def testAllToAll(self): for srcP in [self.source5, self.source22, self.target33]: for tgtP in [self.target6, self.target33]: if srcP == tgtP: prj = sim.Projection( srcP, tgtP, sim.AllToAllConnector(allow_self_connections=False, weights=1.234)) else: prj = sim.Projection(srcP, tgtP, sim.AllToAllConnector(weights=1.234)) weights = prj._connections.W.toarray().flatten().tolist() self.assertEqual(weights, [1.234] * len(prj))
def testFixedProbability(self): """For all connections created with "fixedProbability"...""" for srcP in [self.source5, self.source22]: for tgtP in [self.target1, self.target6, self.target33]: prj1 = sim.Projection(srcP, tgtP, sim.FixedProbabilityConnector(0.5), rng=random.NumpyRNG(12345)) prj2 = sim.Projection(srcP, tgtP, sim.FixedProbabilityConnector(0.5), rng=random.NativeRNG(12345)) for prj in prj1, prj2: assert (0 < len(prj) < len(srcP) * len(tgtP) ), 'len(prj) = %d, len(srcP)*len(tgtP) = %d' % ( len(prj), len(srcP) * len(tgtP))
def setup_2_layers_4_units_ff_net(): configure_scheduling() pynnn.setup() Tns.p1 = pynnn.Population(4, pynnn.IF_curr_alpha, structure=pynnn.space.Grid2D()) Tns.p2 = pynnn.Population(4, pynnn.IF_curr_alpha, structure=pynnn.space.Grid2D()) Tns.prj1_2 = pynnn.Projection( Tns.p1, Tns.p2, pynnn.AllToAllConnector(allow_self_connections=False), target='excitatory') Tns.prj1_2.set("weight", 1) Tns.max_weight = 34 Tns.rore1_update_p = 10 Tns.rore1_win_width = 200 Tns.rore2_update_p = 10 Tns.rore2_win_width = 200 Tns.rore1 = RectilinearOutputRateEncoder(Tns.p1, 2, 2, Tns.rore1_update_p, Tns.rore1_win_width) Tns.rore2 = RectilinearOutputRateEncoder(Tns.p2, 2, 2, Tns.rore2_update_p, Tns.rore2_win_width) common.pynn_utils.POP_ADAPT_DICT[( Tns.p1, common.pynn_utils.RectilinearOutputRateEncoder)] = Tns.rore1 common.pynn_utils.POP_ADAPT_DICT[( Tns.p2, common.pynn_utils.RectilinearOutputRateEncoder)] = Tns.rore2 enable_recording(Tns.p1, Tns.p2) schedule_output_rate_calculation(Tns.p1) schedule_output_rate_calculation(Tns.p2)
def setup_and_fill_adapter(): setup_adapter() Tns.pop_size = 27 Tns.pynn_pop1 = pynnn.Population(Tns.pop_size, pynnn.IF_cond_alpha) Tns.ids1 = [int(u) for u in Tns.pynn_pop1.all()] Tns.pynn_pop2 = pynnn.Population(Tns.pop_size, pynnn.IF_cond_alpha, structure=pynnn.space.Grid3D()) Tns.ids2 = [int(u) for u in Tns.pynn_pop2.all()] A.add_pynn_population(Tns.pynn_pop1) Tns.pop2_alias = "testmap" A.add_pynn_population(Tns.pynn_pop2, alias=Tns.pop2_alias) Tns.pynn_proj1 = pynnn.Projection(Tns.pynn_pop1, Tns.pynn_pop2, pynnn.OneToOneConnector()) Tns.pynn_proj2 = pynnn.Projection(Tns.pynn_pop2, Tns.pynn_pop1, pynnn.AllToAllConnector()) A.add_pynn_projection(Tns.pynn_pop1, Tns.pynn_pop2, Tns.pynn_proj1) A.add_pynn_projection(Tns.pynn_pop2, Tns.pynn_pop1, Tns.pynn_proj2)
def setup_pynn_populations_with_1_to_1_connectivity(): pynnn.setup() Tns.p1 = pynnn.Population(64, pynnn.IF_curr_alpha, structure=pynnn.space.Grid2D()) Tns.p2 = pynnn.Population(64, pynnn.IF_curr_alpha, structure=pynnn.space.Grid2D()) Tns.prj1_2 = pynnn.Projection(Tns.p1, Tns.p2, pynnn.OneToOneConnector(), target='excitatory')
def setup_pynn_populations_with_full_connectivity(): pynnn.setup() Tns.p1 = pynnn.Population(4, pynnn.IF_curr_alpha, structure=pynnn.space.Grid2D()) Tns.p2 = pynnn.Population(4, pynnn.IF_curr_alpha, structure=pynnn.space.Grid2D()) Tns.prj1_2 = pynnn.Projection( Tns.p1, Tns.p2, pynnn.AllToAllConnector(allow_self_connections=False), target='excitatory')
