def live_spike_receive_translated(self): self.stored_data = list() db_conn = DatabaseConnection(local_port=None) db_conn.add_database_callback(self.database_callback) p.setup(1.0) p.set_number_of_neurons_per_core(p.SpikeSourceArray, 5) pop = p.Population( 25, p.SpikeSourceArray([[1000 + (i * 10)] for i in range(25)])) p.external_devices.activate_live_output_for( pop, translate_keys=True, database_notify_port_num=db_conn.local_port, tag=1, use_prefix=True, key_prefix=self.PREFIX, prefix_type=EIEIOPrefix.UPPER_HALF_WORD) p.run(1500) p.end() self.listener.close() self.conn.close() self.assertGreater(len(self.stored_data), 0) for key, time in self.stored_data: self.assertEqual(key >> 16, self.PREFIX) self.assertEqual(1000 + ((key & 0xFFFF) * 10), time)
def setupLayer_PN(time_space): ''' PN ─┬─── pn_neuron_01 ├─── pn_neuron_02 ├─── pn_neuron_03 ├─── ... └─── pn_neuron_100 PN was used as input layer ''' input_population = spynnaker.Population(NUM_PN_CELLS, spynnaker.SpikeSourceArray(spike_times=time_space), label='PN_population') return input_population
def setupLayer_PN(time_space): ''' PN ─┬─── pn_neuron_01 ├─── pn_neuron_02 ├─── pn_neuron_03 ├─── ... └─── pn_neuron_784 ''' NUM_PN_CELLS = 784 ''' 784只PN神经元放在 ''' input_population = spynnaker.Population( NUM_PN_CELLS, spynnaker.SpikeSourceArray(spike_times=time_space), label='PN_population') return input_population
# Post after pre else: post_phase = 1 - t pre_phase = 0 sim_time = max(sim_time, (num_pairs * time_between_pairs) + abs(t)) # Neuron populations pre_pop = sim.Population(1, model(**cell_params)) post_pop = sim.Population(1, model, cell_params) # Stimulating populations pre_times = [i for i in range(pre_phase, sim_time, time_between_pairs)] post_times = [i for i in range(post_phase, sim_time, time_between_pairs)] pre_stim = sim.Population( 1, sim.SpikeSourceArray(spike_times=[pre_times])) post_stim = sim.Population( 1, sim.SpikeSourceArray(spike_times=[post_times])) weight = 0.035 # Connections between spike sources and neuron populations ee_connector = sim.OneToOneConnector() sim.Projection( pre_stim, pre_pop, ee_connector, receptor_type='excitatory', synapse_type=sim.StaticSynapse(weight=weight)) sim.Projection( post_stim, post_pop, ee_connector, receptor_type='excitatory', synapse_type=sim.StaticSynapse(weight=weight)) # Plastic Connection between pre_pop and post_pop
pre_phase = t # Post after pre else: post_phase = 1 - t pre_phase = 0 sim_time = max(sim_time, (num_pairs * time_between_pairs) + abs(t)) # Neuron populations pre_pop = sim.Population(1, model(**cell_params)) post_pop = sim.Population(1, model, cell_params) # Stimulating populations pre_times = [i for i in range(pre_phase, sim_time, time_between_pairs)] post_times = [i for i in range(post_phase, sim_time, time_between_pairs)] pre_stim = sim.Population(1, sim.SpikeSourceArray(spike_times=[pre_times])) post_stim = sim.Population(1, sim.SpikeSourceArray(spike_times=[post_times])) weight = 2 # Connections between spike sources and neuron populations ee_connector = sim.OneToOneConnector() sim.Projection(pre_stim, pre_pop, ee_connector, receptor_type='excitatory', synapse_type=sim.StaticSynapse(weight=weight)) sim.Projection(post_stim, post_pop, ee_connector,
