_patch % 2 == 1:
if is_lattice_connection(self.n_excitatory_patch_sqrt, this_n % self.n_excitatory_patch, \
other_n % self.n_excitatory_patch) and \
other_n // self.n_excitatory_patch == this_n // self.n_excitatory_patch - 1:
self.lattice_locations[this_n].append(other_n)
elif self.connectivity == 'linear':
for this_n in xrange(self.conv_features * self.n_excitatory_patch):
self.lattice_locations[this_n] = []
for other_n in xrange(conv_features * self.n_excitatory_patch):
if this_n // self.n_excitatory_patch != self.conv_features - 1:
if is_lattice_connection(self.n_excitatory_patch_sqrt, this_n % self.n_excitatory_patch, \
other_n % self.n_excitatory_patch) and \
other_n // self.n_excitatory_patch == this_n // self.n_excitatory_patch + 1:
self.lattice_locations[this_n].append(other_n)
elif this_n // self.n_excitatory_patch != 0:
if is_lattice_connection(self.n_excitatory_patch_sqrt, this_n % self.n_excitatory_patch, \
other_n % self.n_excitatory_patch) and \
other_n // self.n_excitatory_patch == this_n // self.n_excitatory_patch - 1:
self.lattice_locations[this_n].append(other_n)
# setting up parameters for weight normalization between patches
num_lattice_connections = sum(
[len(value) for value in lattice_locations.values()])
self.weight['ee_recurr'] = (num_lattice_connections /
self.conv_features) * 0.15
# creating Poission spike train from input image (784 vector, 28x28 image)
for name in self.input_population_names:
self.input_groups[name + 'e'] = b.PoissonGroup(self.n_input, 0)
self.rate_monitors[name + 'e'] = b.PopulationRateMonitor(self.input_groups[name + 'e'], \
bin=(self.single_example_time + self.resting_time) / b.second)
# creating connections from input Poisson spike train to convolution patch populations
for name in self.input_connection_names:
print '\n...creating connections between', name[0], 'and', name[1]
# for each of the input connection types (in this case, excitatory -> excitatory)
for connection_type in self.input_conn_names:
# saved connection name
connection_name = name[0] + connection_type[0] + name[
1] + connection_type[1]
# create connections from the windows of the input group to the neuron population
self.input_connections[connection_name] = b.Connection(self.input_groups['Xe'], \
self.neuron_groups[name[1] + connection_type[1]], structure='sparse', \
state='g' + connection_type[0], delay=True, max_delay=self.delay[connection_type][1])
for feature in xrange(self.conv_features):
for n in xrange(self.n_excitatory_patch):
for idx in xrange(self.conv_size**2):
self.input_connections[connection_name][self.convolution_locations[n][idx], \
feature * self.n_excitatory_patch + n] = (b.random() + 0.01) * 0.3
# if excitatory -> excitatory STDP is specified, add it here (input to excitatory populations)
print '...creating STDP for connection', name
# STDP connection name
connection_name = name[0] + connection_type[0] + name[
1] + connection_type[1]
# create the STDP object
self.stdp_methods[connection_name] = b.STDP(self.input_connections[connection_name], \
eqs=eqs_stdp_ee, pre=eqs_stdp_pre_ee, post=eqs_stdp_post_ee, wmin=0., wmax=self.wmax_ee)
print '\n'
bin=(single_example_time + resting_time) / b.second)
rate_monitors[name + 'i'] = b.PopulationRateMonitor(
neuron_groups[name + 'i'],
bin=(single_example_time + resting_time) / b.second)
spike_counters[name + 'e'] = b.SpikeCounter(neuron_groups[name + 'e'])
# record neuron population spikes if specified
if record_spikes:
spike_monitors[name + 'e'] = b.SpikeMonitor(neuron_groups[name + 'e'])
spike_monitors[name + 'i'] = b.SpikeMonitor(neuron_groups[name + 'i'])
if record_spikes and plot:
b.figure(fig_num)
fig_num += 1
b.ion()
b.subplot(211)
b.raster_plot(spike_monitors['Ae'],
refresh=1000 * b.ms,
showlast=1000 * b.ms)
b.subplot(212)
b.raster_plot(spike_monitors['Ai'],
refresh=1000 * b.ms,
showlast=1000 * b.ms)
#################################################################
# CREATE INPUT POPULATION AND CONNECTIONS FROM INPUT POPULATION #
#################################################################
# creating convolution locations inside the input image
convolution_locations = {}
for n in xrange(n_e):
def run(self):
#------------------------------------------------------------------------------
# run the simulation and set inputs
#------------------------------------------------------------------------------
previousSpikeCountB = np.zeros(self.nE)
previousSpikeCountC = np.zeros(self.nE)
self.resultMonitor = np.zeros((self.numExamples,len(self.populationNames)))
