def test_get_spikes(self):
"""
test for get spikes
:return:
"""
p.setup(timestep=0.1, min_delay=1.0, max_delay=14.40)
n_neurons = 200 # number of neurons in each population
p.set_number_of_neurons_per_core("IF_curr_exp", n_neurons / 2)
cell_params_lif = {
'cm': 0.25,
'i_offset': 0.0,
'tau_m': 20.0,
'tau_refrac': 2.0,
'tau_syn_E': 5.0,
'tau_syn_I': 5.0,
'v_reset': -70.0,
'v_rest': -65.0,
'v_thresh': -50.0
}
populations = list()
projections = list()
weight_to_spike = 2.0
delay = 1.7
loop_connections = list()
for i in range(0, n_neurons):
single_connection = (i, ((i + 1) % n_neurons), weight_to_spike,
delay)
loop_connections.append(single_connection)
injection_connection = [(0, 0, weight_to_spike, 1)]
spike_array = {'spike_times': [[0]]}
populations.append(
p.Population(n_neurons,
p.IF_curr_exp,
cell_params_lif,
label='pop_1'))
populations.append(
p.Population(1,
p.SpikeSourceArray,
spike_array,
label='inputSpikes_1'))
projections.append(
p.Projection(populations[0], populations[0],
p.FromListConnector(loop_connections)))
projections.append(
p.Projection(populations[1], populations[0],
p.FromListConnector(injection_connection)))
populations[0].record_v()
populations[0].record_gsyn()
populations[0].record()
p.run(500)
spikes = populations[0].getSpikes(compatible_output=True)
pre_recorded_spikes = [[0, 3.5], [1, 6.7], [2, 9.9], [3, 13.1],
[4, 16.3], [5, 19.5], [6, 22.7], [7, 25.9],
[8, 29.1], [9, 32.3], [10, 35.5], [11, 38.7],
[12, 41.9], [13, 45.1], [14, 48.3], [15, 51.5],
[16, 54.7], [17, 57.9], [18, 61.1], [19, 64.3],
[20, 67.5], [21, 70.7], [22, 73.9], [23, 77.1],
[24, 80.3], [25, 83.5], [26, 86.7], [27, 89.9],
[28, 93.1], [29, 96.3], [30, 99.5], [31, 102.7],
[32, 105.9], [33, 109.1], [34, 112.3],
[35, 115.5], [36, 118.7], [37, 121.9],
[38, 125.1], [39, 128.3], [40, 131.5],
[41, 134.7], [42, 137.9], [43, 141.1],
[44, 144.3], [45, 147.5], [46, 150.7],
[47, 153.9], [48, 157.1], [49, 160.3],
[50, 163.5], [51, 166.7], [52, 169.9],
[53, 173.1], [54, 176.3], [55, 179.5],
[56, 182.7], [57, 185.9], [58, 189.1],
[59, 192.3], [60, 195.5]]
p.end()
for spike_element, read_element in zip(spikes, pre_recorded_spikes):
self.assertEqual(round(spike_element[0], 1),
round(read_element[0], 1))
self.assertEqual(round(spike_element[1], 1),
round(read_element[1], 1))
for x_ind in range(input_size):
for y_ind in range(output_size):
weights = w1[x_ind][y_ind]
#weights = w1[x_ind]
#for i in range(w.shape[1]):
if weights > 0:
conn_list_exci.append((x_ind, y_ind, weights, 1.))
elif weights < 0:
conn_list_inhi.append((x_ind, y_ind, weights, 1.))
#print output_size
pop_out = p.Population(output_size, p.IF_curr_exp, cell_params_lif)
if len(conn_list_exci) > 0:
connections['e2e'] = p.Projection(
pop_in,
pop_out,
p.FromListConnector(conn_list_exci),
label='excitatory')
if len(conn_list_inhi) > 0:
connections['i2i'] = p.Projection(
pop_in,
pop_out,
p.FromListConnector(conn_list_inhi),
label='inhibitory')
#connection=p.Projection(pop_in, pop_out,AllToAllConnector(weights = pos_w), target='excitatory')
#connection=p.Projection(pop_in, pop_out,AllToAllConnector(weights = neg_w), target='inhibitory')
pop_list.append(pop_out)
pop_in = pop_out
except EOFError:
pass
pop_out.record()
def run_test(w_list, cell_para, spike_source_data):
#Du.set_trace()
pop_list = []
p.setup(timestep=1.0, min_delay=1.0, max_delay=3.0)
#input poisson layer
input_size = w_list[0].shape[0]
#print w_list[0].shape[0]
#print w_list[1].shape[0]
list = []
for j in range(input_size):
list.append(spike_source_data[j])
pop_in = p.Population(input_size, p.SpikeSourceArray,
{'spike_times': list})
pop_list.append(pop_in)
#for j in range(input_size):
#pop_in[j].spike_times = spike_source_data[j]
#pop_in = p.Population(input_size, p.SpikeSourceArray, {'spike_times' : []})
#for j in range(input_size):
# pop_in[j].spike_times = spike_source_data[j]
#pop_list.append(pop_in)
#count =0
#print w_list[0].shape[0]
for w in w_list:
input_size = w.shape[0]
#count = count+1
#print count
output_size = w.shape[1]
#pos_w = np.copy(w)
#pos_w[pos_w < 0] = 0
#neg_w = np.copy(w)
#neg_w[neg_w > 0] = 0
conn_list_exci = []
conn_list_inhi = []
#k_size=in_size-out_size+1
for x_ind in range(input_size):
for y_ind in range(output_size):
weights = w[x_ind][y_ind]
#for i in range(w.shape[1]):
if weights > 0:
conn_list_exci.append((x_ind, y_ind, weights, 1.))
elif weights < 0:
conn_list_inhi.append((x_ind, y_ind, weights, 1.))
