def run_sim(number_neurons=default_number_neurons, connection_probability=default_connection_probability, synaptic_weights=default_synaptic_weights, synaptic_time_constant=default_synaptic_time_constant, tend=300): '''run a simulation of a population of leaky integrate-and-fire excitatory neurons that are randomly connected. The population is injected with a transient current.''' from brian.units import mvolt, msecond, namp, Mohm import brian brian.clear() El = 0 * mvolt tau_m = 30 * msecond tau_syn = synaptic_time_constant * msecond R = 20 * Mohm v_threshold = 30 * mvolt v_reset = 0 * mvolt tau_refractory = 4 * msecond eqs = brian.Equations(''' dv/dt = (-(v - El) + R*I)/tau_m : volt I = I_syn + I_stim : amp dI_syn/dt = -I_syn/tau_syn : amp I_stim : amp ''') external_current = np.zeros(tend) external_current[np.arange(0, 100)] = 5 * namp group = brian.NeuronGroup( model=eqs, N=number_neurons, threshold=v_threshold, reset=v_reset, refractory=tau_refractory) group.I_stim = brian.TimedArray(external_current, dt=1*msecond) connections = brian.Connection(group, group, 'I_syn') connections.connect_random(sparseness=connection_probability, weight=synaptic_weights*namp) spike_monitor = brian.SpikeMonitor(group) population_rate_monitor = brian.PopulationRateMonitor(group, bin=10*msecond) brian.reinit() brian.run(tend * msecond) return spike_monitor, population_rate_monitor
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'
neuron_groups[connName[2:4]], weightMatrix) if ee_STDP_on: if 'ee' in recurrent_conn_names: stdp_methods[name + 'e' + name + 'e'] = b.STDP( connections[name + 'e' + name + 'e' + ending], eqs=eqs_stdp_ee, pre=eqs_stdp_pre_ee, 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)
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'])
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'
def fft_std(delta_u, run_num, new_connectivity, osc, rep): #bn.seed(int(time.time())) bn.reinit_default_clock() #bn.seed(1412958308+2) bn.defaultclock.dt = 0.5 * bn.ms #============================================================================== # Define constants for the model. #============================================================================== fft_file = './std_fft_p20_' rate_file = './std_rate_p20_' print delta_u print run_num print new_connectivity print rep if osc: T = 5.5 * bn.second else: T = 2.5 * bn.second n_tsteps = T / bn.defaultclock.dt fft_start = 0.5 * bn.second / bn.defaultclock.dt # Time window for the FFT computation ro = 1.2 * bn.Hz SEE1 = 1.0 SEE2 = 1.0 qee1 = 1.00 # Fraction of NMDA receptors for e to e connections qee2 = 0.00 qie1 = 1.00 # Fraction of NMDA receptors for e to i connections qie2 = 0.00 uee1 = 0.2 - delta_u uee2 = 0.2 + delta_u uie1 = 0.2 uie2 = 0.2 trec1 = 1000.0 * bn.ms trec2 = 1000.0 * bn.ms k = 0.65 #Jeo_const = 1.0#*bn.mV # Base strength of o (external) to e connections Ne = 3200 # number of excitatory neurons Ni = 800 # number of inhibitory neurons No = 20000 # number of external neurons N = Ne + Ni pcon = 0.2 # probability of connection Jee = 10.0 / (Ne * pcon) Jie = 10.0 / (Ne * pcon) Jii = k * 10.0 / (Ni * pcon) Jei = k * 10.0 / (Ni * pcon) Jeo = 1.0 El = -60.0 * bn.mV # leak reversal potential Vreset = -52.0 * bn.mV # reversal potential Vthresh = -40.0 * bn.mV # spiking threshold tref = 2.0 * bn.ms # refractory period te = 20.0 * bn.ms # membrane time constant of excitatory neurons ti = 10.0 * bn.ms # membrane time constant of inhibitory neruons tee_ampa = 10.0 * bn.ms # time const of ampa currents at excitatory neurons tee_nmda = 100.0 * bn.ms # time const of nmda currents at excitatory neurons tie_ampa = 10.0 * bn.ms # time const of ampa currents at inhibitory neurons tie_nmda = 100.0 * bn.ms # time const of nmda currents at inhibitory neurons tii_gaba = 10.0 * bn.ms # time const of GABA currents at inhibitory neurons tei_gaba = 10.0 * bn.ms # time const of GABA currents at excitatory neurons teo_input = 100.0 * bn.ms #============================================================================== # Define model structure #============================================================================== model = ''' dV/dt = (-(V-El)+J_ampa1*I_ampa1+J_nmda1*I_nmda1+J_ampa2*I_ampa2+J_nmda2*I_nmda2-J_gaba*I_gaba+J_input*I_input+eta)/tm : bn.volt dI_ampa1/dt = -I_ampa1/t_ampa : bn.volt dI_nmda1/dt = -I_nmda1/t_nmda : bn.volt dI_ampa2/dt = -I_ampa2/t_ampa : bn.volt dI_nmda2/dt = -I_nmda2/t_nmda : bn.volt dI_gaba/dt = -I_gaba/t_gaba : bn.volt dI_input/dt = (-I_input+mu)/t_input : bn.volt dx1/dt = (1-x1)/t1_rec : 1 dx2/dt = (1-x2)/t2_rec : 1 u1 : 1 t1_rec : bn.second u2 : 1 t2_rec : bn.second mu : bn.volt eta : bn.volt J_ampa1 : 1 J_nmda1 : 1 J_ampa2 : 1 J_nmda2 : 1 J_gaba : 1 J_input : 1 tm : bn.second t_ampa : bn.second t_nmda : bn.second t_gaba : bn.second t_input : bn.second ''' P_reset = "V=-52*bn.mV;x1+=-u1*x1;x2+=-u2*x2" Se_model = ''' we_ampa1 : bn.volt we_nmda1 : bn.volt we_ampa2 : bn.volt we_nmda2 : bn.volt ''' Se_pre = ('I_ampa1 += x1_pre*we_ampa1', 'I_nmda1 += x1_pre*we_nmda1', 'I_ampa2 += x2_pre*we_ampa2', 'I_nmda2 += x2_pre*we_nmda2') Si_model = ''' wi_gaba : bn.volt ''' Si_pre = 'I_gaba += wi_gaba' So_model = ''' wo_input : bn.volt ''' So_pre = 'I_input += wo_input' #============================================================================== # Define populations #============================================================================== P = bn.NeuronGroup(N, model, threshold=Vthresh, reset=P_reset, refractory=tref) Pe = P[0:Ne] Pe.tm = te Pe.t_ampa = tee_ampa Pe.t_nmda = tee_nmda Pe.t_gaba = tei_gaba Pe.t_input = teo_input Pe.I_ampa1 = 0 * bn.mV Pe.I_nmda1 = 0 * bn.mV Pe.I_ampa2 = 0 * bn.mV Pe.I_nmda2 = 0 * bn.mV Pe.I_gaba = 0 * bn.mV Pe.I_input = 0 * bn.mV Pe.V = (np.random.rand(Pe.V.size) * 12 - 52) * bn.mV Pe.x1 = 1.0 Pe.x2 = 1.0 Pe.u1 = uee1 Pe.u2 = uee2 Pe.t1_rec = trec1 Pe.t2_rec = trec2 Pi = P[Ne:(Ne + Ni)] Pi.tm = ti Pi.t_ampa = tie_ampa Pi.t_nmda = tie_nmda Pi.t_gaba = tii_gaba Pi.t_input = teo_input Pi.I_ampa1 = 0 * bn.mV Pi.I_nmda1 = 0 * bn.mV Pi.I_ampa2 = 0 * bn.mV Pi.I_nmda2 = 0 * bn.mV Pi.I_gaba = 0 * bn.mV Pi.I_input = 0 * bn.mV Pi.V = (np.random.rand(Pi.V.size) * 12 - 52) * bn.mV Pi.x1 = 1.0 Pi.x2 = 1.0 Pi.u1 = 0.0 Pi.u2 = 0.0 Pi.t1_rec = 1.0 Pi.t2_rec = 1.0 Pe.J_ampa1 = Jee * (1 - qee1) #*SEE1 Pe.J_nmda1 = Jee * qee1 #*SEE1 Pe.J_ampa2 = Jee * (1 - qee2) #*SEE2 Pe.J_nmda2 = Jee * qee2 #*SEE2 Pi.J_ampa1 = Jie * (1 - qie2) #*SEE2 Pi.J_nmda1 = Jie * qie2 #*SEE2 Pi.J_ampa2 = Jie * (1 - qie1) #*SEE1 Pi.J_nmda2 = Jie * qie1 #*SEE1 Pe.J_gaba = Jei Pi.J_gaba = Jii Pe.J_input = Jeo Pi.J_input = Jeo #============================================================================== # Define inputs #============================================================================== if osc: Pe.mu = 12.0 * bn.mV holder = np.zeros((n_tsteps, )) t_freq = np.linspace(0, 10, n_tsteps) fo = 0.2 # Smallest frequency in the signal fe = 10.0 # Largest frequency in the signal F = int(fe / 0.2) for m in range(1, F + 1): holder = holder + np.cos(2 * np.pi * m * fo * t_freq - m * (m - 1) * np.pi / F) holder = holder / np.max(holder) Pe.eta = bn.TimedArray(0.0 * bn.mV * holder) #, dt=0.5*bn.ms) Background_eo = bn.PoissonInput(Pe, N=1000, rate=1.05 * bn.Hz, weight=0.2 * bn.mV, state='I_input') Background_io = bn.PoissonInput(Pi, N=1000, rate=1.0 * bn.Hz, weight=0.2 * bn.mV, state='I_input') Pi.mu = 0 * bn.mV Pi.eta = 0 * bn.mV #, dt=0.5*bn.ms) Po = bn.PoissonGroup(No, rates=0 * bn.Hz) else: Background_eo = bn.PoissonInput(Pe, N=1000, rate=1.05 * bn.Hz, weight=0.2 * bn.mV, state='I_input') Background_io = bn.PoissonInput(Pi, N=1000, rate=1.0 * bn.Hz, weight=0.2 * bn.mV, state='I_input') holder_pe = np.zeros((n_tsteps, )) time_steps = np.linspace(0, T / bn.second, n_tsteps) holder_pe[time_steps < 0.5] = 0.0 * bn.mV holder_pe[time_steps >= 0.5] = 6.0 * bn.mV #25 holder_pe[time_steps > 1.5] = 0.0 * bn.mV #25 Pe.mu = bn.TimedArray(holder_pe) def firing_function(t, ro): if t > 0.5 * bn.second and t < 3.5 * bn.second: return 0.0 * bn.Hz else: return 0.0 * bn.Hz Pe.eta = 0 * bn.mV #, dt=0.5*bn.ms) Pi.mu = 0.0 * bn.mV Pi.eta = 0 * bn.mV #, dt=0.5*bn.ms) Po = bn.PoissonGroup(No, rates=lambda t: firing_function(t, ro)) #============================================================================== # Define synapses #============================================================================== See1 = bn.Synapses(Pe, Pe, model=Se_model, pre=Se_pre) See2 = bn.Synapses(Pe, Pe, model=Se_model, pre=Se_pre) Sie1 = bn.Synapses(Pe, Pi, model=Se_model, pre=Se_pre) Sie2 = bn.Synapses(Pe, Pi, model=Se_model, pre=Se_pre) Sei = bn.Synapses(Pi, Pe, model=Si_model, pre=Si_pre) Sii = bn.Synapses(Pi, Pi, model=Si_model, pre=Si_pre) Seo = bn.Synapses(Po, Pe, model=So_model, pre=So_pre) #============================================================================== # Define random connections #============================================================================== if new_connectivity: See1.connect_random(Pe, Pe, sparseness=pcon / 2.0) See2.connect_random(Pe, Pe, sparseness=pcon / 2.0) Sie1.connect_random(Pe, Pi, sparseness=pcon / 2.0) Sie2.connect_random(Pe, Pi, sparseness=pcon / 2.0) Sii.connect_random(Pi, Pi, sparseness=pcon) Sei.connect_random(Pi, Pe, sparseness=pcon) Seo.connect_random(Po, Pe, sparseness=pcon) print 'Saving' See1.save_connectivity('./See1_connections_std_saver_p20_' + str(run_num)) See2.save_connectivity('./See2_connections_std_saver_p20_' + str(run_num)) Sie1.save_connectivity('./Sie1_connections_std_saver_p20_' + str(run_num)) Sie2.save_connectivity('./Sie2_connections_std_saver_p20_' + str(run_num)) Sii.save_connectivity('./Sii_connections_std_saver_p20_' + str(run_num)) Sei.save_connectivity('./Sei_connections_std_saver_p20_' + str(run_num)) Seo.save_connectivity('./Seo_connections_std_saver_p20_' + str(run_num)) else: print 'Loading' See1.load_connectivity('./See1_connections_std_saver_p20_' + str(run_num)) See2.load_connectivity('./See2_connections_std_saver_p20_' + str(run_num)) Sie1.load_connectivity('./Sie1_connections_std_saver_p20_' + str(run_num)) Sie2.load_connectivity('./Sie2_connections_std_saver_p20_' + str(run_num)) Sii.load_connectivity('./Sii_connections_std_saver_p20_' + str(run_num)) Sei.load_connectivity('./Sei_connections_std_saver_p20_' + str(run_num)) Seo.load_connectivity('./Seo_connections_std_saver_p20_' + str(run_num)) See1.we_ampa1 = SEE1 * 1.0 * bn.mV / tee_ampa See1.we_nmda1 = SEE1 * 1.0 * bn.mV / tee_nmda See1.we_ampa2 = 0.0 * bn.mV / tee_ampa See1.we_nmda2 = 0.0 * bn.mV / tee_nmda See2.we_ampa1 = 0.0 * bn.mV / tee_ampa See2.we_nmda1 = 0.0 * bn.mV / tee_nmda See2.we_ampa2 = SEE2 * 1.0 * bn.mV / tee_ampa See2.we_nmda2 = SEE2 * 1.0 * bn.mV / tee_nmda Sie1.we_ampa1 = 0.0 * bn.mV / tie_ampa Sie1.we_nmda1 = 0.0 * bn.mV / tie_nmda Sie1.we_ampa2 = SEE1 * 1.0 * bn.mV / tie_ampa Sie1.we_nmda2 = SEE1 * 1.0 * bn.mV / tie_nmda Sie2.we_ampa1 = SEE2 * 1.0 * bn.mV / tie_ampa Sie2.we_nmda1 = SEE2 * 1.0 * bn.mV / tie_nmda Sie2.we_ampa2 = 0.0 * bn.mV / tie_ampa Sie2.we_nmda2 = 0.0 * bn.mV / tie_nmda Sei.wi_gaba = 1.0 * bn.mV / tei_gaba Sii.wi_gaba = 1.0 * bn.mV / tii_gaba Seo.wo_input = 1.0 * bn.mV / teo_input #============================================================================== # Define monitors #============================================================================== Pe_mon_V = bn.StateMonitor(Pe, 'V', timestep=10, record=True) Pe_mon_eta = bn.StateMonitor(Pe, 'eta', timestep=1, record=True) Pe_mon_ampa1 = bn.StateMonitor(Pe, 'I_ampa1', timestep=1, record=True) Pe_mon_nmda1 = bn.StateMonitor(Pe, 'I_nmda1', timestep=1, record=True) Pe_mon_ampa2 = bn.StateMonitor(Pe, 'I_ampa2', timestep=1, record=True) Pe_mon_nmda2 = bn.StateMonitor(Pe, 'I_nmda2', timestep=1, record=True) Pe_mon_gaba = bn.StateMonitor(Pe, 'I_gaba', timestep=1, record=True) Pe_mon_input = bn.StateMonitor(Pe, 'I_input', timestep=10, record=True) See1_mon_x = bn.StateMonitor(Pe, 'x1', timestep=10, record=True) See2_mon_x = bn.StateMonitor(Pe, 'x2', timestep=10, record=True) Pe_ratemon = bn.PopulationRateMonitor(Pe, bin=10.0 * bn.ms) Pi_ratemon = bn.PopulationRateMonitor(Pi, bin=10.0 * bn.ms) #============================================================================== # Run model #============================================================================== timer = 0 * bn.second t_start = time.time() bn.run(T, report='graphical') timer = timer + T print '-------------------------------------------------------' print 'Time is ' + str(timer) + ' seconds' t_end = time.time() print 'Time to compute last ' +str(T)+' seconds is: ' + \ str(t_end - t_start) + ' seconds' print '-------------------------------------------------------\n' Pe_mon_ampa1_vals = Pe.J_ampa1[0] * np.mean(Pe_mon_ampa1.values.T, axis=1) Pe_mon_nmda1_vals = Pe.J_nmda1[0] * np.mean(Pe_mon_nmda1.values.T, axis=1) Pe_mon_ampa2_vals = Pe.J_ampa2[0] * np.mean(Pe_mon_ampa2.values.T, axis=1) Pe_mon_nmda2_vals = Pe.J_nmda2[0] * np.mean(Pe_mon_nmda2.values.T, axis=1) Pe_mon_ampa_vals = Pe_mon_ampa1_vals + Pe_mon_ampa2_vals Pe_mon_nmda_vals = Pe_mon_nmda1_vals + Pe_mon_nmda2_vals Pe_mon_gaba_vals = Pe.J_gaba[0] * np.mean(Pe_mon_gaba.values.T, axis=1) Pe_mon_input_vals = Pe.J_input[0] * np.mean(Pe_mon_input.values.T, axis=1) Pe_mon_V_vals = np.mean(Pe_mon_V.values.T, axis=1) Pe_mon_all_vals = Pe_mon_ampa_vals + Pe_mon_nmda_vals - Pe_mon_gaba_vals See1_mon_x_vals = np.mean(See1_mon_x.values.T, axis=1) See2_mon_x_vals = np.mean(See2_mon_x.values.T, axis=1) #============================================================================== # Save into a Matlab file #============================================================================== if osc: Pe_output = Pe.J_ampa1[0]*Pe_mon_ampa1.values+Pe.J_nmda1[0]*Pe_mon_nmda1.values + \ Pe.J_ampa2[0]*Pe_mon_ampa2.values+Pe.J_nmda2[0]*Pe_mon_nmda2.values-Pe.J_gaba[0]*Pe_mon_gaba.values Pe_output = Pe_output[:, fft_start:, ] Pe_V = Pe_mon_V.values[:, fft_start:, ] Pe_glut = Pe.J_ampa1[0]*Pe_mon_ampa1.values+Pe.J_nmda1[0]*Pe_mon_nmda1.values + \ Pe.J_ampa2[0]*Pe_mon_ampa2.values+Pe.J_nmda2[0]*Pe_mon_nmda2.values Pe_glut = Pe_glut[:, fft_start:, ] Pe_gaba = Pe.J_gaba[0] * Pe_mon_gaba.values Pe_gaba = Pe_gaba[:, fft_start:, ] Pe_input = Pe_mon_eta[:, fft_start:, ] T_step = bn.defaultclock.dt holder = { 'Pe_output': Pe_output, 'Pe_input': Pe_input, 'Pe_V': Pe_V, 'Pe_glut': Pe_glut, 'Pe_gaba': Pe_gaba, 'T_step': T_step } scipy.io.savemat(fft_file + 'delta_u' + str(delta_u) + '_' + str(rep), mdict=holder) else: holder = { 'Pe_rate': Pe_ratemon.rate, 'Pe_time': Pe_ratemon.times, 'uee1': uee1, 'uee2': uee2, 'uie1': uie1, 'uie2': uie2 } scipy.io.savemat(rate_file + 'delta_q_' + str(delta_u) + '_' + str(run_num) + 'rep' + str(rep), mdict=holder) bn.clear(erase=True, all=True)
def fft_nostd(qee, run_num, new_connectivity, osc, rep): #bn.seed(int(time.time())) # bn.seed(1412958308+2) bn.reinit_default_clock() bn.defaultclock.dt = 0.5 * bn.ms #============================================================================== # Define constants for the model. #============================================================================== fft_file = './nostd_fft_p20_' rate_file = './nostd_rate_p20_' if osc: T = 8.0 * bn.second else: T = 3.5 * bn.second n_tsteps = T / bn.defaultclock.dt fft_start = 3.0 * bn.second / bn.defaultclock.dt # Time window for the FFT computation #run_num = 10 ro = 1.2 * bn.Hz #============================================================================== # Need to do all others besides 0.2 and 0.5 #============================================================================== print qee print run_num print new_connectivity print rep qie = 0.3 # Fraction of NMDA receptors for e to i connections k = 0.65 Jeo_const = 1.0 #*bn.mV # Base strength of o (external) to e connections Ne = 3200 # number of excitatory neurons Ni = 800 # number of inhibitory neurons No = 2000 # number of external neurons N = Ne + Ni pcon = 0.2 # probability of connection Jee = 5.0 / (Ne * pcon) Jie = 5.0 / (Ne * pcon) Jii = k * 5.0 / (Ni * pcon) Jei = k * 5.0 / (Ni * pcon) Jeo = 1.0 El = -60.0 * bn.mV # leak reversal potential Vreset = -52.0 * bn.mV # reversal potential Vthresh = -40.0 * bn.mV # spiking threshold tref = 2.0 * bn.ms # refractory period te = 20.0 * bn.ms # membrane time constant of excitatory neurons ti = 10.0 * bn.ms # membrane time constant of inhibitory neruons tee_ampa = 10.0 * bn.ms # time const of ampa currents at excitatory neurons tee_nmda = 100.0 * bn.ms # time const of nmda currents at excitatory neurons tie_ampa = 10.0 * bn.ms # time const of ampa currents at inhibitory neurons tie_nmda = 100.0 * bn.ms # time const of nmda currents at inhibitory neurons tii_gaba = 10.0 * bn.ms # time const of GABA currents at inhibitory neurons tei_gaba = 10.0 * bn.ms # time const of GABA currents at excitatory neurons teo_input = 100.0 * bn.ms #============================================================================== # Define model structure #============================================================================== model = ''' dV/dt = (-(V-El)+J_ampa*I_ampa+J_nmda*I_nmda-J_gaba*I_gaba+J_input*I_input+eta+eta_corr)/tm : bn.volt dI_ampa/dt = -I_ampa/t_ampa : bn.volt dI_nmda/dt = -I_nmda/t_nmda : bn.volt dI_gaba/dt = -I_gaba/t_gaba : bn.volt dI_input/dt = (-I_input+mu)/t_input : bn.volt J_ampa : 1 J_nmda : 1 J_gaba : 1 J_input : 1 mu : bn.volt eta : bn.volt eta_corr : bn.volt tm : bn.second t_ampa : bn.second t_nmda : bn.second t_gaba : bn.second t_input : bn.second ''' P_reset = "V=-52*bn.mV" Se_model = ''' we_ampa : bn.volt we_nmda : bn.volt ''' Se_pre = ('I_ampa += we_ampa', 'I_nmda += we_nmda') Si_model = ''' wi_gaba : bn.volt ''' Si_pre = 'I_gaba += wi_gaba' So_model = ''' wo_input : bn.volt ''' So_pre = 'I_input += wo_input' #============================================================================== # Define populations #============================================================================== P = bn.NeuronGroup(N, model, threshold=Vthresh, reset=P_reset, refractory=tref) Pe = P[0:Ne] Pe.tm = te Pe.t_ampa = tee_ampa Pe.t_nmda = tee_nmda Pe.t_gaba = tei_gaba Pe.t_input = teo_input Pe.I_ampa = 0 * bn.mV Pe.I_nmda = 0 * bn.mV Pe.I_gaba = 0 * bn.mV Pe.I_input = 0 * bn.mV Pe.V = (np.random.rand(Pe.V.size) * 12 - 52) * bn.mV Pi = P[Ne:(Ne + Ni)] Pi.tm = ti Pi.t_ampa = tie_ampa Pi.t_nmda = tie_nmda Pi.t_gaba = tii_gaba Pi.t_input = teo_input Pi.I_ampa = 0 * bn.mV Pi.I_nmda = 0 * bn.mV Pi.I_gaba = 0 * bn.mV Pi.I_input = 0 * bn.mV Pi.V = (np.random.rand(Pi.V.size) * 12 - 52) * bn.mV Pe.J_ampa = Jee * (1 - qee) #*SEE1 Pe.J_nmda = Jee * qee #*SEE1 Pi.J_ampa = Jie * (1 - qie) #*SEE1 Pi.J_nmda = Jie * qie #*SEE1 Pe.J_gaba = Jei Pi.J_gaba = Jii Pe.J_input = Jeo Pi.J_input = Jeo #============================================================================== # Define inputs #============================================================================== if osc: Pe.mu = 2.0 * bn.mV holder = np.zeros((n_tsteps, )) t_freq = np.linspace(0, 10, n_tsteps) fo = 0.2 # Smallest frequency in the signal fe = 10.0 # Largest frequency in the signal F = int(fe / 0.2) for m in range(1, F + 1): holder = holder + np.cos(2 * np.pi * m * fo * t_freq - m * (m - 1) * np.pi / F) holder = holder / np.max(holder) Pe.eta = bn.TimedArray(0.0 * bn.mV * holder) #, dt=0.5*bn.ms) Pe.eta_corr = 0 * bn.mV Background_eo = bn.PoissonInput(Pe, N=1000, rate=1.0 * bn.Hz, weight=0.2 * bn.mV, state='I_input') Background_io = bn.PoissonInput(Pi, N=1000, rate=1.05 * bn.Hz, weight=0.2 * bn.mV, state='I_input') Pi.mu = 0 * bn.mV Pi.eta = 0 * bn.mV #, dt=0.5*bn.ms) Pi.eta_corr = 0 * bn.mV Po = bn.PoissonGroup(No, rates=0 * bn.Hz) else: Background_eo = bn.PoissonInput(Pe, N=1000, rate=1.0 * bn.Hz, weight=0.2 * bn.mV, state='I_input') Background_io = bn.PoissonInput(Pi, N=1000, rate=1.05 * bn.Hz, weight=0.2 * bn.mV, state='I_input') holder_pe = np.zeros((n_tsteps, )) time_steps = np.linspace(0, T / bn.second, n_tsteps) holder_pe[time_steps < 0.5] = 0.0 * bn.mV holder_pe[time_steps >= 0.5] = 3.0 * bn.mV # 35.0/Jeo *bn.mV #25 Pe.mu = bn.TimedArray(holder_pe) def firing_function(t, ro): if t > 0.5 * bn.second and t < 3.5 * bn.second: return 0.0 * bn.Hz else: return 0.0 * bn.Hz # Pe.mu = 0*bn.mV Pe.eta = 0 * bn.mV #, dt=0.5*bn.ms) Pi.mu = 0.0 * bn.mV Pi.eta = 0 * bn.mV #, dt=0.5*bn.ms) Po = bn.PoissonGroup(No, rates=lambda t: firing_function(t, ro)) #============================================================================== # Define synapses #============================================================================== See = bn.Synapses(Pe, Pe, model=Se_model, pre=Se_pre) Sie = bn.Synapses(Pe, Pi, model=Se_model, pre=Se_pre) Sei = bn.Synapses(Pi, Pe, model=Si_model, pre=Si_pre) Sii = bn.Synapses(Pi, Pi, model=Si_model, pre=Si_pre) Seo = bn.Synapses(Po, Pe, model=So_model, pre=So_pre) #============================================================================== # Define monitors #============================================================================== Pe_mon_V = bn.StateMonitor(Pe, 'V', timestep=1, record=True) Pe_mon_eta = bn.StateMonitor(Pe, 'eta', timestep=1, record=True) Pe_mon_ampa = bn.StateMonitor(Pe, 'I_ampa', timestep=1, record=True) Pe_mon_nmda = bn.StateMonitor(Pe, 'I_nmda', timestep=1, record=True) Pe_mon_gaba = bn.StateMonitor(Pe, 'I_gaba', timestep=1, record=True) Pe_ratemon = bn.PopulationRateMonitor(Pe, bin=10.0 * bn.ms) #============================================================================== # Define random connections #============================================================================== if new_connectivity: See.connect_random(Pe, Pe, sparseness=pcon) Sie.connect_random(Pe, Pi, sparseness=pcon) Sii.connect_random(Pi, Pi, sparseness=pcon) Sei.connect_random(Pi, Pe, sparseness=pcon) Seo.connect_random(Po, Pe, sparseness=pcon) print 'Saving' See.save_connectivity('./See_connections_nostd_saver_p20' + str(run_num)) Sie.save_connectivity('./Sie_connections_nostd_saver_p20' + str(run_num)) Sii.save_connectivity('./Sii_connections_nostd_saver_p20' + str(run_num)) Sei.save_connectivity('./Sei_connections_nostd_saver_p20' + str(run_num)) Seo.save_connectivity('./Seo_connections_nostd_saver_p20' + str(run_num)) else: print 'Loading' See.load_connectivity('./See_connections_nostd_saver_p20' + str(run_num)) Sie.load_connectivity('./Sie_connections_nostd_saver_p20' + str(run_num)) Sii.load_connectivity('./Sii_connections_nostd_saver_p20' + str(run_num)) Sei.load_connectivity('./Sei_connections_nostd_saver_p20' + str(run_num)) Seo.load_connectivity('./Seo_connections_nostd_saver_p20' + str(run_num)) See.we_ampa = 1.0 * bn.mV / tee_ampa See.we_nmda = 1.0 * bn.mV / tee_nmda Sie.we_ampa = 1.0 * bn.mV / tie_ampa Sie.we_nmda = 1.0 * bn.mV / tie_nmda Sei.wi_gaba = 1.0 * bn.mV / tei_gaba Sii.wi_gaba = 1.0 * bn.mV / tii_gaba Seo.wo_input = 1.0 * bn.mV / teo_input #============================================================================== # Run model #============================================================================== timer = 0 * bn.second t_start = time.time() bn.run(T, report='graphical') timer = timer + T print '-------------------------------------------------------' print 'Time is ' + str(timer) + ' seconds' t_end = time.time() print 'Time to compute last ' +str(T)+' seconds is: ' + \ str(t_end - t_start) + ' seconds' print '-------------------------------------------------------\n' #============================================================================== # Save into a Matlab file #============================================================================== if osc: Pe_output = Pe.J_ampa[0] * Pe_mon_ampa.values + Pe.J_nmda[ 0] * Pe_mon_nmda.values - Pe.J_gaba[0] * Pe_mon_gaba.values Pe_output = Pe_output[:, fft_start:, ] Pe_glut = Pe.J_ampa[0] * Pe_mon_ampa.values + Pe.J_nmda[ 0] * Pe_mon_nmda.values Pe_glut = Pe_glut[:, fft_start:, ] Pe_gaba = Pe.J_gaba[0] * Pe_mon_gaba.values[:, fft_start:, ] Pe_V = Pe_mon_V.values[:, fft_start:, ] Pe_input = Pe_mon_eta[:, fft_start:, ] T_step = bn.defaultclock.dt holder = { 'Pe_output': Pe_output, 'Pe_input': Pe_input, 'Pe_V': Pe_V, 'Pe_glut': Pe_glut, 'Pe_gaba': Pe_gaba, 'T_step': T_step } scipy.io.savemat(fft_file + 'qee' + str(qee) + '_' + str(rep), mdict=holder) else: holder = {'Pe_rate': Pe_ratemon.rate, 'Pe_time': Pe_ratemon.times} scipy.io.savemat(rate_file + 'qee_' + str(qee) + '_' + str(run_num) + 'rep' + str(rep), mdict=holder)
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