start = time.time()
if self.recordSpikes:
b.figure()
b.ion()
b.subplot(211)
b.raster_plot(self.spikeMonitors['He'], refresh=1000*b.ms, showlast=1000*b.ms)
b.subplot(212)
b.raster_plot(self.spikeMonitors['Hi'], refresh=1000*b.ms, showlast=1000*b.ms)
# realTimeMonitor = None
# realTimeMonitor = rltmMon.RealtimeConnectionMonitor(self.connections['HeAe'], cmap=cm.get_cmap('gist_rainbow'),
# wmin=0, wmax=self.wmaxEE, clock=Clock(1000*b.ms))
for j in xrange(int(self.numExamples)):
if self.restingTime or j==0:
for i,name in enumerate(self.inputPopulationNames):
rates = np.ones(self.nE) * 0
self.inputGroups[name+'e'].rate = rates
self.net.run(self.restingTime)#, report='text')
if j%self.normalization_interval == 0:
self.normalizeWeights()
print 'set new rates of the inputs'
self.popValues = [0]*len(self.inputPopulationNames)
for i,name in enumerate(self.inputPopulationNames):
if name == 'X':
self.popValues[i] = np.random.rand();
rates = self.createTopoInput(self.nE, self.popValues[i])
self.resultMonitor[j,0] = self.popValues[i]
else:
if self.testMode:
rates = np.ones(self.nE) * 0
elif name == 'Y':
self.popValues[i] = (self.popValues[0]*2) % 1.
rates = self.createTopoInput(self.nE, self.popValues[i])
elif name == 'Z':
self.popValues[i] = (self.popValues[0]**2) % 1.
rates = self.createTopoInput(self.nE, self.popValues[i])
if self.testMode:
rates += noise
self.inputGroups[name+'e'].rate = rates
print 'run number:', j+1, 'of', int(self.numExamples)
self.net.run(self.singleExampleTime)#, report='text')
currentSpikeCountB = np.asarray(self.spikeCounters['Be'].count[:]) - previousSpikeCountB
currentSpikeCountC = np.asarray(self.spikeCounters['Ce'].count[:]) - previousSpikeCountC
# print currentSpikeCount, np.asarray(spikeCounters['Ce'].count[:]), previousSpikeCount
previousSpikeCountB = np.copy(self.spikeCounters['Be'].count[:])
previousSpikeCountC = np.copy(self.spikeCounters['Ce'].count[:])
self.resultMonitor[j,1] = self.computePopVector(currentSpikeCountB)
self.resultMonitor[j,2] = self.computePopVector(currentSpikeCountC)
difference = np.abs((self.resultMonitor[j,0]**2)%1. - self.resultMonitor[j,2])
if difference > 0.5:
difference = 1-difference
print 'Pop. activity: ', self.resultMonitor[j,2], ', Desired activity: ', (self.resultMonitor[j,0]**2)%1., ', Difference: ', difference
if not self.testMode:
if self.numExamples <= 1000:
if j%100 == 0:
self.saveConnections(str(j))
else:
if j%1000 == 0:
self.saveConnections(str(j))
end = time.time()
print 'time needed to simulate:', end - start
#------------------------------------------------------------------------------
# save results
#------------------------------------------------------------------------------
print 'save results'
if self.testMode:
np.savetxt(self.dataPath + 'activity/resultPopVecs' + str(self.numExamples) + '.txt', self.resultMonitor)
else:
self.saveConnections(str(j))
self.normalizeWeights()
self.saveConnections()
neuron_groups[i] = neuron_groups['e'].subgroup(num_neurons[i])
# print 'create monitors for', i
rate_monitors[i] = b.PopulationRateMonitor(neuron_groups[i], bin = (single_example_time+resting_time)/b.second)
spike_counters[i] = b.SpikeCounter(neuron_groups[i])
if record_spikes:
spike_monitors[i] = b.SpikeMonitor(neuron_groups[i])
state_monitors[i] = b.MultiStateMonitor(neuron_groups[i], ['v'], record=[0])
if record_spikes:
b.figure()
b.ion()
b.subplot(211)
b.raster_plot(spike_monitors[1], refresh=1000*b.ms, showlast=1000*b.ms)
b.subplot(212)
b.raster_plot(spike_monitors[1], refresh=1000*b.ms, showlast=1000*b.ms)
#------------------------------------------------------------------------------
# create input population
#------------------------------------------------------------------------------
pop_values = [0,0,0]
for i,name in enumerate(input_population_names):
if poisson_inputs:
input_groups[name] = b.PoissonGroup(n_input*2, 0)
else:
input_groups[name] = b.SpikeGeneratorGroup(n_input*2, [])
if not test_mode:
save_connections()
else:
np.save(data_path + 'activity/resultPopVecs' + str(num_examples),
result_monitor)
np.save(data_path + 'activity/inputNumbers' + str(num_examples),
input_numbers)
#------------------------------------------------------------------------------
# plot results
#------------------------------------------------------------------------------
if rate_monitors:
b.figure(fig_num)
fig_num += 1
for i, name in enumerate(rate_monitors):
b.subplot(len(rate_monitors), 1, i)
b.plot(rate_monitors[name].times / b.second, rate_monitors[name].rate,
'.')