#print output_size
pop_out = p.Population(output_size, p.IF_curr_exp, cell_para)
if len(conn_list_exci) > 0:
p.Projection(pop_in,
pop_out,
p.FromListConnector(conn_list_exci),
target='excitatory')
if len(conn_list_inhi) > 0:
p.Projection(pop_in,
pop_out,
p.FromListConnector(conn_list_inhi),
target='inhibitory')
#p.Projection(pop_in, pop_out, p.AllToAllConnector(weights = pos_w), target='excitatory')
#p.Projection(pop_in, pop_out, p.AllToAllConnector(weights = neg_w), target='inhibitory')
pop_list.append(pop_out)
pop_in = pop_out
pop_out.record()
run_time = np.ceil(np.max(spike_source_data)[0] / 1000.) * 1000
#print run_time
p.run(run_time)
spikes = pop_out.getSpikes(compatible_output=True)
return spikes
# Connect key spike injector to breakout population
p.Projection(key_input, breakout_pop, p.OneToOneConnector(weights=2))
# Create visualiser
visualiser = spinn_breakout.Visualiser(UDP_PORT,
key_input_connection,
x_res=X_RESOLUTION,
y_res=Y_RESOLUTION,
x_bits=X_BITS,
y_bits=Y_BITS)
direction_population = p.Population(3, p.IF_curr_exp, cell_params_lif)
p.Projection(
direction_population, breakout_pop,
p.FromListConnector([(1, 1, weight_to_spike, delay),
(2, 2, weight_to_spike, delay)]))
## Code to implement https://trello.com/c/rLwRIOxq/111-hard-coded-breakout follows
on_ids = get_on_neuron_ids()
off_ids = get_off_neuron_ids()
no_paddle_on_ids = on_ids[:-1, :]
only_paddle_on_ids = on_ids[-1, :]
only_paddle_off_ids = off_ids[-1, :]
paddle_presence_weight = .7
ball_presence_weight = .7
# Create all needed populations
ball_position = p.Population(GAME_WIDTH // pool_size,
p.IF_curr_exp,
cell_params_lif,
projections = list()
weight_to_spike = 2.0
delay = 1
connections = list()
for i in range(0, nNeurons):
singleConnection = (i, ((i + 1) % nNeurons), weight_to_spike, delay)
connections.append(singleConnection)
injectionConnection = [(0, 0, weight_to_spike, 1)]
spikeArray = {'spike_times': [[0]]}
populations.append(p.Population(nNeurons, p.IF_curr_exp, cell_params_lif, label='pop_1'))
populations.append(p.Population(1, p.SpikeSourceArray, spikeArray, label='inputSpikes_1'))
projections.append(p.Projection(populations[0], populations[0], p.FromListConnector(connections)))
projections.append(p.Projection(populations[1], populations[0], p.FromListConnector(injectionConnection)))
#populations[0].record_v()
#populations[0].record_gsyn()
populations[0].record()
p.run(1000)
v = None
gsyn = None
spikes = None
#v = populations[0].get_v(compatible_output=True)
#gsyn = populations[0].get_gsyn(compatible_output=True)
spikes = populations[0].getSpikes(compatible_output=True)
# Create a connection from the injector into the populations
Frontend.Projection(injector_forward, pop_forward,
Frontend.OneToOneConnector(weights=weight_to_spike))
Frontend.Projection(injector_backward, pop_backward,
Frontend.OneToOneConnector(weights=weight_to_spike))
# Synfire chain connections where each neuron is connected to its next neuron
# NOTE: there is no recurrent connection so that each chain stops once it
# reaches the end
loop_forward = list()
loop_backward = list()
for i in range(0, n_neurons - 1):
loop_forward.append((i, (i + 1) % n_neurons, weight_to_spike, 3))
loop_backward.append(((i + 1) % n_neurons, i, weight_to_spike, 3))
Frontend.Projection(pop_forward, pop_forward,
Frontend.FromListConnector(loop_forward))
Frontend.Projection(pop_backward, pop_backward,
Frontend.FromListConnector(loop_backward))
# record spikes from the synfire chains so that we can read off valid results
# in a safe way afterwards, and verify the behavior
pop_forward.record()
pop_backward.record()
# Activate the sending of live spikes
ExternalDevices.activate_live_output_for(pop_forward,
database_notify_host="localhost",
database_notify_port_num=19996)
ExternalDevices.activate_live_output_for(pop_backward,
database_notify_host="localhost",
database_notify_port_num=19996)
def test_get_gsyn(self):
"""
test that tests the getting of gsyn from a pre determined recording
:return:
"""
p.setup(timestep=1, min_delay=1.0, max_delay=14.40)
n_neurons = 10 # number of neurons in each population
runtime = 50
p.set_number_of_neurons_per_core("IF_curr_exp", n_neurons / 2)
cell_params_lif = {'cm': 0.25,
'i_offset': 0.0,
'tau_m': 20.0,
'tau_refrac': 2.0,
'tau_syn_E': 5.0,
'tau_syn_I': 5.0,
'v_reset': -70.0,
'v_rest': -65.0,
'v_thresh': -50.0
}
populations = list()
projections = list()
weight_to_spike = 2.0
delay = 1.7
loop_connections = list()
for i in range(0, n_neurons):
single_connection = (i, ((i + 1) % n_neurons), weight_to_spike,
delay)
loop_connections.append(single_connection)
injection_connection = [(0, 0, weight_to_spike, 1)]
spike_array = {'spike_times': [[0]]}
populations.append(p.Population(n_neurons, p.IF_curr_exp,
cell_params_lif,
label='pop_1'))
populations.append(p.Population(1, p.SpikeSourceArray, spike_array,
label='inputSpikes_1'))
projections.append(p.Projection(populations[0], populations[0],
p.FromListConnector(loop_connections)))
projections.append(p.Projection(populations[1], populations[0],
p.FromListConnector(injection_connection)))
populations[0].record_v()
populations[0].record_gsyn()
populations[0].record()
p.run(runtime)
gsyn = populations[0].get_gsyn(compatible_output=True)
current_file_path = os.path.dirname(os.path.abspath(__file__))
current_file_path = os.path.join(current_file_path, "gsyn.data")
pre_recorded_data = p.utility_calls.read_in_data_from_file(
current_file_path, 0, n_neurons, 0, runtime)
p.end()
for spike_element, read_element in zip(gsyn, pre_recorded_data):
self.assertEqual(round(spike_element[0], 1),
round(read_element[0], 1))
self.assertEqual(round(spike_element[1], 1),
round(read_element[1], 1))
self.assertEqual(round(spike_element[2], 1),
round(read_element[2], 1))
def test_something(self):
#!/usr/bin/python
import pylab
import spynnaker.pyNN as p
p.setup(timestep=1.0, min_delay=1.0, max_delay=144.0)
nNeurons = 200 # number of neurons in each population
p.set_number_of_neurons_per_core("IF_curr_exp", nNeurons / 2)
cell_params_lif = {
'cm': 0.25, # nF
