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one_layer_learn---one_layer_output.py
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one_layer_learn---one_layer_output.py
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#!/usr/bin/python
import pyNN.spiNNaker as sim
import spynnaker_external_devices_plugin.pyNN as ExternalDevices
from spynnaker_external_devices_plugin.pyNN.connections\
.spynnaker_live_spikes_connection import SpynnakerLiveSpikesConnection
from pyNN.random import RandomDistribution
from pyNN.space import Grid2D, Grid3D, Cuboid, BaseStructure
import pylab
import numpy
from numpy import where, exp, power, sqrt
import time
import os
import glob
import cPickle as pickle
from threading import Condition
from generate_populations import *
from generate_connections import *
from spike_adjustments import *
def plot_spikearray(spike_array):
x = []
y = []
for neuron_id in xrange(len(spike_array)):
for spike_time in spike_array[neuron_id]:
y.append(neuron_id)
x.append(spike_time)
pylab.scatter(x, y, s=0.5)
def send_input(label, sender):
ms_count = 0
spikes_per_ms = 5
neurons = []
spike_count = 0
curr_time = 0
prev_time = 0
ms = 1./1000.
for n in xrange(num_samples):
num_focal_spikes = len(focal_spikes[n]['spk'])
max_spikes = int(num_focal_spikes*0.3)
for rep in xrange(num_periods):
prev_time = time.clock()
for spike_idx in xrange(max_spikes):
neuron_id, coeff, layer = focal_spikes[n]['spk'][spike_idx]
if layer < 3:# == in_id:
neurons.append(neuron_id)
spike_count += 1
if spike_count == spikes_per_ms:
print_condition.acquire()
print "Sending spikes", neurons, "sample ", n
print_condition.release()
sender.send_spikes(label, neurons)
ms_count += 1
spike_count = 0
neurons[:] = []
curr_time = time.clock()
time_diff = curr_time - prev_time
prev_time = curr_time
if time_diff < ms:
time.sleep(ms - time_diff)
print "repeat! ", rep
time.sleep(period_length/1000.)
time.sleep(period_length/1000.)
#######################################################################
print_condition = Condition()
#~ pars=[0.02 0.2 -65 6 14 ;... % tonic spiking
#~ 0.02 0.25 -65 6 0.5 ;... % phasic spiking
#~ 0.02 0.2 -50 2 15 ;... % tonic bursting
#~ 0.02 0.25 -55 0.05 0.6 ;... % phasic bursting
#~ 0.02 0.2 -55 4 10 ;... % mixed mode
#~ 0.01 0.2 -65 8 30 ;... % spike frequency adaptation
#~ 0.02 -0.1 -55 6 0 ;... % Class 1
#~ 0.2 0.26 -65 0 0 ;... % Class 2
#~ 0.02 0.2 -65 6 7 ;... % spike latency
#~ 0.05 0.26 -60 0 0 ;... % subthreshold oscillations
#~ 0.1 0.26 -60 -1 0 ;... % resonator
#~ 0.02 -0.1 -55 6 0 ;... % integrator
#~ 0.03 0.25 -60 4 0;... % rebound spike