b.title('Rates of population ' + name)
if spike_monitors:
b.figure(fig_num)
fig_num += 1
for i, name in enumerate(spike_monitors):
b.subplot(len(spike_monitors), 1, i)
b.raster_plot(spike_monitors[name])
b.title('Spikes of population ' + name)
if spike_counters:
b.figure(fig_num)
fig_num += 1
def run(self):
#------------------------------------------------------------------------------
# run the simulation and set inputs
#------------------------------------------------------------------------------
start = time.time()
previousSpikeCount = np.zeros((self.nE,len(self.populationNames)))
currentSpikeCount = np.zeros((self.nE,len(self.populationNames)))
if self.recordSpikes:
b.figure()
b.ion()
b.subplot(411)
b.raster_plot(self.spikeMonitors['Ae'], refresh=1000*b.ms, showlast=1000*b.ms)
b.subplot(412)
b.raster_plot(self.spikeMonitors['Be'], refresh=1000*b.ms, showlast=1000*b.ms)
b.subplot(413)
b.raster_plot(self.spikeMonitors['Ce'], refresh=1000*b.ms, showlast=1000*b.ms)
b.subplot(414)
b.raster_plot(self.spikeMonitors['He'], refresh=1000*b.ms, showlast=1000*b.ms)
# realTimeMonitor = None
# realTimeMonitor = rltmMon.RealtimeConnectionMonitor(self.connections['HeAe'], cmap=cm.get_cmap('gist_rainbow'),
# wmin=0, wmax=self.wmaxEE, clock=Clock(1000*b.ms))
for j in xrange(int(self.numExamples)):
if self.restingTime or j==0:
for i,name in enumerate(self.inputPopulationNames):
rates = np.ones(self.nE) * 0
self.inputGroups[name+'e'].rate = rates
self.net.run(self.restingTime)#, report='text')
if j%self.normalization_interval == 0:
self.normalizeWeights()
print 'set new rates of the inputs'
self.setNewInput(self,j)
print 'run number:', j+1, 'of', int(self.numExamples)
self.net.run(self.singleExampleTime)#, report='text')
for i,name in enumerate(self.populationNames):
name += 'e'
currentSpikeCount[:,i] = np.asarray(self.spikeCounters[name].count[:]) - previousSpikeCount[:,i]
# print currentSpikeCount, np.asarray(spikeCounters['Ce'].count[:]), previousSpikeCount
previousSpikeCount[:,i] = np.copy(self.spikeCounters[name].count[:])
self.resultMonitor[j,i] = self.computePopVector(currentSpikeCount[:,i])
print name, 'pop. activity: ', self.resultMonitor[j,i], ', spikecount:', sum(currentSpikeCount[:,i])
for i,name in enumerate(self.inputPopulationNames):
print name, 'pop. activity: ', (self.popValues[j,i])
if not self.testMode:
if self.numExamples <= 1000:
if j%100==0 and not j==0:
self.saveConnections(str(j))
else:
if j%10000==0 and not j==0:
self.saveConnections(str(j))
end = time.time()
print 'time needed to simulate:', end - start
#------------------------------------------------------------------------------
# save results
#------------------------------------------------------------------------------
print 'save results'
if self.testMode:
np.savetxt(self.dataPath + 'activity/resultPopVecs' + str(self.numExamples) + '.txt', self.resultMonitor)
np.savetxt(self.dataPath + 'activity/spikeCount' + str(self.gaussian_peak_low) + '.txt',
self.spikeCounters['Ae'].count[:]/(self.singleExampleTime*int(self.numExamples)))
np.savetxt(self.dataPath + 'activity/inputSpikeCount' + str(self.gaussian_peak_low) + '.txt',
self.spikeCounters['Xe'].count[:]/(self.singleExampleTime*int(self.numExamples)))
np.savetxt(self.dataPath + 'activity/popVecs' + str(self.numExamples) + '.txt', self.popValues)
else:
self.saveConnections(str(j))
self.normalizeWeights()
self.saveConnections()
def plotResults(self):
#------------------------------------------------------------------------------
# plot results
#------------------------------------------------------------------------------
if self.rateMonitors:
b.figure()
for i, name in enumerate(self.rateMonitors):
b.subplot(len(self.rateMonitors), 1, i)
b.plot(self.rateMonitors[name].times/b.second, self.rateMonitors[name].rate, '.')