'i_offset': 0.0,
'tau_m': 20.0,
'tau_refrac': 2.0,
'tau_syn_E': 5.0,
'tau_syn_I': 5.0,
'v_reset': -70.0,
'v_rest': -65.0,
'v_thresh': -50.0
}
populations = list()
projections = list()
weight_to_spike = 2.0
delay = 17
loop_connections = list()
for i in range(0, nNeurons):
single_connection = (i, ((i + 1) % nNeurons), weight_to_spike,
delay)
loop_connections.append(single_connection)
injection_connection = [(0, 0, weight_to_spike, 1)]
spike_array = {'spike_times': [[0]]}
populations.append(
p.Population(nNeurons,
p.IF_curr_exp,
cell_params_lif,
label='pop_1'))
populations.append(
p.Population(1,
p.SpikeSourceArray,
spike_array,
label='inputSpikes_1'))
projections.append(
p.Projection(populations[0], populations[0],
p.FromListConnector(loop_connections)))
projections.append(
p.Projection(populations[1], populations[0],
p.FromListConnector(injection_connection)))
populations[0].record_v()
#populations[0].record_gsyn()
#populations[0].record(visualiser_mode=p.VISUALISER_MODES.RASTER)
p.run(5000)
v = None
gsyn = None
spikes = None
v = populations[0].get_v(compatible_output=True)
#assert(v == )
#gsyn = populations[0].get_gsyn(compatible_output=True)
#spikes = populations[0].getSpikes(compatible_output=True)
if spikes is not None:
print spikes
pylab.figure()
pylab.plot([i[1] for i in spikes], [i[0] for i in spikes], ".")
pylab.xlabel('Time/ms')
pylab.ylabel('spikes')
pylab.title('spikes')
pylab.show()
else:
print "No spikes received"
# Make some graphs
if v is not None:
ticks = len(v) / nNeurons
pylab.figure()
pylab.xlabel('Time/ms')
pylab.ylabel('v')
pylab.title('v')
for pos in range(0, nNeurons, 20):
v_for_neuron = v[pos * ticks:(pos + 1) * ticks]
pylab.plot([i[1] for i in v_for_neuron],
[i[2] for i in v_for_neuron])
pylab.show()
if gsyn is not None:
ticks = len(gsyn) / nNeurons
pylab.figure()
pylab.xlabel('Time/ms')
pylab.ylabel('gsyn')
pylab.title('gsyn')
for pos in range(0, nNeurons, 20):
gsyn_for_neuron = gsyn[pos * ticks:(pos + 1) * ticks]
pylab.plot([i[1] for i in gsyn_for_neuron],
[i[2] for i in gsyn_for_neuron])
pylab.show()
p.end(stop_on_board=True)
def test_print_spikes(self):
machine_time_step = 0.1
p.setup(timestep=machine_time_step, min_delay=1.0, max_delay=14.40)
n_neurons = 20 # number of neurons in each population
p.set_number_of_neurons_per_core("IF_curr_exp", n_neurons / 2)
cell_params_lif = {
'cm': 0.25,
'i_offset': 0.0,
'tau_m': 20.0,
'tau_refrac': 2.0,
'tau_syn_E': 5.0,
'tau_syn_I': 5.0,
'v_reset': -70.0,
'v_rest': -65.0,
'v_thresh': -50.0
}
populations = list()
projections = list()
weight_to_spike = 2.0
delay = 1.7
loop_connections = list()
for i in range(0, n_neurons):
single_connection = (i, ((i + 1) % n_neurons), weight_to_spike,
delay)
loop_connections.append(single_connection)
injection_connection = [(0, 0, weight_to_spike, 1)]
spike_array = {'spike_times': [[0]]}
populations.append(
p.Population(n_neurons,
p.IF_curr_exp,
cell_params_lif,
label='pop_1'))
populations.append(
p.Population(1,
p.SpikeSourceArray,
spike_array,
label='inputSpikes_1'))
projections.append(
p.Projection(populations[0], populations[0],
p.FromListConnector(loop_connections)))
projections.append(
p.Projection(populations[1], populations[0],
p.FromListConnector(injection_connection)))
populations[0].record_v()
populations[0].record_gsyn()
populations[0].record()
p.run(500)
spikes = populations[0].getSpikes(compatible_output=True)
current_file_path = os.path.dirname(os.path.abspath(__file__))
current_file_path = os.path.join(current_file_path, "spikes.data")
spike_file = populations[0].printSpikes(current_file_path)
spike_reader = p.utility_calls.read_spikes_from_file(
current_file_path,
min_atom=0,
max_atom=n_neurons,
min_time=0,
max_time=500)
read_in_spikes = spike_reader.spike_times
p.end()
os.remove(current_file_path)
for spike_element, read_element in zip(spikes, read_in_spikes):
self.assertEqual(round(spike_element[0], 1),
round(read_element[0], 1))
self.assertEqual(round(spike_element[1], 1),
round(read_element[1], 1))
# cond setup
populations.append(
p.Population(nNeurons, p.IF_curr_exp, cell_params_lif, label='pop_curr'))
# izk setup
populations.append(
p.Population(nNeurons, p.IZK_curr_exp, cell_params_izk, label='izk pop'))
# sink pop for spikes to go to (otherwise they are not recorded as firing)
populations.append(
p.Population(nNeurons, p.IF_curr_exp, cell_params_lif, label='sink_pop'))
populations.append(
p.Population(1, p.SpikeSourceArray, spikeArray, label='inputSpike'))
projections.append(
p.Projection(populations[4], populations[0],
p.FromListConnector(cond_injection_connection)))
projections.append(
p.Projection(populations[4], populations[1],
p.FromListConnector(curr_injection_connection)))
projections.append(
p.Projection(populations[4], populations[2],
p.FromListConnector(izk_injection_connection)))
projections.append(
p.Projection(populations[2], populations[3],
p.FromListConnector(sinkConnection)))
projections.append(
p.Projection(populations[1], populations[3],
p.FromListConnector(sinkConnection)))
projections.append(
p.Projection(populations[0], populations[3],
p.FromListConnector(sinkConnection)))
#TrianSpikeON = BuildTrainingSpike(order,1)
in_spike = BuildTrainingSpike_with_noise(order, 1, 198)
#print TrianSpikeON
spikeArrayOn = {'spike_times': in_spike}
ON_pop = sim.Population(prepop_size,
sim.SpikeSourceArray,
spikeArrayOn,
label='inputSpikes_On')
post_pop = sim.Population(postpop_size,
sim.IF_curr_exp,
cell_params_lif,
label='post_1')
connectionsOn = sim.Projection(
ON_pop, post_pop,
sim.FromListConnector(convert_weights_to_list(weights_import, delay)))
#inhibitory between the neurons
connection_I = sim.Projection(post_pop,
post_pop,
sim.AllToAllConnector(
weights=15,
delays=1,
allow_self_connections=False),
target='inhibitory')
ON_pop.record()
post_pop.record()
sim.run(simtime)
# == Get the Simulated Data =================================================
def __init__(self):
# initial call to set up the front end (pynn requirement)
Frontend.setup(timestep=1.0, min_delay=1.0, max_delay=144.0)