#~ 0.03 0.25 -52 0 0;... % rebound burst
#~ 0.03 0.25 -60 4 0 ;... % threshold variability
#~ 1 1.5 -60 0 -65 ;... % bistability
#~ 1 0.2 -60 -21 0 ;... % DAP
#~ 0.02 1 -55 4 0 ;... % accomodation
#~ -0.02 -1 -60 8 80 ;... % inhibition-induced spiking
#~ -0.026 -1 -45 0 80]; % inhibition-induced bursting
izk_types = {'RS': {'a': 0.02, 'b': 0.2, 'c': -65., 'd': 8., 'I': 0},
'IB': {'a': 0.02, 'b': 0.2, 'c': -55., 'd': 4., 'I': 0},
'CH': {'a': 0.02, 'b': 0.2, 'c': -50., 'd': 2., 'I': 0},
'FS': {'a': 0.1, 'b': 0.2, 'c': -65., 'd': 2., 'I': 0},
'TC': {'a': 0.02, 'b': 0.25, 'c': -65., 'd': 0.05, 'I': 0},
'RZ': {'a': 0.1, 'b': 0.26, 'c': -60., 'd': -1., 'I': 0},
'LTS': {'a': 0.02, 'b': 0.25, 'c': -65., 'd': 2., 'I': 0},
###############################
'TS': {'a': 0.02, 'b': 0.2, 'c': -65., 'd': 6., 'I': 0},
'PS': {'a': 0.02, 'b': 0.25, 'c': -65., 'd': 6., 'I': 0},
'TB': {'a': 0.02, 'b': 0.2, 'c': -50., 'd': 2., 'I': 0},
'PB': {'a': 0.02, 'b': 0.25, 'c': -55., 'd': 0.05, 'I': 0},
'MM': {'a': 0.02, 'b': 0.2, 'c': -55., 'd': 4., 'I': 0},
'SFA': {'a': 0.01, 'b': 0.2, 'c': -65., 'd': 8., 'I': 0},
'C1': {'a': 0.02, 'b': -0.1, 'c': -55., 'd': 6., 'I': 0},
'C2': {'a': 0.2, 'b': 0.26, 'c': -65., 'd': 0., 'I': 0},
'SL': {'a': 0.02, 'b': 0.2, 'c': -65., 'd': 6., 'I': 0},
'STO': {'a': 0.05, 'b': 0.26, 'c': -60., 'd': 0., 'I': 0},
#RZ
'INT': {'a': 0.02, 'b': -0.1, 'c': -55., 'd': 6., 'I': 0},
'RBS': {'a': 0.03, 'b': 0.25, 'c': -60., 'd': 4., 'I': 0},
'RBB': {'a': 0.03, 'b': 0.25, 'c': -52., 'd': 0., 'I': 0},
'TV': {'a': 0.03, 'b': 0.25, 'c': -60., 'd': 4., 'I': 0},
'BI': {'a': 1., 'b': 1.5, 'c': -60., 'd': 4., 'I': 0},
'DAP': {'a': 1., 'b': 0.2, 'c': -60., 'd': -21., 'I': 0},
'ACC': {'a': 0.02, 'b': 1., 'c': -55., 'd': 4., 'I': 0},
'ISS': {'a': -0.02, 'b': -1, 'c': -60., 'd': 8., 'I': 80},
'ISB': {'a': -0.026, 'b': -1, 'c': -45., 'd': 0., 'I': 80},
}
in_id = 1
new_conn_list = True
max_delay = 32.
min_delay = 1.
timestep = 1.
sim_runtime = 10000.
exc_conn_probability = 0.2
inh_conn_probability = 0.3
period_length = 200 #ms
num_periods = 4
img_width = 28
img_height = 28
img_neurons = img_width*img_height
cube_z = 1.5
num_neurons = img_neurons*cube_z*3.
num_samples = 1
focal_spikes = {}
focal_path = "../../images-to-spikes/focal/mnist_spikes/focal/"
for n in xrange(num_samples):
filename = "focal_corrected_MNIST-original-train-%d-?.pickle"%(n+1)
filename = os.path.join(focal_path, filename)
print filename
for f in glob.glob(filename):
print("file:\t%s"%f)
print("reading!\n")
focal_spikes[n] = pickle.load(open(f))
celltype = sim.IZK_curr_exp
start_v = -70.
out_cell_type = 'RS'
out_start_v = start_v
out_params_izk_out = {'a': izk_types[out_cell_type]['a'],
'b': izk_types[out_cell_type]['b'],
'c': izk_types[out_cell_type]['c'],
'd': izk_types[out_cell_type]['d'],
'v_init': izk_types[out_cell_type]['c'],
'u_init': izk_types[out_cell_type]['b']*izk_types[out_cell_type]['c'],
'i_offset': izk_types[out_cell_type]['I'],
#'tau_syn_E': 2,
#'tau_syn_I': 2,
}
exc_cell_type = 'RS'
exc_start_v = start_v
cell_params_izk_exc = {'a': izk_types[exc_cell_type]['a'],
'b': izk_types[exc_cell_type]['b'],
'c': izk_types[exc_cell_type]['c'],
'd': izk_types[exc_cell_type]['d'],
'v_init': izk_types[exc_cell_type]['c'],
'u_init': izk_types[exc_cell_type]['b']*izk_types[exc_cell_type]['c'],
'i_offset': izk_types[exc_cell_type]['I'],
#'tau_syn_E': 2,
#'tau_syn_I': 2,
}
#~ num_exc = num_neurons
inh_cell_type = 'FS'
inh_start_v = start_v
cell_params_izk_inh = {'a': izk_types[inh_cell_type]['a'],
'b': izk_types[inh_cell_type]['b'],
'c': izk_types[inh_cell_type]['c'],
'd': izk_types[inh_cell_type]['d'],
'v_init': izk_types[inh_cell_type]['c'],
'u_init': izk_types[inh_cell_type]['b']*izk_types[inh_cell_type]['b'],
'i_offset': izk_types[inh_cell_type]['I'],
#'tau_syn_E': 2,
#'tau_syn_I': 2,
}
inv_cell_type = 'ISS'
inv_start_v = start_v
cell_params_izk_inv = {'a': izk_types[inv_cell_type]['a'],
'b': izk_types[inv_cell_type]['b'],
'c': izk_types[inv_cell_type]['c'],
'd': izk_types[inv_cell_type]['d'],
'v_init': izk_types[inv_cell_type]['c'],
'u_init': izk_types[inv_cell_type]['b']*izk_types[inv_cell_type]['c'],
'i_offset': izk_types[inv_cell_type]['I'],
#'tau_syn_E': 2,
#'tau_syn_I': 2,
}
inj_cell_params = {
'port': 12345,
}
inj_cell_type = ExternalDevices.SpikeInjector
#~ num_inh = int(num_neurons*(1./4.))
rngseed = int(time.time())
#rngseed = 1
rng = sim.NumpyRNG(seed=rngseed)
neuron_model = sim.IZK_curr_exp
sim.set_number_of_neurons_per_core(neuron_model, 128)
sim.setup(timestep=timestep, min_delay = min_delay, max_delay = max_delay)
############ stdp
stdp_model = sim.STDPMechanism(
timing_dependence=sim.SpikePairRule(tau_plus=20., tau_minus=20.0,
nearest=True),
weight_dependence=sim.AdditiveWeightDependence(w_min=0.01, w_max=6.,
A_plus=0.02, A_minus=0.02)
)
############ random dist objects
exc_delay_dist = RandomDistribution(distribution='uniform',
parameters=[0,20.], rng=rng)
inh_delay_dist = RandomDistribution(distribution='uniform',
parameters=[0,10.], rng=rng)
exc_weight_dist = RandomDistribution(distribution='uniform',
parameters=[0,1.], rng=rng)
inh_weight_dist = RandomDistribution(distribution='uniform',
parameters=[0,3.], rng=rng)
normal_distribution = RandomDistribution('normal', [0., 1.], rng=rng)
############ populations
pop_2d = grid2d_coords(img_neurons)
pop_3d = rand_pop_3d_coords(num_neurons,
space_width=img_width*1.1,
space_height=img_height*1.1,
space_depth=cube_z)
num_inh = int(num_neurons*0.2)
num_exc = int(num_neurons*0.8)
inh_3d = pop_3d[:num_inh]
exc_3d = pop_3d[num_inh:]
#~ inv_3d = rand_pop_3d_coords(img_neurons,
#~ space_width=img_width*1.1,
#~ space_height=img_height*1.1,
#~ space_depth=1.)
#~ inv_3d[:,2] -= 0.1 #translate back 0.1 units
min_z = (pop_3d[:,2].min() - 0.2)*numpy.ones((pop_2d.shape[0], 1))
pop_2d_3d = hstack((pop_2d, min_z))
input_spikes = {}
for n in xrange(num_samples):
input_spikes[n] = focal_to_spike_source_array(focal_spikes[n],
img_width, img_height)
repeat_after(input_spikes[n], period_length, num_periods)
full_input_spikes = input_spikes[0][in_id][:]
for n in xrange(1, num_samples):
add_spikes(full_input_spikes, input_spikes[n][in_id], silence=period_length*2)
input_layer = {}
#~ for i in xrange(len(input_spikes)):
#~ input_layer[i] = sim.Population(img_neurons, sim.SpikeSourceArray,
#~ {"spike_times": input_spikes[i]},
#~ structure=grid)
input_layer[in_id] = sim.Population(img_neurons, inj_cell_type,
inj_cell_params,
label="spike_injector")
live_spikes_connection_send = SpynnakerLiveSpikesConnection(
receive_labels=None, local_port=19999,
send_labels=["spike_injector"])
live_spikes_connection_send.add_start_callback("spike_injector", send_input)
#~ input_layer[in_id] = sim.Population(img_neurons, sim.SpikeSourceArray,
#~ {"spike_times": input_spikes[in_id]}
#~ )
learn_layer = {}
learn_layer['exc'] = sim.Population(num_exc+1, neuron_model, cell_params_izk_exc,
label="Learn layer - exc")
learn_layer['inh'] = sim.Population(num_inh+1, neuron_model, cell_params_izk_inh,
label="Learn layer - inh")
#~ learn_layer['inv'] = sim.Population(img_neurons, neuron_model, cell_params_izk_inv,
#~ label="Learn layer - inv")
id_layer = sim.Population(num_exc+1, neuron_model, out_params_izk_out,
label="Output layer - exc")
learn_layer['exc'].record()
learn_layer['inh'].record()
#~ learn_layer['inv'].record()
id_layer.record()
print("\n\n")
print("Input neurons: %s"%img_neurons)
print("Excitatory neurons: %s"%num_exc)
print("Inhibitory neurons: %s"%num_inh)
print("Invert neurons: %s"%img_neurons)
print("Identification neurons: %s"%(num_exc+1))
print("----------------------------")
print("Total neurons: %s"%(2*img_neurons+num_exc*2+num_inh+1))
############ connectors
input_strength = 3.# 1.8
in2exc = dist_dep_conn_3d_3d(pop_2d_3d, exc_3d, dist_rule = "exp(-(d**2))",
conn_prob = 0.5,
weight=input_strength,
new_format=new_conn_list)
in2inh = dist_dep_conn_3d_3d(pop_2d_3d, inh_3d, dist_rule = "exp(-(d**2))",
conn_prob = 0.5,
weight=input_strength,
new_format=new_conn_list)
#~ in2inv = dist_dep_conn_3d_3d(pop_2d_3d, inv_3d, dist_rule = "exp(-(d**2))",
#~ conn_prob = 0.2,
#~ weight=input_strength,
#~ new_format=new_conn_list)
inh2exc = dist_dep_conn_3d_3d(inh_3d, exc_3d, dist_rule = "exp(-(d**2))",
conn_prob = 0.1,
weight=input_strength,
new_format=new_conn_list)
inh2inh = dist_dep_conn_3d_3d(inh_3d, inh_3d, dist_rule = "exp(-(d**2))",
conn_prob = 0.1,
weight=input_strength,
new_format=new_conn_list)
exc2exc = dist_dep_conn_3d_3d(exc_3d, exc_3d, dist_rule = "exp(-sqrt(d*d))",
conn_prob = 0.2,
weight=input_strength,
new_format=new_conn_list)
exc2inh = dist_dep_conn_3d_3d(exc_3d, inh_3d, dist_rule = "exp(-sqrt(d*d))",
conn_prob = 0.2,
weight=input_strength,
new_format=new_conn_list)
connectors = {}
connectors['in2exc'] = sim.FromListConnector(in2exc)
connectors['in2inh'] = sim.FromListConnector(in2inh)
#connectors['in2inv'] = sim.FromListConnector(in2inv)
connectors['inh2exc'] = sim.FromListConnector(inh2exc)
connectors['inh2inh'] = sim.FromListConnector(inh2inh)
connectors['exc2inh'] = sim.FromListConnector(exc2inh)
connectors['exc2exc'] = sim.FromListConnector(exc2exc)
connectors['exc2id'] = sim.AllToAllConnector(allow_self_connections=False,
weights=normal_distribution,
delays=exc_delay_dist)
#~
#~ connectors['exc2id'] = sim.FixedProbabilityConnector(0.2, allow_self_connections=False,
#~ weights=normal_distribution,
#~ delays=exc_delay_dist
#~ )
#~
#~
#~
#~ connectors['id2id'] = sim.AllToAllConnector(allow_self_connections=False,
#~ weights=normal_distribution,
#~ # delays=inh_delay_dist
#~ )
#~
#~ connectors['id2id'] = sim.FixedProbabilityConnector(0.2, allow_self_connections=False,
#~ weights=normal_distribution,
#~ delays=exc_delay_dist
#~ )
############ connections
connections = {}
#~ for k in input_layer:
#~ connections['in2exc_%s'%k] = sim.Projection(input_layer[k], learn_layer['exc'],
#~ prob_conn['in'], stdp_model,
#~ target='excitatory')
#~
#~ connections['in2inh_%s'%k] = sim.Projection(input_layer[k], learn_layer['inh'],
#~ prob_conn['in'], stdp_model,
#~ target='excitatory')
connections['in2exc'] = sim.Projection(input_layer[in_id], learn_layer['exc'],
connectors['in2exc'], stdp_model,
target='excitatory')
connections['in2inh'] = sim.Projection(input_layer[in_id], learn_layer['inh'],
connectors['in2inh'], stdp_model,
target='excitatory')
#~ connections['in2inv'] = sim.Projection(input_layer[in_id], learn_layer['inh'],
#~ connectors['in2inv'], stdp_model,
#~ target='excitatory')
connections['exc2exc'] = sim.Projection(learn_layer['exc'], learn_layer['exc'],
connectors['exc2exc'], stdp_model,
target='excitatory')
connections['exc2inh'] = sim.Projection(learn_layer['exc'], learn_layer['inh'],
connectors['exc2inh'], stdp_model,
target='excitatory')
connections['inh2inh'] = sim.Projection(learn_layer['inh'], learn_layer['inh'],
connectors['inh2inh'], stdp_model,
target='inhibitory')
connections['inh2exc'] = sim.Projection(learn_layer['inh'], learn_layer['exc'],
connectors['inh2exc'], stdp_model,
target='inhibitory')
connections['exc2id'] = sim.Projection(learn_layer['exc'], id_layer,
connectors['exc2id'], stdp_model,
target='excitatory')
#~ connections['id2id'] = sim.Projection(id_layer, id_layer,
#~ connectors['id2id'],
#~ target='inhibitory')
#### changed spynnaker/pyNN/models/pynn_projection.py
#~ def __len__(self):
#~ """Return the total number of local connections."""
#~ rows = self._host_based_synapse_list.get_rows()
#~ count = 0
#~ for pre_atom in xrange(len(rows)):
#~ count += len(rows[pre_atom].target_indices)
#~ return count
#~ # raise NotImplementedError
print("\n\n")
total_connections = 0
for k in sorted(connections.keys()):
num_conn = len(connections[k])
print( "%s\t\tconnections: %s"%(k, num_conn) )
total_connections += num_conn
print("-------------------------------")
print("Total connections: %s"%(total_connections))
print("\n\n")
time.sleep(1)
############ start sim
sim.run(sim_runtime)
############ get data out
any_spikes_recorded = True
sim_spikes = {}
try:
#~ sim_spikes['in'] = {}
#~ for l in input_layer:
#~ sim_spikes['in'][l] = input_layer[l].getSpikes(compatible_output=True)
sim_spikes['id'] = id_layer.getSpikes(compatible_output=True)
sim_spikes['exc'] = learn_layer['exc'].getSpikes(compatible_output=True)
sim_spikes['inh'] = learn_layer['inh'].getSpikes(compatible_output=True)
except IndexError:
print("No spikes?")
any_spikes_recorded = False
############ plot
if any_spikes_recorded:
symbols = {'id': 's', 'exc': '.', 'inh': 'x', 'in': '+'}
colours = {'id': 'blue', 'exc': 'green', 'inh': 'red', 'in': 'black'}
titles = {'id': 'Identification layer', 'exc': 'Excitatory neurons',
'inh': 'Inhibitory neurons', 'in': 'Input layer'}
layer = 0
#fig = pylab.figure()
spike_times = []
spike_ids = []
wrap_spikes = []
fig = pylab.figure()
for k in sim_spikes:
spike_times[:] = []
spike_ids[:] = []
ax = pylab.subplot(2,2, layer)
ax.set_title(titles[k])
spike_times = [spike_time for (neuron_id, spike_time) in sim_spikes[k]]
spike_ids = [neuron_id for (neuron_id, spike_time) in sim_spikes[k]]
pylab.plot(spike_times, spike_ids, symbols[k], markerfacecolor="None",
markeredgecolor=colours[k], markersize=2)
layer += 1
ax = pylab.subplot(2,2, 3)
ax.set_title(titles["in"])
plot_spikearray(full_input_spikes)
############ finish sim
pylab.show()
sim.end()
#~ pickle.dump( sim_spikes, open( "all-spikes.p", "wb" ) )