b.title('rates of population ' + name)
if self.spikeMonitors:
b.figure()
for i, name in enumerate(self.spikeMonitors):
b.subplot(len(self.spikeMonitors), 1, i)
b.raster_plot(self.spikeMonitors[name])
b.title('spikes of population ' + name)
if name=='Ce':
timePoints = np.linspace(0+(self.singleExampleTime+self.restingTime)/(2*b.second)*1000,
self.runtime/b.second*1000-(self.singleExampleTime+self.restingTime)/(2*b.second)*1000,
self.numExamples)
b.plot(timePoints, self.resultMonitor[:,0]*self.nE, 'g')
b.plot(timePoints, self.resultMonitor[:,1]*self.nE, 'r')
if self.stateMonitors:
b.figure()
for i, name in enumerate(self.stateMonitors):
b.plot(self.stateMonitors[name].times/b.second, self.stateMonitors[name]['v'][0], label = name + ' v 0')
b.legend()
b.title('membrane voltages of population ' + name)
b.figure()
for i, name in enumerate(self.stateMonitors):
b.plot(self.stateMonitors[name].times/b.second, self.stateMonitors[name]['ge'][0], label = name + ' v 0')
b.legend()
b.title('conductances of population ' + name)
plotWeights = [
# 'XeAe',
# 'XeAi',
# 'AeAe',
# 'AeAi',
# 'AiAe',
# 'AiAi',
# 'BeBe',
# 'BeBi',
# 'BiBe',
# 'BiBi',
# 'CeCe',
# 'CeCi',
'CiCe',
# 'CiCi',
# 'HeHe',
# 'HeHi',
# 'HiHe',
# 'HiHi',
'AeHe',
# 'BeHe',
# 'CeHe',
'HeAe',
# 'HeBe',
# 'HeCe',
]
for name in plotWeights:
b.figure()
# my_cmap = matplotlib.colors.LinearSegmentedColormap.from_list('own2',['#f4f4f4', '#000000'])
# my_cmap2 = matplotlib.colors.LinearSegmentedColormap.from_list('own2',['#000000', '#f4f4f4'])
if name[1]=='e':
nSrc = self.nE
else:
nSrc = self.nI
if name[3]=='e':
nTgt = self.nE
else:
nTgt = self.nI
w_post = np.zeros((nSrc, nTgt))
connMatrix = self.connections[name][:]
for i in xrange(nSrc):
w_post[i, connMatrix.rowj[i]] = connMatrix.rowdata[i]
im2 = b.imshow(w_post, interpolation="nearest", vmin = 0, cmap=cm.get_cmap('gist_ncar')) #my_cmap
b.colorbar(im2)
b.title('weights of connection' + name)
if self.plotError:
error = np.abs(self.resultMonitor[:,1] - self.resultMonitor[:,0])
correctionIdxs = np.where(error > 0.5)[0]
correctedError = [1 - error[i] if (i in correctionIdxs) else error[i] for i in xrange(len(error))]
correctedErrorSum = np.average(correctedError)
b.figure()
b.scatter(self.resultMonitor[:,1], self.resultMonitor[:,0], c=range(len(error)), cmap=cm.get_cmap('gray'))
b.title('Error: ' + str(correctedErrorSum))
b.xlabel('Desired activity')
b.ylabel('Population activity')
b.figure()
error = np.abs(self.resultMonitor[:,1] - self.resultMonitor[:,0])
correctionIdxs = np.where(error > 0.5)[0]
correctedError = [1 - error[i] if (i in correctionIdxs) else error[i] for i in xrange(len(error))]
correctedErrorSum = np.average(correctedError)
b.scatter(self.resultMonitor[:,1], self.resultMonitor[:,0], c=self.resultMonitor[:,2], cmap=cm.get_cmap('gray'))