# neurons per population and the length of runtime in ms for the
# simulation, as well as the expected weight each spike will contain
self.n_neurons = 100
run_time = 100000
weight_to_spike = 2.0
# neural parameters of the IF_curr model used to respond to injected
# spikes.
# (cell params for a synfire chain)
cell_params_lif = {
'cm': 0.25,
'i_offset': 0.0,
'tau_m': 20.0,
'tau_refrac': 2.0,
'tau_syn_E': 5.0,
'tau_syn_I': 5.0,
'v_reset': -70.0,
'v_rest': -65.0,
'v_thresh': -50.0
}
##################################
# Parameters for the injector population. This is the minimal set of
# parameters required, which is for a set of spikes where the key is
# not important. Note that a virtual key *will* be assigned to the
# population, and that spikes sent which do not match this virtual key
# will be dropped; however, if spikes are sent using 16-bit keys, they
# will automatically be made to match the virtual key. The virtual
# key assigned can be obtained from the database.
##################################
cell_params_spike_injector = {
# The port on which the spiNNaker machine should listen for
# packets. Packets to be injected should be sent to this port on
# the spiNNaker machine
'port': 12345
}
##################################
# Parameters for the injector population. Note that each injector
# needs to be given a different port. The virtual key is assigned
# here, rather than being allocated later. As with the above, spikes
# injected need to match this key, and this will be done automatically
# with 16-bit keys.
##################################
cell_params_spike_injector_with_key = {
# The port on which the spiNNaker machine should listen for
# packets. Packets to be injected should be sent to this port on
# the spiNNaker machine
'port': 12346,
# This is the base key to be used for the injection, which is used
# to allow the keys to be routed around the spiNNaker machine.
# This assignment means that 32-bit keys must have the high-order
# 16-bit set to 0x7; This will automatically be prepended to
# 16-bit keys.
'virtual_key': 0x70000
}
# create synfire populations (if cur exp)
pop_forward = Frontend.Population(self.n_neurons,
Frontend.IF_curr_exp,
cell_params_lif,
label='pop_forward')
pop_backward = Frontend.Population(self.n_neurons,
Frontend.IF_curr_exp,
cell_params_lif,
label='pop_backward')
# Create injection populations
injector_forward = Frontend.Population(
self.n_neurons,
ExternalDevices.SpikeInjector,
cell_params_spike_injector_with_key,
label='spike_injector_forward')
injector_backward = Frontend.Population(
self.n_neurons,
ExternalDevices.SpikeInjector,
cell_params_spike_injector,
label='spike_injector_backward')
# Create a connection from the injector into the populations
Frontend.Projection(
injector_forward, pop_forward,
Frontend.OneToOneConnector(weights=weight_to_spike))
Frontend.Projection(
injector_backward, pop_backward,
Frontend.OneToOneConnector(weights=weight_to_spike))
# Synfire chain connections where each neuron is connected to its next
# neuron
# NOTE: there is no recurrent connection so that each chain stops once
# it reaches the end
loop_forward = list()
loop_backward = list()
for i in range(0, self.n_neurons - 1):
loop_forward.append(
(i, (i + 1) % self.n_neurons, weight_to_spike, 3))
loop_backward.append(
((i + 1) % self.n_neurons, i, weight_to_spike, 3))
Frontend.Projection(pop_forward, pop_forward,
Frontend.FromListConnector(loop_forward))
Frontend.Projection(pop_backward, pop_backward,
Frontend.FromListConnector(loop_backward))
# record spikes from the synfire chains so that we can read off valid
# results in a safe way afterwards, and verify the behavior
pop_forward.record()
pop_backward.record()
# Activate the sending of live spikes
ExternalDevices.activate_live_output_for(
pop_forward,
database_notify_host="localhost",
database_notify_port_num=19996)
ExternalDevices.activate_live_output_for(
pop_backward,
database_notify_host="localhost",
database_notify_port_num=19996)
# set up gui
from multiprocessing import Process
p = Process(target=GUI, args=[self.n_neurons])
p.start()
# Run the simulation on spiNNaker
Frontend.run(run_time)