'i_offset': 0, 'tau_m': 20, 'tau_refrac': 2, 'tau_syn_E': 50, 'tau_syn_I': 5, 'v_reset': -70, 'v_rest': -65, 'v_thresh': -55 } sim.set_number_of_neurons_per_core(sim.IF_curr_exp, 100) G1_1 = sim.Population(1, sim.IF_curr_exp(**cell_params_lif), label="G1_1") G2_2 = sim.Population(10, sim.IF_curr_exp(**cell_params_lif), label="G2_2") GEN1_3 = sim.Population( 1, sim.SpikeSourceArray(spike_times=[1, 2, 4, 6, 7], label="GEN1_3")) G4_4 = sim.Population(1, sim.IF_curr_exp(**cell_params_lif), label="G4_4") Dani_5 = sim.Population(1, sim.IF_curr_exp(**cell_params_lif), label="Dani_5") G5_6 = sim.Population(1, sim.IF_curr_exp(**cell_params_lif), label="G5_6") G6_7 = sim.Population(1, sim.IF_curr_exp(**cell_params_lif), label="G6_7") input_GEN1_3G1_1 = sim.Projection(GEN1_3, G1_1, sim.OneToOneConnector(), synapse_type=sim.StaticSynapse(weight=1.25, delay=0)) input_G2_2G1_1 = sim.Projection(G2_2, G1_1, sim.OneToOneConnector(), synapse_type=sim.StaticSynapse(weight=0.8, delay=0))
if i == 0: projections.append( sim.Projection(input_layer, layers[i + 1], sim.FromFileConnector(filepath))) #projections.append(sim.Projection(input_layer, layers[i+1], sim.OneToOneConnector(), synapse_type=sim.StaticSynapse(weight=5, delay=1))) else: projections.append( sim.Projection(layers[i], layers[i + 1], sim.FromFileConnector(filepath))) return layers, projections sim.setup(timestep=1.0) spike_times = [[i] for i in range(1296)] input_pop = sim.Population(1296, sim.SpikeSourceArray(spike_times=spike_times), label="InputLayer") #sim.set_number_of_neurons_per_core(sim.IF_curr_exp, 100) layers, projections = load(path=PATH, filename=MODEL, input_layer=input_pop) layers[1].record(["spikes", "v"]) simtime = 20 sim.run(simtime) neo = layers[1].get_data(variables=["spikes", "v"]) spikes = neo.segments[0].spiketrains print(spikes) v = neo.segments[0].filter(name='v')[0] print(v)
'i_offset': 0, 'tau_m': 20, 'tau_refrac': 2, 'tau_syn_E': 50, 'tau_syn_I': 5, 'v_reset': -70, 'v_rest': -65, 'v_thresh': -55 } sim.set_number_of_neurons_per_core(sim.IF_curr_exp, 100) G1_1 = sim.Population(1, sim.IF_curr_exp(**cell_params_lif), label="G1_1") G2_2 = sim.Population(10, sim.IF_curr_exp(**cell_params_lif), label="G2_2") GEN1_3 = sim.Population( 1, sim.SpikeSourceArray(spike_times=[0, 8], label="GEN1_3")) G4_4 = sim.Population(1, sim.IF_curr_exp(**cell_params_lif), label="G4_4") Dani_5 = sim.Population(1, sim.IF_curr_exp(**cell_params_lif), label="Dani_5") G5_6 = sim.Population(1, sim.IF_curr_exp(**cell_params_lif), label="G5_6") G6_7 = sim.Population(1, sim.IF_curr_exp(**cell_params_lif), label="G6_7") input_GEN1_3G1_1 = sim.Projection(GEN1_3, G1_1, sim.OneToOneConnector(), synapse_type=sim.StaticSynapse(weight=1.25, delay=1)) input_G2_2G1_1 = sim.Projection(G2_2, G1_1, sim.OneToOneConnector(), synapse_type=sim.StaticSynapse(weight=0.8, delay=1))
spike_times, simtime = misc.extract_spiketimes_from_aedat( filepath, no_gaps=True, start_time=0, simtime=SIMTIME, eventframe_width=None) sim.setup(timestep=1.0) #first = [i for i in range(1000)] #spike_times = [[i] for i in range(NR)] #spike_times[0] = first input_pop = sim.Population( size=NR, cellclass=sim.SpikeSourceArray(spike_times=spike_times), label="spikes") pop_0 = sim.Population(size=NR, cellclass=sim.IF_curr_exp(), label="1_pre_input") pop_0.set(v_thresh=0.1) pop_1 = sim.Population(size=16, cellclass=sim.IF_curr_exp(), label="1_input") pop_1.set(v_thresh=0.05) #misc.set_cell_params(pop_1, cellparams) pop_2 = sim.Population(size=4, cellclass=sim.IF_curr_exp(), label="2_hidden") pop_2.set(v_thresh=0.1) #misc.set_cell_params(pop_2, cellparams) #pop_2.set(i_offset=s[label]['v_thresh']) # this projection / pop_0 is only used to monitor the input spikes (actually not needed for MLP network) input_proj = sim.Projection(input_pop,