def setup_pynn_populations(): pynnn.setup() Tns.p1 = pynnn.Population(64, pynnn.IF_curr_alpha, structure=pynnn.space.Grid2D()) Tns.p2 = pynnn.Population(64, pynnn.IF_curr_alpha, structure=pynnn.space.Grid2D()) Tns.prj1_2 = pynnn.Projection( Tns.p1, Tns.p2, pynnn.AllToAllConnector(allow_self_connections=False), target='excitatory') # Weights in nA as IF_curr_alpha uses current-based synapses Tns.prj1_2.set("weight", 1) Tns.max_weight = 33
def test_partitioning(self): p1 = sim.Population(5, sim.IF_cond_exp()) p2 = sim.Population(7, sim.IF_cond_exp()) a = p1 + p2[1:4] # [0 2 3 4 5][x 1 2 3 x x x] prj = sim.Projection(a, a, MockConnector(), synapse_type=self.syn) presynaptic_indices = numpy.array([0, 3, 4, 6, 7]) partitions = prj._partition(presynaptic_indices) self.assertEqual(len(partitions), 2) assert_array_equal(partitions[0], numpy.array([0, 3, 4])) assert_array_equal(partitions[1], numpy.array([2, 3])) # [0 1 2 3 4][x 1 2 3 x] self.assertEqual(prj._localize_index(0), (0, 0)) self.assertEqual(prj._localize_index(3), (0, 3)) self.assertEqual(prj._localize_index(5), (1, 1)) self.assertEqual(prj._localize_index(7), (1, 3))
def testDistanceDependentProbability(self): """For all connections created with "distanceDependentProbability"...""" # Test should be improved..." for rngclass in (random.NumpyRNG, random.NativeRNG): for expr in ('exp(-d)', 'd < 0.5'): #rngclass = random.NumpyRNG #expr = 'exp(-d)' prj = sim.Projection(self.source33, self.target33, sim.DistanceDependentProbabilityConnector( d_expression=expr), rng=rngclass(12345)) assert (0 < len(prj) <= len(self.source33) * len(self.target33)), len(prj) self.assertRaises(ZeroDivisionError, sim.DistanceDependentProbabilityConnector, d_expression="d/0.0")
def test_adapter_methods_call_check_open(): """methods in the methods_checking_open list have called check_open""" A.check_open = Mock(return_value=True) pynn_pop1 = pynnn.Population(1, pynnn.IF_cond_alpha) pynn_pop2 = pynnn.Population(1, pynnn.IF_cond_alpha) pynn_prj = pynnn.Projection( pynn_pop1, pynn_pop2, pynnn.OneToOneConnector(), target='excitatory') pynn_u = pynn_pop1[0] methods_checking_open = [ [A.assert_open, ()], [A.commit_structure, ()], [A.add_pynn_population, (pynn_pop1,)], [A.add_pynn_projection, (pynn_pop1, pynn_pop1, pynn_prj)]] for m in methods_checking_open: m[0](*m[1]) assert A.check_open.called, \ m[0].__name__ + " does not call check_open." A.check_open.reset_mock()
def test(spikeTimes, trained_weights, label): #spikeTimes = extractSpikes(sample) runTime = int(max(max(spikeTimes))) + 100 ########################################## sim.setup(timestep=1) pre_pop = sim.Population(input_size, sim.SpikeSourceArray, {'spike_times': spikeTimes}, label="pre_pop") post_pop = sim.Population(output_size, sim.IF_curr_exp, cell_params_lif, label="post_pop") ''' if len(untrained_weights)>input_size: training_weights = [[0 for j in range(output_size)] for i in range(input_size)] #np array? size 1024x25 k=0 for i in untrained_weights: training_weights[i[0]][i[1]]=i[2] ''' if len(trained_weights) > input_size: weigths = [[0 for j in range(output_size)] for i in range(input_size)] #np array? size 1024x25 k = 0 for i in range(input_size): for j in range(output_size): weigths[i][j] = trained_weights[k] k += 1 else: weigths = trained_weights connections = [] #k = 0 for n_pre in