# Retrieve spikes from the synfire chain population
spikes_forward = pop_forward.getSpikes()
spikes_backward = pop_backward.getSpikes()
# If there are spikes, plot using matplotlib
if len(spikes_forward) != 0 or len(spikes_backward) != 0:
pylab.figure()
if len(spikes_forward) != 0:
pylab.plot([i[1] for i in spikes_forward],
[i[0] for i in spikes_forward], "b.")
if len(spikes_backward) != 0:
pylab.plot([i[1] for i in spikes_backward],
[i[0] for i in spikes_backward], "r.")
pylab.ylabel('neuron id')
pylab.xlabel('Time/ms')
pylab.title('spikes')
pylab.show()
else:
print "No spikes received"
# Clear data structures on spiNNaker to leave the machine in a clean
# state for future executions
Frontend.end()
p.join()
def _connect_spike_sources(self, retinae=None, verbose=False):
if verbose:
print "INFO: Connecting Spike Sources to Network."
global _retina_proj_l, _retina_proj_r
# left is 0--dimensionRetinaY-1; right is dimensionRetinaY--dimensionRetinaY*2-1
connListRetLBlockerL = []
connListRetLBlockerR = []
connListRetRBlockerL = []
connListRetRBlockerR = []
for y in range(0, self.dim_y):
connListRetLBlockerL.append(
(y, y, self.cell_params['synaptic']['wSaB'],
self.cell_params['synaptic']['dSaB']))
connListRetLBlockerR.append(
(y, y + self.dim_y, self.cell_params['synaptic']['wSzB'],
self.cell_params['synaptic']['dSzB']))
connListRetRBlockerL.append(
(y, y, self.cell_params['synaptic']['wSzB'],
self.cell_params['synaptic']['dSzB']))
connListRetRBlockerR.append(
(y, y + self.dim_y, self.cell_params['synaptic']['wSaB'],
self.cell_params['synaptic']['dSaB']))
retinaLeft = retinae['left'].pixel_columns
retinaRight = retinae['right'].pixel_columns
pixel = 0
for row in _retina_proj_l:
for pop in row:
ps.Projection(retinaLeft[pixel],
self.network[pop][1],
ps.OneToOneConnector(
weights=self.cell_params['synaptic']['wSC'],
delays=self.cell_params['synaptic']['dSC']),
target='excitatory')
ps.Projection(retinaLeft[pixel],
self.network[pop][0],
ps.FromListConnector(connListRetLBlockerL),
target='excitatory')
ps.Projection(retinaLeft[pixel],
self.network[pop][0],
ps.FromListConnector(connListRetLBlockerR),
target='inhibitory')
pixel += 1
pixel = 0
for col in _retina_proj_r:
for pop in col:
ps.Projection(retinaRight[pixel],
self.network[pop][1],
ps.OneToOneConnector(
weights=self.cell_params['synaptic']['wSC'],
delays=self.cell_params['synaptic']['dSC']),
target='excitatory')
ps.Projection(retinaRight[pixel],
self.network[pop][0],
ps.FromListConnector(connListRetRBlockerR),
target='excitatory')
ps.Projection(retinaRight[pixel],
self.network[pop][0],
ps.FromListConnector(connListRetRBlockerL),
target='inhibitory')
pixel += 1
# configure for the live input streaming if desired
if not (retinae['left'].use_prerecorded_input
and retinae['right'].use_prerecorded_input):
from spynnaker_external_devices_plugin.pyNN.connections.spynnaker_live_spikes_connection import \
SpynnakerLiveSpikesConnection
all_retina_labels = retinaLeft.labels + retinaRight.labels
self.live_connection_sender = SpynnakerLiveSpikesConnection(
receive_labels=None,
local_port=19999,
send_labels=all_retina_labels)
# this callback will be executed right after simulation.run() has been called. If simply a while True
# is put there, the main thread will stuck there and will not complete the simulation.
# One solution might be to start a thread/process which runs a "while is_running:" loop unless the main thread
# sets the "is_running" to False.
self.live_connection_sender.add_start_callback(
all_retina_labels[0], self.start_injecting)
import DVSReader as dvs
# the port numbers might well be wrong
self.dvs_stream_left = dvs.DVSReader(
port=0,
label=retinaLeft.label,
live_connection=self.live_connection_sender)
self.dvs_stream_right = dvs.DVSReader(
port=1,
label=retinaRight.label,
live_connection=self.live_connection_sender)
# start the threads, i.e. start reading from the DVS. However, nothing will be sent to the SNN.
# See start_injecting
self.dvs_stream_left.start()
self.dvs_stream_right.start()
def _interconnect_neurons_inhexc(self, network, verbose=False):
assert network is not None, \
"ERROR: Network is not initialised! Interconnecting for inhibitory and excitatory patterns failed."