label="All Cells") exc_cells = all_cells[:n_exc] exc_cells.label = "Excitatory cells" inh_cells = all_cells[n_exc:] inh_cells.label = "Inhibitory cells" exc_cells_input_subset = exc_cells[:n_exc / 4] exc_cells_input_subset.label = "Excitatory cells Input subset" inh_cells_input_subset = inh_cells[:n_inh] inh_cells_input_subset.label = "Inhibitory cells Input subset" ext_stim_exc = sim.Population( n_stim, sim.SpikeSourceArray(spike_times=spike_times_train_up[i].tolist()), label="Input spike time array exitatory") ext_stim_inh = sim.Population( n_stim, sim.SpikeSourceArray(spike_times=spike_times_train_dn[i].tolist()), label="Input spike time array inhibitory") # stdp_mechanism = sim.STDPMechanism( # timing_dependence=sim.SpikePairRule(**synaptic_parameters['excitatory_stdp']['timing_dependence']), # weight_dependence=sim.AdditiveWeightDependence(**synaptic_parameters['excitatory_stdp']['weight_dependence']), # weight=synaptic_parameters['excitatory_stdp']['weight'] # ,delay=synaptic_parameters['excitatory_stdp']['delay'], # dendritic_delay_fraction=0 # ) # Determine the connectivity among the population of neuron
def lancement_sim(cellSourceSpikes, path, weight_input=0, weight_inter=0, max_time=800000, TIME_STEP=TIME_STEP, input_n=input_n, nb_neuron_int=nb_neuron_int, nb_neuron_out=nb_neuron_out, delay=delay, p_conn_in_int=p_conn_in_int, p_conn_int_out=p_conn_int_out, v_tresh=v_tresh): simulator = 'spinnaker' # le max_delay doit être inférieur à 14*time_step sim.setup(timestep=TIME_STEP, min_delay=delay, max_delay=delay * 2) randoms = np.random.rand(100, 1) #defining network topology lif_curr_exp_params = { 'cm': 1.0, # The capacitance of the LIF neuron in nano-Farads 'tau_m': 20.0, # The time-constant of the RC circuit, in milliseconds 'tau_refrac': 5.0, # The refractory period, in milliseconds 'v_reset': -65.0, # The voltage to set the neuron at immediately after a spike 'v_rest': -65.0, # The ambient rest voltage of the neuron 'v_thresh': -(v_tresh), # The threshold voltage at which the neuron will spike 'tau_syn_E': 5.0, # The excitatory input current decay time-constant 'tau_syn_I': 5.0, # The inhibitory input current decay time-constant 'i_offset': 0.0, # A base input current to add each timestep } # Population d'entrée avec comme source le SpikeSourceArray en paramètre Input = sim.Population(input_n, sim.SpikeSourceArray(spike_times=cellSourceSpikes), label="Input") Input.record("spikes") # Définition des types de neurones et des couches intermédiaire, de sortie, ainsi que celle contenant le neurone de l'attention LIF_Intermediate = sim.IF_curr_exp(**lif_curr_exp_params) Intermediate = sim.Population(nb_neuron_int, LIF_Intermediate, label="Intermediate") Intermediate.record(("spikes", "v")) LIF_Output = sim.IF_curr_exp(**lif_curr_exp_params) Output = sim.Population(nb_neuron_out, LIF_Output, label="Output") Output.record(("spikes", "v")) LIF_delayer = sim.IF_curr_exp(**lif_curr_exp_params) Delay_n = sim.Population(1, LIF_delayer, label="Delay") Delay_n.record(("spikes", "v")) # set the stdp mechanisim parameters, we are going to use stdp in both connections between (input-intermediate) adn (intermediate-output) python_rng = NumpyRNG(seed=98497627) delay = delay # (ms) synaptic time delay #A_minus # définition des connexions entre couches de neurones entrée <=> intermédiaire, intermédiaire <=> sortie # vérificatio pour savoir si on est dans le cas de la première simulation par défault ou si on doit injecter les poids if ((weight_input != 0) or (weight_inter != 0)): # cas ou l'on inject les poids