b.title('Error: ' + str(correctedErrorSum))
b.xlabel('Desired activity')
b.ylabel('Population activity')
b.ioff()
b.show()
neuron_groups[name + 'e'],
bin=(single_example_time + resting_time) / b.second)
rate_monitors[name + 'i'] = b.PopulationRateMonitor(
neuron_groups[name + 'i'],
bin=(single_example_time + resting_time) / b.second)
spike_counters[name + 'e'] = b.SpikeCounter(neuron_groups[name + 'e'])
if record_spikes:
spike_monitors[name + 'e'] = b.SpikeMonitor(neuron_groups[name + 'e'])
spike_monitors[name + 'i'] = b.SpikeMonitor(neuron_groups[name + 'i'])
if record_spikes:
b.figure(fig_num)
fig_num += 1
b.ion()
b.subplot(211)
b.raster_plot(spike_monitors['Ae'],
refresh=1000 * b.ms,
showlast=1000 * b.ms)
b.subplot(212)
b.raster_plot(spike_monitors['Ai'],
refresh=1000 * b.ms,
showlast=1000 * b.ms)
#------------------------------------------------------------------------------
# create input population and connections from input populations
#------------------------------------------------------------------------------
pop_values = [0, 0, 0]
for i, name in enumerate(input_population_names):
input_groups[name + 'e'] = b.PoissonGroup(n_input, 0)
rate_monitors[name + 'e'] = b.PopulationRateMonitor(
def plotTuningHeatmap(dataPath, gaussianDistances, ax1=None):
b.rcParams['font.size'] = 20
# averagingWindowSize = 1
nE = 1600
if ax1==None:
b.figure(figsize=(8,15))
fig_axis = b.subplot(1,1,1)
else:
b.sca(ax1)
fig_axis=ax1
path = dataPath + '/dist'+str(gaussianDistances[0])+'/' +'activity/'
spikeCount = np.load(path + 'spikeCountPerExample.npy')
numMeasurements = len(spikeCount[:,0,0])
measurementSpace = np.arange(numMeasurements)
populationSpikeCount = np.zeros((numMeasurements, len(gaussianDistances)))
for i,gaussianDistance in enumerate(gaussianDistances):
# distanceAngle = gaussianDistance * 2 * 360
path = dataPath + '/dist'+str(gaussianDistance)+'/' +'activity/'
spikeCount = np.load(path + 'spikeCountPerExample.npy')
for j in xrange(nE):
populationSpikeCount[:,i] += np.roll(spikeCount[:,j,0], int(-1.*j/nE*numMeasurements+ numMeasurements/2))
# j = 0
# populationSpikeCount[:,i] += np.roll(spikeCount[:,j,0], int(-1.*j/nE*numMeasurements+ numMeasurements/2))
# populationSpikeCount[:,i] = movingaverage(populationSpikeCount[:,i],averagingWindowSize)
populationSpikeCount[:,i] /= np.max(populationSpikeCount[:,i])
if gaussianDistance==0.:
mean = sum(measurementSpace*populationSpikeCount[:,i])/numMeasurements #note this correction
sigma = sum(populationSpikeCount[:,i]*(measurementSpace-mean)**2)/numMeasurements #note this correction
popt, _ = curve_fit(gaus,measurementSpace,populationSpikeCount[:,i],p0=[1,mean,sigma])
# tuningWidth = abs(popt[2])*2
tuningWidth = np.sum(populationSpikeCount>=0.5)
tuningWidthAngle = tuningWidth/50.*360.
print 'Gaussian amplitude:', popt[0], 'mean:', popt[1], 'std.:', popt[2], \
', tuning width:', tuningWidth, ', tuning width angle:', tuningWidthAngle
minX = max(0,round(numMeasurements/2-tuningWidth*2))
maxX = min(numMeasurements, round(numMeasurements/2+tuningWidth*2))
minY = round(0)
maxY = round(tuningWidth/50.*len(gaussianDistances)*2*2)
print minX, maxX, minY, maxY
croppedCount = populationSpikeCount[minX:maxX,minY:maxY]
fig_axis.imshow(croppedCount.transpose(), aspect='auto',
extent=[minX, maxX, 2, 0])
# b.colorbar()
fig_axis.set_xlabel('Distance from preferred stimulus, \ndivided by tuning width')
fig_axis.set_xticks([numMeasurements/2-tuningWidth*2, numMeasurements/2-tuningWidth,
numMeasurements/2, numMeasurements/2+tuningWidth, numMeasurements/2+tuningWidth*2])
fig_axis.set_xticklabels(['-2', '-1', '0', '1', '2'])
fig_axis.set_yticks([0., 0.5, 1, 1.5, 2])
fig_axis.spines['top'].set_visible(False)
fig_axis.spines['right'].set_visible(False)
fig_axis.spines['bottom'].set_visible(False)
fig_axis.spines['left'].set_visible(False)
fig_axis.get_xaxis().tick_bottom()
fig_axis.get_yaxis().tick_left()
if ax1==None:
b.savefig(dataPath + '/tuningHeatmap.png', dpi=300, bbox_inches='tight')
return tuningWidthAngle
def build_network():
global fig_num
neuron_groups['e'] = b.NeuronGroup(n_e_total, neuron_eqs_e, threshold=v_thresh_e, \
refractory=refrac_e, reset=scr_e, compile=True, freeze=True)
neuron_groups['i'] = b.NeuronGroup(n_e_total, neuron_eqs_i, threshold=v_thresh_i, \
refractory=refrac_i, reset=v_reset_i, compile=True, freeze=True)
for name in population_names:
print '...Creating neuron group:', name
# get a subgroup of size 'n_e' from all exc
neuron_groups[name + 'e'] = neuron_groups['e'].subgroup(conv_features *
n_e)
# get a subgroup of size 'n_i' from the inhibitory layer
neuron_groups[name + 'i'] = neuron_groups['i'].subgroup(conv_features *
n_e)
# start the membrane potentials of these groups 40mV below their resting potentials
neuron_groups[name + 'e'].v = v_rest_e - 40. * b.mV
neuron_groups[name + 'i'].v = v_rest_i - 40. * b.mV
print '...Creating recurrent connections'
for name in population_names:
neuron_groups['e'].theta = np.load(
os.path.join(best_weights_dir,
'_'.join(['theta_A', ending + '_best.npy'])))
for conn_type in recurrent_conn_names:
if conn_type == 'ei':
# create connection name (composed of population and connection types)
conn_name = name + conn_type[0] + name + conn_type[1]
# create a connection from the first group in conn_name with the second group
connections[conn_name] = b.Connection(
neuron_groups[conn_name[0:2]],
neuron_groups[conn_name[2:4]],
structure='sparse',
state='g' + conn_type[0])
# instantiate the created connection
for feature in xrange(conv_features):
for n in xrange(n_e):
connections[conn_name][feature * n_e + n,
feature * n_e + n] = 10.4
elif conn_type == 'ie':
# create connection name (composed of population and connection types)
conn_name = name + conn_type[0] + name + conn_type[1]
# load weight matrix
weight_matrix = np.load(
os.path.join(best_weights_dir,
'_'.join([conn_name, ending, 'best.npy'])))
# create a connection from the first group in conn_name with the second group
connections[conn_name] = b.Connection(
neuron_groups[conn_name[0:2]],
neuron_groups[conn_name[2:4]],
structure='sparse',
state='g' + conn_type[0])
# define the actual synaptic connections and strengths
for feature in xrange(conv_features):
for other_feature in xrange(conv_features):
if feature != other_feature:
for n in xrange(n_e):
connections[conn_name][feature * n_e + n,
other_feature * n_e +
n] = inhibition_level
print '...Creating monitors for:', name
# spike rate monitors for excitatory and inhibitory neuron populations
rate_monitors[name + 'e'] = b.PopulationRateMonitor(
neuron_groups[name + 'e'],
bin=(single_example_time + resting_time) / b.second)
rate_monitors[name + 'i'] = b.PopulationRateMonitor(
neuron_groups[name + 'i'],
bin=(single_example_time + resting_time) / b.second)
spike_counters[name + 'e'] = b.SpikeCounter(neuron_groups[name + 'e'])
# record neuron population spikes if specified
if record_spikes and do_plot:
spike_monitors[name + 'e'] = b.SpikeMonitor(neuron_groups[name +
'e'])
spike_monitors[name + 'i'] = b.SpikeMonitor(neuron_groups[name +
'i'])
if record_spikes and do_plot:
b.figure(fig_num, figsize=(8, 6))
b.ion()
b.subplot(211)
b.raster_plot(spike_monitors['Ae'],
refresh=1000 * b.ms,
showlast=1000 * b.ms,
title='Excitatory spikes per neuron')
b.subplot(212)
b.raster_plot(spike_monitors['Ai'],
refresh=1000 * b.ms,
showlast=1000 * b.ms,
title='Inhibitory spikes per neuron')
b.tight_layout()
fig_num += 1
# creating Poission spike train from input image (784 vector, 28x28 image)
for name in input_population_names:
input_groups[name + 'e'] = b.PoissonGroup(n_input, 0)
rate_monitors[name + 'e'] = b.PopulationRateMonitor(
input_groups[name + 'e'],
bin=(single_example_time + resting_time) / b.second)
# creating connections from input Poisson spike train to excitatory neuron population(s)
for name in input_connection_names:
print '\n...Creating connections between', name[0], 'and', name[1]
# for each of the input connection types (in this case, excitatory -> excitatory)
for conn_type in input_conn_names:
# saved connection name
conn_name = name[0] + conn_type[0] + name[1] + conn_type[1]
# get weight matrix depending on training or test phase
weight_matrix = np.load(
os.path.join(best_weights_dir,
'_'.join([conn_name, ending + '_best.npy'])))
# create connections from the windows of the input group to the neuron population
input_connections[conn_name] = b.Connection(input_groups['Xe'], neuron_groups[name[1] + conn_type[1]], \
structure='sparse', state='g' + conn_type[0], delay=True, max_delay=delay[conn_type][1])
for feature in xrange(conv_features):
for n in xrange(n_e):
for idx in xrange(conv_size**2):
input_connections[conn_name][convolution_locations[n][idx], feature * n_e + n] = \
weight_matrix[convolution_locations[n][idx], feature * n_e + n]
if do_plot:
plot_2d_input_weights()
fig_num += 1
print '\n'
def build_network():
global fig_num
neuron_groups['e'] = b.NeuronGroup(n_e_total,
neuron_eqs_e,
threshold=v_thresh_e,
refractory=refrac_e,
reset=scr_e,
compile=True,
freeze=True)
neuron_groups['i'] = b.NeuronGroup(n_e_total,
neuron_eqs_i,
threshold=v_thresh_i,
refractory=refrac_i,
reset=v_reset_i,
compile=True,
freeze=True)
for name in ['A']:
print '...Creating neuron group:', name
# get a subgroup of size 'n_e' from all exc
neuron_groups[name + 'e'] = neuron_groups['e'].subgroup(conv_features *
n_e)
# get a subgroup of size 'n_i' from the inhibitory layer
neuron_groups[name + 'i'] = neuron_groups['i'].subgroup(conv_features *
n_e)
# start the membrane potentials of these groups 40mV below their resting potentials
neuron_groups[name + 'e'].v = v_rest_e - 40. * b.mV
neuron_groups[name + 'i'].v = v_rest_i - 40. * b.mV
print '...Creating recurrent connections'
for name in ['A']:
neuron_groups['e'].theta = np.load(
os.path.join(best_weights_dir,
'_'.join(['theta_A', ending + '_best.npy'])))
for conn_type in ['ei', 'ie']:
if conn_type == 'ei':
# create connection name (composed of population and connection types)
conn_name = name + conn_type[0] + name + conn_type[1]
# create a connection from the first group in conn_name with the second group
connections[conn_name] = b.Connection(neuron_groups[conn_name[0:2]], \
neuron_groups[conn_name[2:4]], structure='sparse', state='g' + conn_type[0])
# instantiate the created connection
for feature in xrange(conv_features):
for n in xrange(n_e):
connections[conn_name][feature * n_e + n,
feature * n_e + n] = 10.4
elif conn_type == 'ie' and not remove_inhibition:
# create connection name (composed of population and connection types)
conn_name = name + conn_type[0] + name + conn_type[1]
# create a connection from the first group in conn_name with the second group
connections[conn_name] = b.Connection(neuron_groups[conn_name[0:2]], \
neuron_groups[conn_name[2:4]], structure='sparse', state='g' + conn_type[0])
# define the actual synaptic connections and strengths
for feature in xrange(conv_features):