if verbose and self.cell_params['topological']['radius_i'] < self.dim_x:
print "WARNING: Bad radius of inhibition. Uniquness constraint cannot be satisfied."
if verbose and 0 <= self.cell_params['topological'][
'radius_e'] > self.dim_x:
print "WARNING: Bad radius of excitation. "
# create lists with inhibitory along the Retina Right projective line
nbhoodInhL = []
nbhoodInhR = []
nbhoodExcX = []
nbhoodEcxY = []
# used for the triangular form of the matrix in order to remain within the square
if verbose:
print "INFO: Generating inhibitory and excitatory connectivity patterns."
# generate rows
limiter = self.max_disparity - self.min_disparity + 1
ensembleIndex = 0
while ensembleIndex < len(network):
if ensembleIndex / (self.max_disparity - self.min_disparity + 1) > \
(self.dim_x - self.min_disparity) - (self.max_disparity - self.min_disparity) - 1:
limiter -= 1
if limiter == 0:
break
nbhoodInhL.append(
[ensembleIndex + disp for disp in range(0, limiter)])
ensembleIndex += limiter
ensembleIndex = len(network)
# generate columns
nbhoodInhR = [[x] for x in nbhoodInhL[0]]
shiftGlob = 0
for x in nbhoodInhL[1:]:
shiftGlob += 1
shift = 0
for e in x:
if (shift + 1) % (self.max_disparity - self.min_disparity +
1) == 0:
nbhoodInhR.append([e])
else:
nbhoodInhR[shift + shiftGlob].append(e)
shift += 1
# generate all diagonals
for diag in map(None, *nbhoodInhL):
sublist = []
for elem in diag:
if elem is not None:
sublist.append(elem)
nbhoodExcX.append(sublist)
# generate all y-axis excitation
for x in range(0, self.dim_y):
for e in range(1, self.cell_params['topological']['radius_e'] + 1):
if x + e < self.dim_y:
nbhoodEcxY.append(
(x, x + e, self.cell_params['synaptic']['wCCe'],
self.cell_params['synaptic']['dCCe']))
if x - e >= 0:
nbhoodEcxY.append(
(x, x - e, self.cell_params['synaptic']['wCCe'],
self.cell_params['synaptic']['dCCe']))
# Store these lists as global parameters as they can be used to quickly match the spiking collector neuron
# with the corresponding pixel xy coordinates (same_disparity_indices)
# TODO: think of a better way to encode pixels: closed form formula would be perfect
# These are also used when connecting the spike sources to the network! (retina_proj_l, retina_proj_r)
global _retina_proj_l, _retina_proj_r, same_disparity_indices
_retina_proj_l = nbhoodInhL
_retina_proj_r = nbhoodInhR
same_disparity_indices = nbhoodExcX
if verbose:
print "INFO: Connecting neurons for internal excitation and inhibition."
for row in nbhoodInhL:
for pop in row:
for nb in row:
if nb != pop:
ps.Projection(
network[pop][1],
network[nb][1],
ps.OneToOneConnector(
weights=self.cell_params['synaptic']['wCCi'],
delays=self.cell_params['synaptic']['dCCi']),
target='inhibitory')
for col in nbhoodInhR:
for pop in col:
for nb in col:
if nb != pop:
ps.Projection(
network[pop][1],
network[nb][1],
ps.OneToOneConnector(
weights=self.cell_params['synaptic']['wCCi'],
delays=self.cell_params['synaptic']['dCCi']),
target='inhibitory')
for diag in nbhoodExcX:
for pop in diag:
for nb in range(
1, self.cell_params['topological']['radius_e'] + 1):
if diag.index(pop) + nb < len(diag):
ps.Projection(
network[pop][1],
network[diag[diag.index(pop) + nb]][1],
ps.OneToOneConnector(
weights=self.cell_params['synaptic']['wCCe'],
delays=self.cell_params['synaptic']['dCCe']),
target='excitatory')
if diag.index(pop) - nb >= 0:
ps.Projection(
network[pop][1],
network[diag[diag.index(pop) - nb]][1],
ps.OneToOneConnector(
weights=self.cell_params['synaptic']['wCCe'],
delays=self.cell_params['synaptic']['dCCe']),
target='excitatory')
for ensemble in network:
ps.Projection(ensemble[1],
ensemble[1],
ps.FromListConnector(nbhoodEcxY),
target='excitatory')
def start_sim(self,sim_time):
#simulation setup
self.simtime = sim_time
sim.setup(timestep=self.setup_cond["timestep"], min_delay=self.setup_cond["min_delay"], max_delay=self.setup_cond["max_delay"])
#initialise the neuron population
spikeArrayOn = {'spike_times': self.in_spike}
pre_pop = sim.Population(self.pre_pop_size, sim.SpikeSourceArray,
spikeArrayOn, label='inputSpikes_On')
post_pop= sim.Population(self.post_pop_size,sim.IF_curr_exp,
self.cell_params_lif, label='post_1')
stdp_model = sim.STDPMechanism(timing_dependence=sim.SpikePairRule(tau_plus= self.stdp_param["tau_plus"],
tau_minus= self.stdp_param["tau_minus"],
nearest=True),
weight_dependence=sim.MultiplicativeWeightDependence(w_min= self.stdp_param["w_min"],
w_max= self.stdp_param["w_max"],
A_plus= self.stdp_param["A_plus"],
A_minus= self.stdp_param["A_minus"]))
#initialise connectiviity of neurons
#exitatory connection between pre-synaptic and post-synaptic neuron population
if(self.inhibitory_spike_mode):
connectionsOn = sim.Projection(pre_pop, post_pop, sim.AllToAllConnector(weights = self.I_syn_weight,delays=1,
allow_self_connections=False),
target='inhibitory')
else:
if(self.STDP_mode):
if(self.allsameweight):
connectionsOn = sim.Projection(pre_pop, post_pop,
sim.AllToAllConnector(weights = self.E_syn_weight,delays=1),
synapse_dynamics=sim.SynapseDynamics(slow=stdp_model),target='excitatory')
else:
connectionsOn = sim.Projection(pre_pop, post_pop,
sim.FromListConnector(self.conn_list),
synapse_dynamics=sim.SynapseDynamics(slow=stdp_model),target='excitatory')
else:
if(self.allsameweight):
connectionsOn = sim.Projection(pre_pop, post_pop, sim.AllToAllConnector(weights = self.E_syn_weight,delays=1),
target='excitatory')
else:
connectionsOn = sim.Projection(pre_pop, post_pop, sim.FromListConnector(self.conn_list),
target='excitatory')
#sim.Projection.setWeights(self.E_syn_weight)
#inhibitory between the neurons post-synaptic neuron population
connection_I = sim.Projection(post_pop, post_pop, sim.AllToAllConnector(weights = self.I_syn_weight,delays=1,
allow_self_connections=False),
target='inhibitory')
pre_pop.record()
post_pop.record()
post_pop.record_v()
sim.run(self.simtime)
self.pre_spikes = pre_pop.getSpikes(compatible_output=True)
self.post_spikes = post_pop.getSpikes(compatible_output=True)
self.post_spikes_v = post_pop.get_v(compatible_output=True)
self.trained_weights = connectionsOn.getWeights(format='array')
sim.end()
#print self.conn_list
#print self.trained_weights
'''scipy.io.savemat('trained_weight.mat',{'initial_weight':self.init_weights,
'trained_weight':self.trained_weights
})'''
scipy.io.savemat('trained_weight0.mat',{'trained_weight':self.trained_weights
})
pop_forward = Frontend.Population(n_neurons, Frontend.IF_curr_exp,
cell_params_lif, label='pop_forward')
pop_backward = Frontend.Population(n_neurons, Frontend.IF_curr_exp,
cell_params_lif, label='pop_backward')
# create equiv of parrot popultions for closed loop calculation
pop_forward_parrot = Frontend.Population(
n_neurons, Frontend.IF_curr_exp, cell_params_lif, label='pop_forward_parrot')
pop_backward_parrot = Frontend.Population(
n_neurons, Frontend.IF_curr_exp, cell_params_lif, label='pop_backward_parrot')
# Create injection populations
injector_forward = Frontend.Population(
2, ExternalDevices.SpikeInjector,
{"port": 19344}, label='spike_injector_forward')
# Create a connection from the injector into the populations
Frontend.Projection(injector_forward, pop_forward,
Frontend.FromListConnector([(0, 0, weight_to_spike, 3)]))
Frontend.Projection(injector_forward, pop_backward,
Frontend.FromListConnector([(1, 99, weight_to_spike, 3)]))
# Synfire chain connections where each neuron is connected to its next neuron
# NOTE: there is no recurrent connection so that each chain stops once it
# reaches the end
loop_forward = list()
loop_backward = list()
for i in range(0, n_neurons - 1):
loop_forward.append((i, (i + 1) % n_neurons, weight_to_spike, 3))
loop_backward.append(((i + 1) % n_neurons, i, weight_to_spike, 3))
Frontend.Projection(pop_forward, pop_forward,
Frontend.FromListConnector(loop_forward))
Frontend.Projection(pop_backward, pop_backward,
Frontend.FromListConnector(loop_backward))
# Add links to the parrot populations for closed loop calculations
singleConnection = (i, ((i + 1) % nNeurons), weight_to_spike, delay)
connections.append(singleConnection)
injectionConnection = [(0, 0, weight_to_spike, delay)]
spikeArray = {'spike_times': [[0]]}
populations.append(
p.Population(nNeurons, p.IF_curr_exp, cell_params_lif, label='pop_1'))
#populations.append(p.Population(nNeurons, p.IF_curr_exp, cell_params_lif, label='pop_2'))
populations.append(
p.Population(1, p.SpikeSourceArray, spikeArray, label='inputSpikes_1'))
#populations[0].set_mapping_constraint({"x": 1, "y": 0})
projections.append(
p.Projection(populations[0], populations[0],
p.FromListConnector(connections)))
projections.append(
p.Projection(populations[1], populations[0],
p.FromListConnector(injectionConnection)))
populations[0].record_v()
populations[0].record_gsyn()
populations[0].record()
p.run(1000)
v = None
gsyn = None
spikes = None
''''
weights = projections[0].getWeights()
'v_rest': -65.0,
'v_thresh': -50.0
}
# create synfire populations (if cur exp)
pop_forward = Frontend.Population(n_neurons,
Frontend.IF_curr_exp,
cell_params_lif,
label='pop_forward')
# Create injection populations
injector_forward = Frontend.Population(1,
Frontend.SpikeSourceArray,
{'spike_times': [[0]]},
label='spike_playback_forward')