Conn_input_inter = sim.Projection( Input, Intermediate, # Le fromListConnector pour injecter les poids connector=sim.FromListConnector(weight_input), receptor_type="excitatory", label="Connection input to intermediate", # des synapses static pour "suprimer" l'apprentissage synapse_type=sim.StaticSynapse()) Conn_inter_output = sim.Projection( Intermediate, Output, # pre and post population connector=sim.FromListConnector(weight_inter), receptor_type="excitatory", label="Connection intermediate to output", synapse_type=sim.StaticSynapse()) else: # cas par défault Conn_input_inter = sim.Projection( Input, Intermediate, connector=sim.FixedProbabilityConnector( p_conn_in_int, allow_self_connections=False), synapse_type=sim.StaticSynapse( weight=RandomDistribution('normal', (3, 2.9), rng=python_rng)), receptor_type="excitatory", label="Connection input to intermediate") Conn_inter_output = sim.Projection( Intermediate, Output, # pre and post population connector=sim.FixedProbabilityConnector( p_conn_int_out, allow_self_connections=False), synapse_type=sim.StaticSynapse( weight=RandomDistribution('normal', (3, 2.9), rng=python_rng)), receptor_type="excitatory", label="Connection intermediate to output") # définition des connexions inhibitrices des couches intermédiaire et de sortie FixedInhibitory_WTA = sim.StaticSynapse(weight=6) WTA_INT = sim.Projection( Intermediate, Intermediate, connector=sim.AllToAllConnector(allow_self_connections=False), synapse_type=FixedInhibitory_WTA, receptor_type="inhibitory", label="Connection WTA") WTA_OUT = sim.Projection( Output, Output, connector=sim.AllToAllConnector(allow_self_connections=False), synapse_type=FixedInhibitory_WTA, receptor_type="inhibitory", label="Connection WTA") # Connexion avec le neurone de l'attention FixedInhibitory_delayer = sim.StaticSynapse(weight=2) Delay_out = sim.Projection( Delay_n, Output, connector=sim.AllToAllConnector(allow_self_connections=False), synapse_type=FixedInhibitory_delayer, receptor_type="inhibitory", label="Connection WTA") Delay_inter = sim.Projection( Intermediate, Delay_n, connector=sim.AllToAllConnector(allow_self_connections=False), synapse_type=FixedInhibitory_delayer, receptor_type="inhibitory", label="Connection WTA") # On précise le nombre de neurone par coeurs au cas ou sim.set_number_of_neurons_per_core(sim.IF_curr_exp, 255) # on arrondie le temps de simulation, sinon avec les callbacks, on a une boucle infinie pour des temps d'arrêts plus précis que la fréquence des callbacks simtime = ceil(max_time) try: #lancement de la simulation sim.run(simtime) #récupération des infos sur les spike des trois couches neo = Output.get_data(variables=["spikes", "v"]) spikes = neo.segments[0].spiketrains #print(spikes) v = neo.segments[0].filter(name='v')[0] neo_in = Input.get_data(variables=["spikes"]) spikes_in = neo_in.segments[0].spiketrains #print(spikes_in) neo_intermediate = Intermediate.get_data(variables=["spikes", "v"]) spikes_intermediate = neo_intermediate.segments[0].spiketrains #print(spikes_intermediate) v_intermediate = neo_intermediate.segments[0].filter(name='v')[0] #print(v_intermediate) sim.reset() sim.end() except: # Si la simulation fail, on set ces deux variables à zéros pour gérer l'erreur dans le script principal v = 0 spikes = 0 # Création et sauvegarde des graphs des graphs si la simluation s'est bien passée, + envoie des sorties de la fonction if (isinstance(spikes, list) and isinstance(v, AnalogSignal)): plot.Figure( # plot voltage for first ([0]) neuron plot.Panel(v, ylabel="Membrane