if inhib_scheme in ['far', 'strengthen']:
for other_feature in set(range(conv_features)) - set(
neighbor_mapping[feature]):
if inhib_scheme == 'far':
for n in xrange(n_e):
connections[conn_name][feature * n_e + n,
other_feature *
n_e + n] = 17.4
elif inhib_scheme == 'strengthen':
if n_e == 1:
x, y = feature // np.sqrt(
n_e_total), feature % np.sqrt(
n_e_total)
x_, y_ = other_feature // np.sqrt(
n_e_total), other_feature % np.sqrt(
n_e_total)
else:
x, y = feature // np.sqrt(
conv_features), feature % np.sqrt(
conv_features)
x_, y_ = other_feature // np.sqrt(
conv_features
), other_feature % np.sqrt(conv_features)
for n in xrange(n_e):
connections[conn_name][feature * n_e + n, other_feature * n_e + n] = \
min(17.4, inhib_const * np.sqrt(euclidean([x, y], [x_, y_])))
elif inhib_scheme == 'increasing':
for other_feature in xrange(conv_features):
if n_e == 1:
x, y = feature // np.sqrt(
n_e_total), feature % np.sqrt(n_e_total)
x_, y_ = other_feature // np.sqrt(
n_e_total), other_feature % np.sqrt(
n_e_total)
else:
x, y = feature // np.sqrt(
conv_features), feature % np.sqrt(
conv_features)
x_, y_ = other_feature // np.sqrt(
conv_features), other_feature % np.sqrt(
conv_features)
if feature != other_feature:
for n in xrange(n_e):
connections[conn_name][feature * n_e + n, other_feature * n_e + n] = \
min(17.4, inhib_const * np.sqrt(euclidean([x, y], [x_, y_])))
else:
raise Exception(
'Expecting one of "far", "increasing", or "strengthen" for argument "inhib_scheme".'
)
# spike rate monitors for excitatory and inhibitory neuron populations
rate_monitors[name + 'e'] = b.PopulationRateMonitor(
neuron_groups[name + 'e'],
bin=(single_example_time + resting_time) / b.second)
rate_monitors[name + 'i'] = b.PopulationRateMonitor(
neuron_groups[name + 'i'],
bin=(single_example_time + resting_time) / b.second)
spike_counters[name + 'e'] = b.SpikeCounter(neuron_groups[name + 'e'])
# record neuron population spikes if specified
if record_spikes:
spike_monitors[name + 'e'] = b.SpikeMonitor(neuron_groups[name +
'e'])
spike_monitors[name + 'i'] = b.SpikeMonitor(neuron_groups[name +
'i'])
if record_spikes and do_plot:
if reset_state_vars:
time_window = single_example_time * 1000
else:
time_window = (single_example_time + resting_time) * 1000
b.figure(fig_num, figsize=(8, 6))
b.ion()
b.subplot(211)
b.raster_plot(spike_monitors['Ae'],
refresh=time_window * b.ms,
showlast=time_window * b.ms,
title='Excitatory spikes per neuron')
b.subplot(212)
b.raster_plot(spike_monitors['Ai'],
refresh=time_window * b.ms,
showlast=time_window * b.ms,
title='Inhibitory spikes per neuron')
b.tight_layout()
fig_num += 1
# creating Poission spike train from input image (784 vector, 28x28 image)
for name in ['X']:
input_groups[name + 'e'] = b.PoissonGroup(n_input, 0)
rate_monitors[name + 'e'] = b.PopulationRateMonitor(
input_groups[name + 'e'],
bin=(single_example_time + resting_time) / b.second)
# creating connections from input Poisson spike train to convolution patch populations
for name in ['XA']:
print '\n...Creating connections between', name[0], 'and', name[1]
# for each of the input connection types (in this case, excitatory -> excitatory)
for conn_type in ['ee_input']:
# saved connection name
conn_name = name[0] + conn_type[0] + name[1] + conn_type[1]
# get weight matrix depending on training or test phase
weight_matrix = np.load(
os.path.join(best_weights_dir,
'_'.join([conn_name, ending + '_best.npy'])))
# create connections from the windows of the input group to the neuron population
input_connections[conn_name] = b.Connection(input_groups['Xe'], neuron_groups[name[1] + \
conn_type[1]], structure='sparse', state='g' + conn_type[0], delay=True, max_delay=delay[conn_type][1])
for feature in xrange(conv_features):
for n in xrange(n_e):
for idx in xrange(conv_size**2):
input_connections[conn_name][convolution_locations[n][idx], feature * n_e + n] = \
weight_matrix[convolution_locations[n][idx], feature * n_e + n]
if do_plot:
plot_2d_input_weights()
fig_num += 1