range(input_size): # len(untrained_weights) = input_size for n_post in range( output_size ): # len(untrained_weight[0]) = output_size; 0 or any n_pre #connections.append((n_pre, n_post, weigths[n_pre][n_post]*(wMax), __delay__)) connections.append((n_pre, n_post, weigths[n_pre][n_post] * (wMax) / max(trained_weights), __delay__)) # #k += 1 prepost_proj = sim.Projection( pre_pop, post_pop, sim.FromListConnector(connections), synapse_type=sim.StaticSynapse(), receptor_type='excitatory') # no more learning !! #inhib_proj = sim.Projection(post_pop, post_pop, sim.AllToAllConnector(), synapse_type=sim.StaticSynapse(weight=inhibWeight, delay=__delay__), receptor_type='inhibitory') # no more lateral inhib post_pop.record(['v', 'spikes']) sim.run(runTime) neo = post_pop.get_data(['v', 'spikes']) spikes = neo.segments[0].spiketrains v = neo.segments[0].filter(name='v')[0] f1 = pplt.Figure( # plot voltage pplt.Panel(v, ylabel="Membrane potential (mV)", xticks=True, yticks=True, xlim=(0, runTime + 100)), # raster plot pplt.Panel(spikes, xlabel="Time (ms)", xticks=True, yticks=True, markersize=2, xlim=(0, runTime + 100)), title='Test with label ' + str(label), annotations='Test with label ' + str(label)) f1.save('plot/' + str(trylabel) + str(label) + '_test.png') f1.fig.texts = [] print("Weights:{}".format(prepost_proj.get('weight', 'list'))) weight_list = [ prepost_proj.get('weight', 'list'), prepost_proj.get('weight', format='list', with_address=False) ] #predict_label= sim.end() return spikes
import pyNN.brian as sim import numpy as np training_data = np.loadtxt('training_data_0_1.txt', delimiter=',') training_label = training_data[:, -1] training_rate = training_data[:, 0:64] # print training_rate[1, :] inputpop = [] sim.setup() for i in range(np.size(training_rate, 1)): inputpop.append( sim.Population(1, sim.SpikeSourcePoisson(rate=abs(training_rate[0, i])))) # print inputpop[0].get('rate') # inputpop[0].set(rate = 8) # print inputpop[0].get('rate') pop = sim.Population(1, sim.IF_cond_exp(), label='exc') prj1 = sim.Projection(inputpop[0], pop, sim.OneToOneConnector(), synapse_type=sim.StaticSynapse(weight=0.04, delay=0.5), receptor_type='inhibitory') print prj1.get('weight', format='list')
# facilitating_synapse_ie = sim.TsodyksMarkramSynapse(weight = w_ie, delay = 0.5, U = 0.04, tau_rec = 100.0, tau_facil = 1000) # facilitating_synapse_ei = sim.TsodyksMarkramSynapse(weight = w_ei, delay = 0.5, U = 0.04, tau_rec = 100.0, tau_facil = 1000) # # connect the neuronal network # E_E_connections = sim.Projection(Pexc, Pexc, connector_ee, depressing_synapse_ee, receptor_type = 'excitatory', label = "excitatory to excitatory") # excitatory to excitatory connection # I_I_connections = sim.Projection(Pinh, Pinh, connector_ii, depressing_synapse_ii, receptor_type = 'inhibitory', label = "inhibitory to inhibitory") # inhibitory to inhibitory connection # E_I_connections = sim.Projection(Pexc, Pinh, connector_ie, facilitating_synapse_ie, receptor_type = 'excitatory', label = "excitatory to inhibitory") # from excitatory to inhibitory connection # I_E_connections = sim.Projection(Pinh, Pexc, connector_ei, facilitating_synapse_ei, receptor_type = 'inhibitory', label = "inhibitory to excitatory") # from inhibitory to excitatory # ==========injecting neuron currents OR connect to the input signal======================= syn = sim.StaticSynapse(weight=weight_ini, delay=0.5) Ie_E_connections = sim.Projection( Excinp, Pexc, sim.FixedProbabilityConnector(p_connect=0.5), syn, receptor_type='excitatory', label="excitatory input to excitatory neurons") Ii_E_connections = sim.Projection( Inhinp, Pexc, sim.FixedProbabilityConnector(p_connect=0.5), syn, receptor_type='inhibitory', label="inhibitory input to excitatory neurons") Ie_I_connections = sim.Projection( Excinp, Pinh, sim.FixedProbabilityConnector(p_connect=0.5), syn,
{'spike_times': []}) spikeSourcePlastic = sim.Population(1, sim.SpikeSourceArray, {'spike_times': stimulusPlastic}) assert (spikeSourceStim != None) assert (spikeSourcePlastic != None) # configure stdp stdp = sim.STDPMechanism(weight = 0.2, # this is the initial value of the weight timing_dependence = sim.SpikePairRule(tau_plus = 20.0, tau_minus = 20.0, A_plus = 0.01, A_minus = 0.012),\ weight_dependence = sim.AdditiveWeightDependence(w_min = 0, w_max = 0.04)) # connect stimulus sim.Projection(spikeSourceStim, neuron, sim.AllToAllConnector(), sim.StaticSynapse(weight=0.04, delay=timingPrePostStim), receptor_type='excitatory') # create plastic synapse prj = sim.Projection(spikeSourcePlastic, neuron, sim.AllToAllConnector(), stdp) weightBefore = prj.get('weight', format='list') prj.set(weight=0.15) print weightBefore neuron.record('spikes') lastInputSpike = np.max(np.concatenate((stimulus, stimulusPlastic))) runtime = lastInputSpike + stimulusOffset sim.run(runtime)
import pyNN.brian as sim # can of course replace `neuron` with `nest`, `brian`, etc. import matplotlib.pyplot as plt import numpy as np sim.setup(timestep=0.01) p_in = sim.Population(10, sim.SpikeSourcePoisson(rate=10.0), label="input") p_out = sim.Population(10, sim.EIF_cond_exp_isfa_ista(), label="AdExp neurons") syn = sim.StaticSynapse(weight=0.05) random = sim.FixedProbabilityConnector(p_connect=0.5) connections = sim.Projection(p_in, p_out, random, syn, receptor_type='excitatory') p_in.record('spikes') p_out.record('spikes') # record spikes from all neurons p_out[0:2].record(['v', 'w', 'gsyn_exc' ]) # record other variables from first two neurons for i in range(2): sim.run(500.0) spikes_in = p_in.get_data() data_out = p_out.get_data() sim.reset() connections.set(weight=0.05) sim.end() print 'finish simulation'
W_c = 0.5 # scaling factor of weight w_ee = W_c * 1 # type of synaptic connection # syn = sim.StaticSynapse(weight = 0.05, delay = 0.5) depressing_synapse_ee = sim.TsodyksMarkramSynapse(weight=w_ee, delay=0.2, U=0.5, tau_rec=800.0, tau_facil=0.01) # connect the neuronal network E_E_connections = sim.Projection( Pexc, Pexc, connector_ee, depressing_synapse_ee, receptor_type='excitatory', label="excitatory to excitatory") # excitatory to excitatory connection # ==========injecting neuron currents OR connect to the input signal======================= syn = sim.StaticSynapse(weight=weight_inp[0], delay=0.5) Ie_E_connections = sim.Projection( Excinp, Pexc, sim.FixedProbabilityConnector(p_connect=0.5), syn, receptor_type='excitatory', label="excitatory input to excitatory neurons") Ii_E_connections = sim.Projection(
def main(): ## Uninteresting setup, start up the visu process,... logfile = make_logfile_name() ensure_dir(logfile) f_h = logging.FileHandler(logfile) f_h.setLevel(SUBDEBUG) d_h = logging.StreamHandler() d_h.setLevel(INFO) utils.configure_loggers(debug_handler=d_h, file_handler=f_h) parent_conn, child_conn = multiprocessing.Pipe() p = multiprocessing.Process(target=visualisation.visualisation_process_f, name="display_process", args=(child_conn, LOGGER)) p.start() pynnn.setup(timestep=SIMU_TIMESTEP) init_logging("logfile", debug=True) LOGGER.info("Simulation started with command: %s", sys.argv) ## Network setup # First population p1 = pynnn.Population(100, pynnn.IF_curr_alpha, structure=pynnn.space.Grid2D()) p1.set({'tau_m': 20, 'v_rest': -65}) # Second population p2 = pynnn.Population(20, pynnn.IF_curr_alpha, cellparams={ 'tau_m': 15.0, 'cm': 0.9 }) # Projection 1 -> 2 prj1_2 = pynnn.Projection( p1, p2, pynnn.AllToAllConnector(allow_self_connections=False), target='excitatory') # I may need to make own PyNN Connector class. Otherwise, this is # neat: exponentially decaying probability of connections depends # on distance. Distance is only calculated using x and y, which # are on a toroidal topo with boundaries at 0 and 500. connector = pynnn.DistanceDependentProbabilityConnector( "exp(-abs(d))", space=pynnn.Space(axes='xy', periodic_boundaries=((0, 500), (0, 500), None))) # Alternately, the powerful connection set algebra (python CSA # module) can be used. weight_distr = pynnn.RandomDistribution(distribution='gamma', parameters=[1, 0.1]) prj1_2.randomizeWeights(weight_distr) # This one is in NEST but not in Brian: # source = pynnn.NoisyCurrentSource( # mean=100, stdev=50, dt=SIMU_TIMESTEP, # start=10.0, stop=SIMU_DURATION, rng=pynnn.NativeRNG(seed=100)) source = pynnn.DCSource(start=10.0, stop=SIMU_DURATION, amplitude=100) source.inject_into(list(p1.sample(50).all())) p1.record(to_file=False) p2.record(to_file=False) ## Build and send the visualizable network structure adapter = pynn_to_visu.PynnToVisuAdapter(LOGGER) adapter.add_pynn_population(p1) adapter.add_pynn_population(p2) adapter.add_pynn_projection(p1, p2, prj1_2.connection_manager) adapter.commit_structure() parent_conn.send(adapter.output_struct) # Number of chunks to run the simulation: n_chunks = SIMU_DURATION // SIMU_TO_VISU_MESSAGE_PERIOD last_chunk_duration = SIMU_DURATION % SIMU_TO_VISU_MESSAGE_PERIOD # Run the simulator for visu_i in xrange(n_chunks): pynnn.run(SIMU_TO_VISU_MESSAGE_PERIOD) parent_conn.send(adapter.make_activity_update_message()) LOGGER.debug("real current p1 spike counts: %s", p1.get_spike_counts().values()) if last_chunk_duration > 0: pynnn.run(last_chunk_duration) parent_conn.send(adapter.make_activity_update_message()) # Cleanup pynnn.end() # Wait for the visualisation process to terminate p.join(VISU_PROCESS_JOIN_TIMEOUT)
Excinp = sim.Population(10, sim.SpikeSourcePoisson(rate = 20.0, start = 0, duration = run_time * no_run)) # cell_type_parameters = {'tau_refrac': 0.1, 'v_thresh': -50.0, 'tau_m': 20.0, 'tau_syn_E': 0.5, 'v_rest': -65.0,\ # 'cm': 1.0, 'v_reset': -65.0, 'tau_syn_I': 0.5, 'i_offset': 0.0} # print(sim.IF_curr_alpha.default_parameters) # cell_type = sim.IF_cond_exp(**cell_type_parameters) # neuron type of population Pexc = sim.Population(10, sim.EIF_cond_exp_isfa_ista(), label = "excitotary neurons") # Pexc.set(tau_refrac = 0.1, v_thresh = -50.0, tau_m = 20.0, tau_syn_E = 0.5, v_rest = -65.0, \ # cm = 1.0, v_reset = -65, tau_syn_I = 0.5, i_offset = 0.0) # Pexc.initialize(**cell_type_parameters) # print Pexc.celltype.default_initial_values # print Pexc.get('tau_m') # syn = sim.StaticSynapse(weight = 0.05, delay = 0.5) # depressing_synapse_ee = sim.TsodyksMarkramSynapse(weight = 0.05, delay = 0.2, U = 0.5, tau_rec = 800.0, tau_facil = 0.01) facilitating_synapse_ee = sim.TsodyksMarkramSynapse(weight = 0.05, delay = 0.5, U = 0.04, tau_rec = 100.0, tau_facil = 1000) connection = sim.Projection(Excinp, Pexc, sim.AllToAllConnector(), facilitating_synapse_ee, receptor_type = 'excitatory') # E_E_connection = sim.Projection(Pexc, Pexc, sim.FixedProbabilityConnector(p_connect = 0.5), depressing_synapse_ee, receptor_type = 'excitatory') Excinp.record('spikes') # Excinp[1].record('v') Pexc.record('spikes') Pexc[5:6].record('v') for i in range(no_run): sim.run_until(run_time * (i + 1)) print('the time is %.1f' %(run_time * (i + 1))) spikes = Excinp.get_data() spike = Pexc.get_data() # print connection.get('weight',format = 'array')
In_2 = sim.Population( 10, sim.SpikeSourcePoisson(rate=source_rate[1 - training_label[i]])) In = In_1 + In_2 Out_1 = sim.Population(10, sim.IF_cond_exp()) Out_2 = sim.Population(10, sim.IF_cond_exp()) Out = Out_1 + Out_2 syn_1_1 = sim.StaticSynapse(weight=w1_1, delay=0.5) syn_1_2 = sim.StaticSynapse(weight=w1_2, delay=0.5) syn_2_1 = sim.StaticSynapse(weight=w2_1, delay=0.5) syn_2_2 = sim.StaticSynapse(weight=w2_2, delay=0.5) prj_1_1 = sim.Projection(In_1, Out_1, sim.ArrayConnector(connector1_1), syn_1_1, receptor_type='excitatory') prj_1_2 = sim.Projection(In_1, Out_2, sim.ArrayConnector(connector1_2), syn_1_2, receptor_type='excitatory') prj_2_1 = sim.Projection(In_2, Out_1, sim.ArrayConnector(connector2_1), syn_2_1, receptor_type='excitatory') prj_2_2 = sim.Projection(In_2, Out_2, sim.ArrayConnector(connector2_2),
def testOneToOne(self): """For all connections created with "OneToOne" ...""" prj = sim.Projection(self.source33, self.target33, sim.OneToOneConnector(weights=0.5)) self.assertEqual(prj._connections.W.getnnz(), self.target33.cell.size)
def train(label, untrained_weights=None): organisedStim = {} labelSpikes = [] spikeTimes = generate_data(label) for i in range(output_size): labelSpikes.append([]) labelSpikes[label] = [int(max(max(spikeTimes))) + 1] if untrained_weights == None: untrained_weights = RandomDistribution('uniform', low=wMin, high=wMaxInit).next(input_size * output_size) #untrained_weights = RandomDistribution('normal_clipped', mu=0.1, sigma=0.05, low=wMin, high=wMaxInit).next(input_size*output_size) untrained_weights = np.around(untrained_weights, 3) #saveWeights(untrained_weights, 'untrained_weightssupmodel1traj') print("init!") print "length untrained_weights :", len(untrained_weights) if len(untrained_weights) > input_size: training_weights = [[0 for j in range(output_size)] for i in range(input_size) ] #np array? size 1024x25 k = 0 #for i in untrained_weights: # training_weights[i[0]][i[1]]=i[2] for i in range(input_size): for j in range(output_size): training_weights[i][j] = untrained_weights[k] k += 1 else: training_weights = untrained_weights connections = [] for n_pre in range(input_size): # len(untrained_weights) = input_size for n_post in range( output_size ): # len(untrained_weight[0]) = output_size; 0 or any n_pre connections.append((n_pre, n_post, training_weights[n_pre][n_post], __delay__)) #index runTime = int(max(max(spikeTimes))) + 100 ##################### sim.setup(timestep=1) #def populations layer1 = sim.Population(input_size, sim.SpikeSourceArray, {'spike_times': spikeTimes}, label='inputspikes') layer2 = sim.Population(output_size, sim.IF_curr_exp, cellparams=cell_params_lif, label='outputspikes') supsignal = sim.Population(output_size, sim.SpikeSourceArray, {'spike_times': labelSpikes}, label='supersignal') #def learning rule stdp = sim.STDPMechanism( #weight=untrained_weights, #weight=0.02, # this is the initial value of the weight #delay="0.2 + 0.01*d", timing_dependence=sim.SpikePairRule(tau_plus=tauPlus, tau_minus=tauMinus, A_plus=aPlus, A_minus=aMinus), #weight_dependence=sim.MultiplicativeWeightDependence(w_min=wMin, w_max=wMax), weight_dependence=sim.AdditiveWeightDependence(w_min=wMin, w_max=wMax), dendritic_delay_fraction=0) #def projections stdp_proj = sim.Projection(layer1, layer2, sim.FromListConnector(connections), synapse_type=stdp) inhibitory_connections = sim.Projection( layer2, layer2, sim.AllToAllConnector(allow_self_connections=False), synapse_type=sim.StaticSynapse(weight=inhibWeight, delay=__delay__), receptor_type='inhibitory') stim_proj = sim.Projection(supsignal, layer2, sim.OneToOneConnector(), synapse_type=sim.StaticSynapse( weight=stimWeight, delay=__delay__)) layer1.record(['spikes']) layer2.record(['v', 'spikes']) supsignal.record(['spikes']) sim.run(runTime) print("Weights:{}".format(stdp_proj.get('weight', 'list'))) weight_list = [ stdp_proj.get('weight', 'list'), stdp_proj.get('weight', format='list', with_address=False) ] neo = layer2.get_data(["spikes", "v"]) spikes = neo.segments[0].spiketrains v = neo.segments[0].filter(name='v')[0] neostim = supsignal.get_data(["spikes"]) print(label) spikestim = neostim.segments[0].spiketrains neoinput = layer1.get_data(["spikes"]) spikesinput = neoinput.segments[0].spiketrains plt.close('all') pplt.Figure(pplt.Panel(v, ylabel="Membrane potential (mV)", xticks=True, yticks=True, xlim=(0, runTime)), pplt.Panel(spikesinput, xticks=True, yticks=True, markersize=2, xlim=(0, runTime)), pplt.Panel(spikestim, xticks=True, yticks=True, markersize=2, xlim=(0, runTime)), pplt.Panel(spikes, xticks=True, xlabel="Time (ms)", yticks=True, markersize=2, xlim=(0, runTime)), title="Training" + str(label), annotations="Training" + str(label)).save('plot/' + str(trylabel) + str(label) + '_training.png') #plt.hist(weight_list[1], bins=100) #plt.show() plt.close('all') print(wMax) ''' plt.hist([weight_list[1][0:input_size], weight_list[1][input_size:input_size*2], weight_list[1][input_size*2:]], bins=20, label=['neuron 0', 'neuron 1', 'neuron 2'], range=(0, wMax)) plt.title('weight distribution') plt.xlabel('Weight value') plt.ylabel('Weight count') ''' #plt.show() #plt.show() sim.end() for i in weight_list[0]: #training_weights[int(i[0])][int(i[1])]=float(i[2]) weight_list[1][int(i[0]) * output_size + int(i[1])] = i[2] return weight_list[1]
facilitating_synapse_ie = sim.TsodyksMarkramSynapse(weight=w_ie, delay=0.5, U=0.04, tau_rec=100.0, tau_facil=1000) facilitating_synapse_ei = sim.TsodyksMarkramSynapse(weight=w_ei, delay=0.5, U=0.04, tau_rec=100.0, tau_facil=1000) # connect the neuronal network E_E_connections = sim.Projection( Pexc, Pexc, connector_ee, depressing_synapse_ee, receptor_type='excitatory', label="excitatory to excitatory") # excitatory to excitatory connection I_I_connections = sim.Projection( Pinh, Pinh, connector_ii, depressing_synapse_ii, receptor_type='inhibitory', label="inhibitory to inhibitory") # inhibitory to inhibitory connection E_I_connections = sim.Projection(Pexc, Pinh, connector_ie, facilitating_synapse_ie, receptor_type='excitatory',