# Create a connection from the injector into the populations
Frontend.Projection(injector_forward, pop_forward,
Frontend.FromListConnector([(0, 0, weight_to_spike, 1)]))
# Synfire chain connections where each neuron is connected to its next neuron
# NOTE: there is no recurrent connection so that each chain stops once it
# reaches the end
loop_forward = list()
for i in range(0, n_neurons - 1):
loop_forward.append((i, (i + 1) % n_neurons, weight_to_spike, 3))
Frontend.Projection(pop_forward, pop_forward,
Frontend.FromListConnector(loop_forward))
# record spikes from the synfire chains so that we can read off valid results
# in a safe way afterwards, and verify the behavior
pop_forward.record()
# Activate the sending of live spikes
ExternalDevices.activate_live_output_for(pop_forward,
database_notify_host="localhost",
database_notify_port_num=19996)
# First it's necessary to find which POS neurons are EXCITATORY and which ones are INHIBITORY:
temp_exc_pos = (numpy.array([indices_pos_exc == i
for i in exc_neuron_idx])).sum(axis=0)
exc2exc = numpy.arange(
len(indices_pos_exc))[temp_exc_pos == 1] # excitatory ones
exc2inh = numpy.arange(
len(indices_pos_exc))[temp_exc_pos == 0] # inhibitory ones
# EXC2EXC
indices_pre_exc_e2e = [(numpy.abs(exc_neuron_idx - i)).argmin()
for i in indices_pre_exc[exc2exc]]
indices_pos_exc_e2e = [(numpy.abs(exc_neuron_idx - i)).argmin()
for i in indices_pos_exc[exc2exc]]
connections_exc2exc = sim.FromListConnector(conn_list=zip(
indices_pre_exc_e2e, indices_pos_exc_e2e, weights_pre_exc[exc2exc], [0.0] *
len(exc2exc)))
sim.Projection(pop_lsm_exc,
pop_lsm_exc,
connections_exc2exc,
label="EXC2EXC_conn",
target='excitatory')
# EXC2INH
indices_pre_exc_e2i = [(numpy.abs(inh_neuron_idx - i)).argmin()
for i in indices_pre_exc[exc2inh]]
indices_pos_exc_e2i = [(numpy.abs(inh_neuron_idx - i)).argmin()
for i in indices_pos_exc[exc2inh]]
connections_exc2inh = sim.FromListConnector(conn_list=zip(
indices_pre_exc_e2i, indices_pos_exc_e2i, weights_pre_exc[exc2inh], [0.0] *
for ii in range(0,len(argw)):
pylab.plot(argw[ii][3]+200*ii,argw[ii][0]+argw[ii][1]*16,".")
pylab.title('raster plot of Virtual Retina Neuron Population')
pylab.show()
raster_plot()
#Let us only use the ON events
TrianSpikeON = BuildTrainingSpike(order,1)
#print TrianSpikeON
spikeArrayOn = {'spike_times': TrianSpikeON}
ON_pop = sim.Population(prepop_size, sim.SpikeSourceArray, spikeArrayOn,
label='inputSpikes_On')
post_pop= sim.Population(postpop_size,sim.IF_curr_exp, cell_params_lif,
label='post_1')
connectionsOn = sim.Projection(ON_pop, post_pop, sim.FromListConnector(
convert_weights_to_list(weights_import, delay)))
#inhibitory between the neurons
connection_I = sim.Projection(post_pop, post_pop, sim.AllToAllConnector(
weights = 15,delays=1,allow_self_connections=False), target='inhibitory')
post_pop.record()
sim.run(simtime)
# == Get the Simulated Data =================================================
post_spikes = post_pop.getSpikes(compatible_output=True)
sim.end()
def GetFiringPattern(spike,low,high):
spikeT = np.transpose(spike)
time_stamp = spikeT[1]
spikeArray = {'spike_times': [[0]]}
for i in range(0, n_pops):
populations.append(
p.Population(nNeurons,
p.IF_curr_exp,
cell_params_lif,
label='pop_{}'.format(i)))
populations.append(
p.Population(1, p.SpikeSourceArray, spikeArray, label='inputSpikes_1'))
for i in range(0, n_pops):
projections.append(
p.Projection(populations[i], populations[i],
p.FromListConnector(connections)))
projections.append(
p.Projection(populations[i], populations[((i + 1) % n_pops)],
p.FromListConnector(pop_jump_connection)))
projections.append(
p.Projection(populations[n_pops], populations[0],
p.FromListConnector(injectionConnection)))
#populations[0].record_v()
#populations[0].record_gsyn()
for pop_index in range(0, n_pops):
populations[pop_index].record()
p.run(25000)
stdp_model = sim.STDPMechanism(
timing_dependence=sim.SpikePairRule(tau_plus=50.0, tau_minus=50.0),
weight_dependence=sim.AdditiveWeightDependence(
w_min=0.0,
w_max=instant_spike_weight,
A_plus=0.000001,
A_minus=1.0),
dendritic_delay_fraction=1.0)
synapse_dynamics = sim.SynapseDynamics(slow=stdp_model)
else:
synapse_dynamics = None
# Create connector
proj = sim.Projection(neurons,
neurons,
sim.FromListConnector(conn_list),
synapse_dynamics=synapse_dynamics,
target="excitatory")
# Stimulate stim neuron
stim = sim.Population(1,
sim.SpikeSourceArray, {"spike_times": [2.0]},
label="stim")
sim.Projection(
stim, neurons,
sim.FromListConnector([
(0, get_neuron_index(stim_x, stim_y,
cost_image.shape[0]), instant_spike_weight, 1.0)
]))
# Run network