potential (mV)", data_labels=[Output.label], yticks=True, xlim=(0, simtime)), # plot spikes (or in this case spike) plot.Panel(spikes, yticks=True, markersize=5, xlim=(0, simtime)), title="Spiking activity of the output layer during test", annotations="Simulated with {}".format(sim.name())).save( "./Generated_data/tests/" + path + "/output_layer_membrane_voltage_and_spikes.png") plot.Figure( # plot voltage for first ([0]) neuron plot.Panel(v_intermediate, ylabel="Membrane potential (mV)", data_labels=[Output.label], yticks=True, xlim=(0, simtime)), # plot spikes (or in this case spike) plot.Panel(spikes_intermediate, yticks=True, markersize=5, xlim=(0, simtime)), title="Spiking activity of the intermediate layer during test", annotations="Simulated with {}".format(sim.name())).save( "./Generated_data/tests/" + path + "/intermediate_layer_membrane_voltage_and_spikes.png") return v, spikes else: print( "simulation failed with parameters : (l'affichage des paramètres ayant causés le disfonctionnement de la simulation sera traitée à une date ultérieur, merci!)" ) return 0, 0
#pre_spikes.append(i*v+2) runTime = (i + 1) * v cell_params_lif = { 'cm': 1, #70 'i_offset': 0.0, 'tau_m': 20.0, #20 'tau_refrac': 10.0, #2 more that t inhibit#10 'tau_syn_E': 2.0, #2 'tau_syn_I': 10.0, #5 'v_reset': -70.0, 'v_rest': -65.0, 'v_thresh': -55.0 } pre_cell = sim.SpikeSourceArray(pre_spikes) post_cell = sim.SpikeSourceArray(post_spikes) layer1 = sim.Population(1, pre_cell, label='inputspikes') post = sim.Population(1, sim.IF_curr_exp, cellparams=cell_params_lif, label='post') layer2 = sim.Population(1, post_cell, label='outputspikes') stim_proj = sim.Projection(layer2, post, sim.OneToOneConnector(), synapse_type=sim.StaticSynapse(weight=7, delay=0.25)) stdp = sim.STDPMechanism(
str(prediction * (100 - current_poucentage))) current_poucentage += 1 if (df['x'].iloc[i] % 4 == 0 & df['y'].iloc[i] % 4 == 0): x = df['x'].iloc[i] y = df['y'].iloc[i] time = df['ts'].iloc[i] * 1.e-3 if (timemax < time): timemax = time index = y * x + x - 2 sourceArray[index] = sourceArray[index] + [time] # Pour la desambiguisation sur SpiNNaker sourceArray = [list(elem) for elem in sourceArray] # lancement d'un simulation ultra simple pour vérifier l'acceptation du spikeSourceArray import pyNN.spiNNaker as sim simulator = 'spinnaker' sim.setup(timestep=10, min_delay=20, max_delay=30) sources = sim.SpikeSourceArray(spike_times=sourceArray) spikeSource = sim.Population(1024, sources) spikeSource.record(['spikes']) sim.run(simtime=10000) spikeSources = spikeSource.get_data() #.segments[0].spiketrains S_spikes = spikeSources.segments[0].spiketrains sim.end()
def lancement_sim(cellSourceSpikes, max_time=800000, path="default", TIME_STEP=TIME_STEP, input_n=input_n, nb_neuron_int=nb_neuron_int, nb_neuron_out=nb_neuron_out, delay=delay, p_conn_in_int=p_conn_in_int, p_conn_int_out=p_conn_int_out, a_minus=0.6, a_plus=0.6, tau_minus=12.0, tau_plus=10.0, v_tresh=10.0): simulator = 'spinnaker' sim.setup(timestep=TIME_STEP, min_delay=delay, max_delay=delay * 2) randoms = np.random.rand(100, 1) lif_curr_exp_params = { 'cm': 1.0, # The capacitance of the LIF neuron in nano-Farads 'tau_m': 20.0, # The time-constant of the RC circuit, in milliseconds 'tau_refrac': 5.0, # The refractory period, in milliseconds 'v_reset': -65.0, # The voltage to set the neuron at immediately after a spike 'v_rest': -65.0, # The ambient rest voltage of the neuron 'v_thresh': -(v_tresh), # The threshold voltage at which the neuron will spike 