tc_cells = sim.Population( 100, thalamocortical_type, structure=RandomStructure(boundary=Sphere(radius=200.0)), initial_values={'v': -70.0}, label="Thalamocortical neurons") from pyNN.random import RandomDistribution v_init = RandomDistribution('uniform', (-70.0, -60.0)) ctx_cells = sim.Population(500, cortical_type, structure=Grid2D(dx=10.0, dy=10.0), initial_values={'v': v_init}, label="Cortical neurons") pre = tc_cells[:50] post = ctx_cells[:50] excitatory_connections = sim.Projection(pre, post, sim.AllToAllConnector(), sim.StaticSynapse(weight=0.123)) #full example from pyNN.space import Space rng = NumpyRNG(seed=64754) sparse_connectivity = sim.FixedProbabilityConnector(0.1, rng=rng) weight_distr = RandomDistribution('normal', [0.01, 1e-3], rng=rng) facilitating = sim.TsodyksMarkramSynapse(U=0.04, tau_rec=100.0, tau_facil=1000.0, weight=weight_distr, delay=lambda d: 0.1 + d / 100.0) space = Space(axes='xy') #specifying periodic boundary conditions #space = Space(periodic_boundaries=((0,500), (0,500), None)) #calculates distance on the surface of a torus of circumference 500 µm #(wrap-around in the x- and y-dimensions but not z)
# syn = sim.StaticSynapse(weight = 0.05, delay = 0.5) depressing_synapse_ee = sim.TsodyksMarkramSynapse(weight=0.05, delay=0.2, U=0.5, tau_rec=800.0, tau_facil=0.01) facilitating_synapse_ee = sim.TsodyksMarkramSynapse(weight=0.05, delay=0.5, U=0.04, tau_rec=100.0, tau_facil=1000) static_synapse = sim.StaticSynapse(weight=0.05, delay=0.5) Input_E_connection = sim.Projection(Excinp, Pexc, sim.AllToAllConnector(), static_synapse, receptor_type='excitatory') E_E_connection = sim.Projection(Pexc, Pexc, sim.FixedProbabilityConnector(p_connect=0.5), depressing_synapse_ee, receptor_type='excitatory') Excinp.record('spikes') # Excinp[1].record('v') Pexc.record('spikes') Pexc[5:6].record('v') for i in range(no_run):
# todo: the initail parameters of neurons might be modified cell_type_parameters = {'tau_refrac': 0.1, 'v_thresh': -50.0, 'tau_m': 20.0, 'tau_syn_E': 0.5, 'v_rest': -65.0,\ 'cm': 1.0, 'v_reset': -65.0, 'tau_syn_I': 0.5, 'i_offset': 0.0} # print(sim.IF_curr_alpha.default_parameters) cell_type = sim.IF_cond_exp(**cell_type_parameters) # neuron type of population Pexc = sim.Population(3, cell_type, label = "excitotary neurons") # excitatory neuron population # ==========generate OR read in the input spikes data===================== noSpikes = 20 # number of spikes per chanel per simulation run stimSpikes = RandomDistribution('uniform', low = 0, high = 500, rng = NumpyRNG(seed = 72386)).next(noSpikes) # generate a time uniform distributed signal with Exc_in + Inh_in chanels and noSpikes for each chanel Excinp = sim.Population(3, sim.SpikeSourceArray(spike_times = stimSpikes)) syn = sim.StaticSynapse(weight = 0.05, delay = 0.5) Ie_A_connections = sim.Projection(Excinp, Pexc, sim.FixedProbabilityConnector(p_connect = 0.5), syn, receptor_type = 'excitatory', label = "excitatory input") for i in range(ite_no): sim.run(100) # ==========write the data====================== sim.reset() Ie_A_connections.set(weight = (i + 2) * 0.05) print Ie_A_connections.get('weight', format = 'list') # Ie_A_connections.set(weight = 0.02) # print Ie_A_connections.get('weight', format = 'list') # sim.run(200) # sim.reset()