'tau_syn_E': 5.0, # The excitatory input current decay time-constant 'tau_syn_I': 5.0, # The inhibitory input current decay time-constant 'i_offset': 0.0, # A base input current to add each timestep } Input = sim.Population(input_n, sim.SpikeSourceArray(spike_times=cellSourceSpikes), label="Input") Input.record("spikes") LIF_Intermediate = sim.IF_curr_exp(**lif_curr_exp_params) Intermediate = sim.Population(nb_neuron_int, LIF_Intermediate, label="Intermediate") Intermediate.record(("spikes", "v")) LIF_Output = sim.IF_curr_exp(**lif_curr_exp_params) Output = sim.Population(nb_neuron_out, LIF_Output, label="Output") Output.record(("spikes", "v")) LIF_delayer = sim.IF_curr_exp(**lif_curr_exp_params) Delay_n = sim.Population(1, LIF_delayer, label="Delay") Delay_n.record(("spikes", "v")) python_rng = NumpyRNG(seed=98497627) delay = delay # (ms) synaptic time delay # Définition du fonctionnement de la stdp stdp_proj = sim.STDPMechanism( timing_dependence=sim.SpikePairRule(tau_plus=tau_plus, tau_minus=tau_minus, A_plus=a_plus, A_minus=a_minus), weight_dependence=sim.AdditiveWeightDependence(w_min=0.1, w_max=6), weight=RandomDistribution('normal', (3, 2.9), rng=python_rng), delay=delay) Conn_input_inter = sim.Projection( Input, Intermediate, connector=sim.FixedProbabilityConnector(p_conn_in_int, allow_self_connections=False), # synapse type set avec la définition de la stdp pour l'aprentsissage synapse_type=stdp_proj, receptor_type="excitatory", label="Connection input to intermediate") # second projection with stdp Conn_inter_output = sim.Projection( Intermediate, Output, # pre and post population connector=sim.FixedProbabilityConnector(p_conn_int_out, allow_self_connections=False), synapse_type=stdp_proj, receptor_type="excitatory", label="Connection intermediate to output") FixedInhibitory_WTA = sim.StaticSynapse(weight=6) WTA_INT = sim.Projection( Intermediate, Intermediate, connector=sim.AllToAllConnector(allow_self_connections=False), synapse_type=FixedInhibitory_WTA, receptor_type="inhibitory", label="Connection WTA") WTA_OUT = sim.Projection( Output, Output, connector=sim.AllToAllConnector(allow_self_connections=False), synapse_type=FixedInhibitory_WTA, receptor_type="inhibitory", label="Connection WTA") FixedInhibitory_delayer = sim.StaticSynapse(weight=2) Delay_out = sim.Projection( Delay_n, Output, connector=sim.AllToAllConnector(allow_self_connections=False), synapse_type=FixedInhibitory_delayer, receptor_type="inhibitory", label="Connection WTA") Delay_inter = sim.Projection( Intermediate, Delay_n, connector=sim.AllToAllConnector(allow_self_connections=False), synapse_type=FixedInhibitory_delayer, receptor_type="inhibitory", label="Connection WTA") sim.set_number_of_neurons_per_core(sim.IF_curr_exp, 255) # Définition des callbacks pour la récupération de l'écart-type sur les connexions entrée-intermédiaire, intermédiaire-sortie weight_recorder1 = WeightRecorder(sampling_interval=1000.0, projection=Conn_input_inter) weight_recorder2 = WeightRecorder(sampling_interval=1000.0, projection=Conn_inter_output) simtime = ceil(max_time) # Initialisation des tableaux pour la récupération des poids weights_int = [] weights_out = [] try: sim.run(simtime, callbacks=[weight_recorder1, weight_recorder2]) neo = Output.get_data(variables=["spikes", "v"]) spikes = neo.segments[0].spiketrains #print(spikes) v = neo.segments[0].filter(name='v')[0] weights_int = Conn_input_inter.get(["weight"], format="list") neo_in = Input.get_data(variables=["spikes"]) spikes_in = neo_in.segments[0].spiketrains #print(spikes_in) neo_intermediate = Intermediate.get_data(variables=["spikes", "v"]) spikes_intermediate = neo_intermediate.segments[0].spiketrains #print(spikes_intermediate) v_intermediate = neo_intermediate.segments[0].filter(name='v')[0] #print(v_intermediate) weights_out = Conn_inter_output.get(["weight"], format="list") sim.reset() sim.end() except: v = 0 spikes = 0 if (isinstance(spikes, list) and isinstance(v, AnalogSignal)): # Récupération des écart-types standard_deviation_out = weight_recorder2.get_standard_deviations() standard_deviation_int = weight_recorder1.get_standard_deviations() t = np.arange(0., max_time, 1.) # Création et sauvegarde des graphs sur les spikes et écart-types savePath = "./Generated_data/training/" + path + "/intermediate_layer_standard_deviation.png" plt.plot(standard_deviation_int) plt.xlabel("callbacks tick (1s)") plt.ylabel("standard deviation of the weights( wmax=6, wmin=0.1 )") plt.savefig(savePath) plt.clf() savePath = "./Generated_data/training/" + path + "/output_layer_standard_deviation.png" plt.plot(standard_deviation_out) plt.xlabel("callbacks tick") plt.ylabel("standard deviation ( wmax=6, wmin=0.1 )") plt.savefig(savePath) plt.clf() savePath = "./Generated_data/training/" + path + "/output_layer_membrane_voltage_and_spikes.png" plot.Figure( # plot voltage for first ([0]) neuron plot.Panel(v, ylabel="Membrane potential (mV)", data_labels=[Output.label], yticks=True, xlim=(0, simtime)), # plot spikes (or in this case spike) plot.Panel(spikes, yticks=True, markersize=5, xlim=(0, simtime)), title="Spiking activity of the output layer during training", annotations="Simulated with {}".format(sim.name())).save(savePath) savePath = "./Generated_data/training/" + path + "/intermediate_layer_membrane_voltage_and_spikes.png" plot.Figure( # plot voltage for first ([0]) neuron plot.Panel(v_intermediate, ylabel="Membrane potential (mV)", data_labels=[Output.label], yticks=True, xlim=(0, simtime)), # plot spikes (or in this case spike) plot.Panel(spikes_intermediate, yticks=True, markersize=5, xlim=(0, simtime)), title="Spiking activity of the intermediate layer during training", annotations="Simulated with {}".format(sim.name())).save(savePath) return v, spikes, weights_int, weights_out else: print( "simulation failed with parmaters parameters : (l'affichage des paramètres ayant causés le disfonctionnement de la simulation sera traitée à une date ultérieur, merci!)" ) return 0, 0, 0, 0
INHIBITORY = True # use the MLP model with inhibitory synapses (negaitve weights) output_spikes = [] if INHIBITORY: path = './model/dvs36_evtaccCOR_D16_B0_FLAT_30E/' else: path = './model/dvs36_evtacc_D16_B0_FLAT_posW_10E/' p1 = path + '01Dense_16' p2 = path + '02Dense_4' filepaths, labels = misc.get_sample_filepaths_and_labels('./data/aedat/balanced_100/') sim.setup(timestep=1.0) input_pop = sim.Population(size=1296, cellclass=sim.SpikeSourceArray(spike_times=[]), label="spikes") # to measure input spiketrains introduce an additional population if MEASUREMENTS: pop_0 = sim.Population(size=1296, cellclass=sim.IF_curr_exp(), label="1_pre_input") pop_0.set(v_thresh=0.1) input_proj = sim.Projection(input_pop, pop_0, sim.OneToOneConnector(), synapse_type=sim.StaticSynapse(weight=3, delay=1)) pop_1 = sim.Population(size=16, cellclass=sim.IF_curr_exp(), label="1_input") if INHIBITORY: pop_1.set(v_thresh=0.05) else: pop_1.set(v_thresh=0.1) pop_2 = sim.Population(size=4, cellclass=sim.IF_curr_exp(), label="2_hidden") pop_2.set(v_thresh=0.1) if INHIBITORY: