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SpiNNakerManchester / sPyNNaker

The SpiNNaker implementation of the PyNN neural networking language
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Limited numbers of spikes being transmitted by a SpikeSourceArray #157

Closed AlexRast closed 8 years ago

AlexRast commented 9 years ago

With the script and data seen below the SpikeSourceArray populations input_pol_1 and input_pol_2 are transmitting spikes only for the first 1000 ms. The script below also generates debug files of the inputs to the arrays so it can be verified that it is indeed generating input for times beyond 1000 ms. However these do not seem to be reaching anywhere else in the simulation. All spikes stop after 1000 ms. The script does not issue an error; it completes successfully

I note that this appears to be related to the number of total spikes in the input because if I specify that horiz_left spikes for the entire time of the run (by having a single tuple in the first entry of stimulus_onset_offsets: (0, 5000)) then spikes stop after 800 ms.

Happy to send the complete file set if/when you need.

!/usr/bin/python

""" Visual selection model Francesco Galluppi, Kevin Brohan

Modified 2013 ADR. WTA sharpened (bug-fixed?), added plasticity (optional) between V2 and V4, added PFC preferred and aversive stimulus capability with tunable parameters

Scaling module added January 2014 ADR to auto-scale dependent network parameters with the size of the input field.

Enhanced June 2014 ADR. Modified Gaussian creation to permit specification of gains and eccentricities in the Gaussian filter (also available for Gabor filtering). Tuned weights, gain, eccentricity for sample visual input. Added optional features that:

1) Allow the PFC to provide bipolar (excitatory/inhibitory) reinforcement 2) Implement top-down feedback as per Bernabe Linares-Barranco's suggestions 3) Add additional PFC priming to V1 layer 4) Provide a new output module for better visualisation of LIP output

FEF style PFC added September 2014 ADR. The "active_pfc" option instantiates a module which makes PFC output dependent upon V2 activity, modelling the FEF in biology.

This is the scalable PyNN only version of the model and is designed to work with real input from iCub cameras using Yarp for comms

""" USE_CABINET_SYSTEM = False

if USE_CABINET_SYSTEM: import site site.addsitedir('/home/P06/rasta/cabinet-packages')

import sys import itertools import re import copy import visual_network_scaling import visual_metrics

import numpy import matplotlib.pyplot as plt import matplotlib.cm as cmap from matplotlib.colors import ListedColormap

from gaussiancreatejose import from vector_topographic_activity_plot import * #mapped_boxplot from spike_file_to_spike_array import \ # convert_file_to_spikes

from pyNN.brian import * # use if running brian

from pyNN.random import * # add support for RNG from native PyNN from pyNN.spiNNaker_arbitrary import * # Imports the pyNN.spiNNaker module

from pyNN.random import NumpyRNG, RandomDistribution

from pyNN.utility import Timer

from operator import itemgetter

time_step = 1.0

Simulation Setup

setup(timestep=time_step, min_delay = 1.0, max_delay = 11.0, db_name='vis_attn_iCub.sqlite')

layer_to_observe = 'input_pol_1' lip_map_on = True plasticity_on = False feedback_on = True aversive_inhibitory = True top_down_priming = False metrics_on = False vector_plot = True active_pfc = True # PFC as FEF multistage_pfc = False # switchable PFC. Should have more biologically relevant name. output_to_file = False randomise_spike_times = True compatible_output = True # added parameter for getSpikes order. True = (id, time)

preferred_orientation = 0 # ADR added preferred and aversive orientations. These aversive_orientation = 2 # will be used to set biassing in LIP base_num_neurons = 64 #128 weight_prescale_factor = 1 # prescales weights and capacitances to account for system limits subsample_step = 1 # number of milliseconds to take average spike counts for spike subsampling random_spike_jitter = 3 # +- timing on any given spike

input_file_type = 1 # Input from: 0 - a simple list of ids; 1 - a single full list of spikes and times; 2 - a pair of polarities with spikes and times metrics_with_input = 0 # Get metrics from 1 - the input file; 0 - a separate file input_file_base_names = ['./TestData/horiz_left', './TestData/vert_right'] # base name of the input file(s) stimulus_onset_offsets = [[(0, 500), (1000, 2000), (2500, 3000), (4000, 5000)], [(500, 1000), (2000, 2500), (3500, 4500)]]

stimulus_onset_offsets = [[(0, 5000)], [(500, 1000)]]

compatible_input = True # whether input has (time, spike) order or reverse. True = (time, spike) virtual_velocity = (3, math.pi/2) # virtual velocity (displacement, angle) to use when computing shadow distance

if input_filetype == 2: InputFilesPol1 = ['%s%dx%d_pol1.dat' % (base_name, base_num_neurons, base_num_neurons) for base_name in input_file_basenames] InputFilesPol2 = ['%s%dx%d_pol2.dat' % (base_name, base_num_neurons, base_num_neurons) for base_name in input_file_basenames] else: InputFilesPol1 = ['%s%dx%d' % (base_name, base_num_neurons, base_num_neurons) for base_name in input_file_base_names]

if metrics_on: if metrics_with_input: MetricFile = InputFilePol1 else: MetricFile = 'objectannots%dx%d' % (tuple([base_num_neurons]*2))

neuronSim = True

loopT = True

scale = visual_network_scaling.scale_factor(dimensions=2, dim_sizes=None, base_dim_size=base_num_neurons, subsample_factor_i_1=1.6, subsample_factor_1_2=1.0, subsample_factor_2_4=2.0, subsample_factor_4_L=1.0, subsample_factor_4_P=1.0, base_filter_size=5.0, sweep_filter=False, filter_scale=(1,1,1), xy_kernel_quant_cutoff=(0,0), base_v2_subfield_size=2, sweep_subfield=False, subfield_scale=(1,1,1), active_P=active_pfc) scale.setindex(f_idx=0, s_idx=0) # base_filter_size=5.0

Create some arrays for neuron ID to x-y coord mappings

for doing nice 2D plotting

Normally I'd do this with PyNN spatial structure functionality

but not sure if it's available in PyNN.SpiNNaker

input_x_array = numpy.zeros((int(scale.input_size[0]_scale.input_size[1])),int) input_y_array = numpy.zeros((int(scale.input_size[0]_scale.input_size[1])),int)

startx = 0 starty = 0

for nrn in xrange(scale.input_size[0]*scale.input_size[1]): input_x_array[nrn] = startx input_y_array[nrn] = starty if startx == (scale.input_size[0] - 1): startx = 0 starty = starty + 1 else: startx = startx + 1

lip_x_array = numpy.zeros((int(scale.lip_pop_size[0]_scale.lip_pop_size[1])),int) lip_y_array = numpy.zeros((int(scale.lip_pop_size[0]_scale.lip_pop_size[1])),int)

startx = 0 starty = 0

for nrn in xrange(scale.lip_pop_size[0]*scale.lip_pop_size[1]): lip_x_array[nrn] = startx lip_y_array[nrn] = starty if startx == (scale.lip_pop_size[0] - 1): startx = 0 starty = starty + 1 else: startx = startx + 1

PARAMETERS gaussian

scales=2 # scales orientations=4 # orientations

sizeg1=5.0 # size of the gaussian filter

size_k1 = 5 # x-Size kernel (connections)

size_k2 = 5 # y-Size kernel (connections)

jump = 1 # overlapping

delays = 1.0 # connection delays v2_v4_delays = 2*delays if active_pfc else delays

PARAMETERS NETWORK

pfc_bias_pref = 0.825 if active_pfc else 1.0 #3.0 #4.0 # spinn is 4.0 # strong stimulation; should cause persistent spiking ADR pfc_bias_avert = 0.7 if active_pfc else 0.6 # 0.8 #0.0 # spinn is 0.0 # mild stimulation to non-aversive orientations;

biasses away from the aversive group. ADR

pfc_bias_neut = 0.5 if active_pfc else 0.5 #0.0 # spinn is 0.0 # very mild stimulation;

should just keep a population in random spiking ADR

feedback_weight_scaling = 0.81 #1.25 #0.9 input_gain = 1.0 #0.8 #0.5 #1.0 #0.4 # steepness of the Gaussian input filter. This varies parametrically with scales. Generally the more scales, the shallower, gaussian_eccentricity = 5.5 #4.6 #4.0 #8.0 # 2.25 # ratio of major/minor axis for the input filter. This varies parametrically with orientations. Generally the more orientations, the more eccentric input_strength = 10_weight_prescale_factor #2.25 #1.86 #4 #7 #4.1 #2.25 #24 #15 #9 #24 #20 # spinn is 20 # drive strength of the input->gaussian filters v1_v2_weights = 8_weight_prescale_factor #8 #15 #10.75 #15 #12 #15 # spinn is 15 # weights between v1 and v2 orientation maps (one2one connectors) wta_v2 = True # do the gaussian filter maps in v2 inhibit each other? wta_between_v2_weight = -1.85_weight_prescale_factor #-1 # spinn is -1 # inhibition weight between several orientations in v2 wta_within_v2_weight = -0.6_weight_prescale_factor #0.2 #-0.155 #-1 # spinn is -1 # inhibition weight within a single orientation in v2 weights_v4_lip = 22_weight_prescale_factor #22 #35 #25 #5 # spinn is 3 wta_lip_weight = -7_weight_prescale_factor #-20 #-10 # spinn is -7 # competition in LIP pfc_pre_weights = 1_weight_prescale_factor # pfc_pre->pfc gating weights. These multiply the bias. pfc_v1_weights = 0.0725_weight_prescale_factor # pfc->v1 competition biasing weights. Used if top-down priming is on. pfc_v4_weights = 7.25_weight_prescale_factor if active_pfc else 0.0725_weight_prescale_factor #0.0915 #0.6 #.6 # spinn is 0.1 # pfc->v4 competition biasing weights v2_pfc_weights = 48*weight_prescale_factor # base weight value for v1-pfc connections when using active PFC wta_bias = 1.3 #1.3 #1.5 # spinn is 1.3 # sets the relative strength of the other- (heterosynaptic) vs self- (homosynaptic) inhibitory connections for WTA wta_IOR_delay = 1 # scales the delay for IOR (self-inhibition)

set weight values for the critical V2->V4 connection. History of parameters:

3 AP 2.25 #4.5 FB #5.5 !FB #3 #6.79 #2.5 #2.9 #2.3 #7 # spinn is 3

if plasticity_on: weights_v2_v4 = 5_weight_prescale_factor elif active_pfc: if feedback_on: weights_v2_v4 = 3_weight_prescale_factor else: weights_v2_v4 = 3_weight_prescale_factor else: if feedback_on: weights_v2_v4 = 4.5_weight_prescale_factor else: weights_v2_v4 = 5.5*weight_prescale_factor

random objects and initialisations

rng = NumpyRNG(seed=28374) v_init_distr = RandomDistribution('uniform', [-55,-95], rng) v_rest_distr = RandomDistribution('uniform', [-55,-65], rng)

time parameters

runtime = 100

runtime = 500

runtime = 1000

runtime = 5000

runtime = 120000

runtime = 1000000

plot_time_window = True # Enable plot time windowing to reduce graph size/detail p_t_w = (0, 1000) # (min, max) times for plotting plot_t_max = p_t_w[1] if plot_time_window else runtime # plot max time plot_t_min = p_t_w[0] if plot_time_window else 0 # plot min time metric_window = 100 metric_start_offset = 0 metric_t_start = 0 metric_t_stop = runtime

Neural Parameters

tau_m = 24.0 # (ms) cm = 1 v_rest = -65 # (mV) v_thresh = -45 # (mV) v_reset = -65 # (mV) t_refrac = 3. # (ms) (clamped at v_reset) tau_syn_exc = 3 tau_syn_inh = tau_syn_exc*3 i_offset = 0

if multistage_pfc: tau_syn_exc_2 = 100 # long time contant creates an effective input integrator. i_bias_pref = 0 # with multistage areas input spike rate into stage 1 i_bias_avert = 0 # will set the PFC bias, not i_bias i_bias_neut = 0 pfc_pre_times = [None for i in range(orientations)] for i in range(orientations): # instantiate arrays for PFC activation. These could vary based upon orientation preference if i == preferred_orientation: pfc_pre_times[i] = [[t for t in range(runtime)] for n in range(scale.pfc_pop_size[0]_scale.pfc_pop_size[1])] elif i == aversive_orientation: pfc_pre_times[i] = [[t for t in range(runtime)] for n in range(scale.pfc_pop_size[0]_scale.pfc_pop_size[1])] else: pfc_pre_times[i] = [[t for t in range(runtime)] for n in range(scale.pfc_pop_size[0]*scale.pfc_pop_size[1])] else: i_bias_pref = pfc_bias_pref # with single PFC biasses are literal currents into the neuron ADR i_bias_avert = pfc_bias_avert #
i_bias_neut = pfc_bias_neut #

if plasticity_on: # set plasticity between v2 and v4, if desired

sets symmetric window, biassed slightly towards inhibition,

maximum weight is the must-fire weight

stdp_model = STDPMechanism( timing_dependence = SpikePairRule(tau_plus = 30.0, tau_minus = 30.0), weight_dependence = AdditiveWeightDependence(w_min = 0, w_max = 20, A_plus=0.005, A_minus = 0.006) # A_plus=0.5, A_minus=0.6 )

timer = Timer() timer.start()

cell_params will be passed to the constructor of the Population Object

cell_params = { 'tau_m' : tau_m, 'cm' : cm,
'v_rest' : v_rest, 'v_reset' : v_reset, 'v_thresh' : v_thresh, 'tau_syn_E' : tau_syn_exc, 'tau_syn_I' : tau_syn_inh, 'tau_refrac' : t_refrac, 'i_offset' : i_offset }

print "%g - Creating input population: %d x %d" % (timer.elapsedTime(), scale.input_size[0], scale.input_size[1]) data_input_1_components = [] for input_file_num in range(len(input_file_base_names)): input_file = open(InputFilesPol1[input_file_num], 'r') if not input_file_type: input_spike_list = eval(input_file.readline()) data_input_1 = convert_spike_list_to_timed_spikes(spike_list=input_spike_list, tmax=runtime, tstep=int(time_step)) sample_time = runtime else: data_input_1 = convert_file_to_spikes(input_file_name=InputFilesPol1[input_file_num], tmax=runtime, compatible_input=compatible_input) try: sample_time = int(re.search('^#\sruntime\s=\s(\d)\s', input_file.read(), flags=re.MULTILINE).group(1)) except AttributeError: sample_time = reduce(lambda x, y: max(x, numpy.fmax.reduce(y,0)), data_input_1.values(), 0) """ data_input_1 = convert_spike_list_to_timed_spikes(spike_list=input_spike_list, min_idx=0, max_idx=scale.input_size[0]_scale.input_size[1], tmin=0, tmax=runtime, tstep=int(time_step)) else: data_input_1 = convert_file_to_spikes(input_file_name=InputFilePol1, min_idx=0, max_idx=scale.input_size[0]_scale.input_size[1], tmin=0, tmax=runtime, compatible_input=compatible_input) """ input_file.close() data_input_1 = subsample_spikes_by_time(data_input_1, 0, runtime, subsample_step)

if sample_time < runtime:
   data_input_1 = loop_array(input_array=data_input_1, runtime=runtime, sampletime=sample_time)
if randomise_spike_times:
   data_input_1 = random_skew_times(data_input_1, random_spike_jitter)
data_input_1_components.append(data_input_1)

data_input_1 = splice_arrays(input_arrays=data_input_1_components, input_times=stimulus_onset_offsets) input_1_times = [data_input_1[neuron] if neuron in data_input_1 else [] for neuron in range(scale.input_size[0]*scale.input_size[1])]

input_pol_1_debug = open('input_pol_1.txt', 'w+') numpy.set_printoptions(threshold=1000000) input_pol_1_debug.write("input for polarity 1: %s" % input_1_times) input_pol_1_debug.close()

input_pol_1 = Population(scale.input_size[0]*scale.input_size[1], # size SpikeSourceArray, # Neuron Type {'spike_times': input_1_times}, # Neuron Parameters label="input_pol_1") # Label if layer_to_observe == 'input_pol_1' or layer_to_observe == 'all': print "%g - observing input (positive polarity)" % timer.elapsedTime()

input_pol_1.set_mapping_constraint({'x':0, 'y':0})

input_pol_1.record() # ('spikes', to_file=False)

data_input_2_components = [] for input_file_num in range(len(input_file_base_names)): if input_file_type < 2: data_input_2 = generate_shadow_spikes(data_input_1_components[input_file_num], scale.input_size[0], scale.input_size[1], virtual_velocity) else: input_file = open(InputFilesPol2[input_file_num], 'r') data_input_2 = convert_file_to_spikes(input_file_name=InputFilesPol2[input_file_num], tmax=runtime, compatible_input=compatible_input) try: sample_time = int(re.search('^#\sruntime\s=\s(\d)\s', input_file.read(), flags=re.MULTILINE).group(1)) except AttributeError: sample_time = reduce(lambda x, y: max(x, numpy.fmax.reduce(y,0)), data_input_2.values(), 0)

data_input_2 = convert_file_to_spikes(input_file_name=InputFilePol2, min_idx=0, max_idx=scale.input_size[0]_scale.input_size[1], tmin=0, tmax=runtime, compatible_input=compatible_input)

   data_input_2 = subsample_spikes_by_time(data_input_2, 0, runtime, subsample_step)
   if sample_time < runtime:
      data_input_2 = loop_array(input_array=data_input_2, runtime=runtime, sampletime=sample_time)
input_file.close()
# for spikes originating in a file type less than 2 the randomisation 
# will be carried out twice. If this causes a problem, randomisation of 
# times for data_input_1 can be carried out outside the file number loop.
if randomise_spike_times:
   data_input_2 = random_skew_times(data_input_2, random_spike_jitter)
data_input_2_components.append(data_input_2)

data_input_2 = splice_arrays(input_arrays=data_input_2_components, input_times=stimulus_onset_offsets) input_2_times = [data_input_2[neuron] if neuron in data_input_2 else [] for neuron in range(scale.input_size[0]_scale.input_size[1])]

input_pol_2_debug = open('input_pol_2.txt', 'w+') numpy.set_printoptions(threshold=1000000) input_pol_2_debug.write("input for polarity 2: %s" % input_2_times) input_pol_2_debug.close()

input_pol_2 = Population(scale.input_size[0]*scale.input_size[1], # size SpikeSourceArray, # Neuron Type {'spike_times': input_2_times}, # Neuron Parameters label="input_pol_2") # Label if layer_to_observe == 'input_pol_2' or layer_to_observe == 'all': print "%g - observing input (negative polarity)" % timer.elapsedTime()

input_pol_2.set_mapping_constraint({'x':0, 'y':0})

input_pol_2.record() # ('spikes', to_file=False)

population and projection containers

v1_pop = [] v2_pop = [] v4_pop = [] pfc_pre = [] pfc = [] projections = []

print "%g - Creating v1 populations" % timer.elapsedTime()

for i in range(orientations): # Cycles orientations

creates a population for each connection

v1_pop.append(Population(scale.v1_pop_size[0]*scale.v1_pop_size[1],         # size 
              IF_curr_exp,   # Neuron Type
              cell_params,   # Neuron Parameters
              label="v1_%d" % i)) # Label)  
if layer_to_observe == 'v1' or layer_to_observe == 'all':
    print "%g - observing v1" % timer.elapsedTime()
    #if layer_to_observe == 'v1':    v1_pop[i].set_mapping_constraint({'x':0, 'y':0})
    v1_pop[i].record() # ('spikes', to_file=False)

print "%g - Creating v2 populations" % timer.elapsedTime()

for i in range(orientations): # Cycles orientations

creates a population for each connection

v2_pop.append(Population(scale.v2_pop_size[0]*scale.v2_pop_size[1],         # size 
              IF_curr_exp,   # Neuron Type
              cell_params,   # Neuron Parameters
              label="v2_%d" % i)) # Label)  
if layer_to_observe == 'v2' or layer_to_observe == 'all':
    print "%g - observing v2" % timer.elapsedTime()  
    #if layer_to_observe == 'v2':    v2_pop[i].set_mapping_constraint({'x':0, 'y':0})
    v2_pop[i].record() # ('spikes', to_file=False)

print "%g - Creating v4 populations" % timer.elapsedTime()

for i in range(orientations): # Cycles orientations

creates a population for each connection

v4_pop.append(Population(scale.v4_pop_size[0]*scale.v4_pop_size[1],         # size 
          IF_curr_exp,   # Neuron Type
      cell_params,   # Neuron Parameters
      label="v4_%d" % i)) # Label)  
if layer_to_observe == 'v4' or layer_to_observe == 'all':  
    print "%g - observing v4" % timer.elapsedTime()  
    #if layer_to_observe == 'v4':    v4_pop[i].set_mapping_constraint({'x':0, 'y':1})
    v4_pop[i].record() # ('spikes', to_file=False)

print "%g - Creating PFC populations" % timer.elapsedTime()

for i in range(orientations): # Cycles orientations if multistage_pfc: pfc_pre.append(Population(scale.pfc_pop_size[0]_scale.pfc_pop_size[1], # size SpikeSourceArray, # Neuron Type {'spike_times': pfc_pre_times[i]}, # Neuron Parameters label="pfcpre%d" % i)) # Label if layer_to_observe == 'pfc_pre' or layer_to_observe == 'all':
print "%g - observing pfc_pre" % timer.elapsedTime()

pfc_pre[i].set_mapping_constraint({'x':0, 'y':1})

      pfc_pre[i].record() # ('spikes', to_file=False)
   cell_params_dual_exp = copy.deepcopy(cell_params)
   cell_params_dual_exp['tau_syn_E2'] = tau_syn_exc_2
   pfc.append(Population(scale.pfc_pop_size[0]_scale.pfc_pop_size[1],        # size 
          IF_curr_dual_exp,   # Neuron Type
          cell_params,   # Neuron Parameters
          label="pfc_%d" % i))  
else:
   pfc.append(Population(scale.pfc_pop_size[0]*scale.pfc_pop_size[1],        # size 
          IF_curr_exp,   # Neuron Type
          cell_params,   # Neuron Parameters
          label="pfc_%d" % i))
#pfc[i].initialize('v',v_init_distr)
pfc[i].randomInit(v_init_distr) # this was commented in SA's version
# set biasses to hardwire preference ADR
if i == preferred_orientation:
   pfc[i].set('i_offset', i_bias_pref)
elif i == aversive_orientation:
   pfc[i].set('i_offset', i_bias_avert)
else:
   pfc[i].set('i_offset', i_bias_neut)
if active_pfc == False:
   v_rest_or = []
   for j in range(pfc[i].size):
   v_rest_or.append(v_rest_distr.next())
   pfc[i].set('v_rest', v_rest_or)
   #pfc[i].tset('v_rest', numpy.array(v_rest_or))

if layer_to_observe == 'pfc' or layer_to_observe == 'all':                     
    print "%g - observing pfc" % timer.elapsedTime()    
    #pfc[i].set_mapping_constraint({'x':0, 'y':1})
    pfc[i].record() # ('spikes', to_file=False)

print "%g - Creating LIP population" % timer.elapsedTime() lip = Population(scale.lip_pop_size[0]*scale.lip_pop_size[1], # size IF_curr_exp, # Neuron Type cell_params, # Neuron Parameters label="lip") if layer_to_observe == 'lip' or layer_to_observe == 'all':
print "%g - observing lip" % timer.elapsedTime()

lip_placement = PlacerChipAndCoreConstraint(x=0, y=0)

#lip.set_constraint(lip_placement)  
#lip.set_mapping_constraint({'x':0, 'y':0}) # , 'p':15}
lip.record() # ('spikes', to_file=False)

print "%g - Creating gaussian Filters connections: scale=%d orientation=%d size=%f" % (timer.elapsedTime(), scales, orientations, scale.filter_scale)

projections = [] # Connection Handler gaussian_filters = TunedGaussianConnectorList(scales, orientations, scale.filter_scale, input_gain, gaussian_eccentricity)

for i in range(orientations):

creates connections lists for different orientations, implementing a

# convolutional network with different gaussian orientation filters (single scale)
conn_list = Filter2DConnector_jose(scale.input_size[0], scale.input_size[1], 
                           scale.v1_pop_size[0], scale.v1_pop_size[1], 
                           gaussian_filters[i], 
                           scale.x_kernel, scale.y_kernel, 
                           scale.jump[0], delays, 
                           gain=input_strength)
#conn_list_pairs = [(conn[0], conn[1]) for conn in conn_list]
#conn_list_pairs.sort()
#conn_list_file=open('input_v1_connections.txt', 'w')
#conn_list_file.write('input->v1 connections: %s\n' % conn_list_pairs)
#conn_list_file.close
projections.append(Projection(input_pol_1, v1_pop[i], 
FromListConnector(conn_list), label='input[p0]->v1_pop_%d' % (i)))
projections.append(Projection(input_pol_2, v1_pop[i], 
FromListConnector(conn_list), label='input[p1]->v1_pop_%d' % (i)))
"""
SortedConnections = sorted(conn_list, key=lambda x: x[1])
ConnectionsByDestination = itertools.groupby(SortedConnections, key=lambda x: x[1])
numDestinations = 0
numLinks = 0
ConnectionsFile = open('Conn_Stats_InV1_%d' % i, 'w')
for key in ConnectionsByDestination:
    sources = [conn[0] for conn in key[1]]
    ConnectionsFile.write('j: %i fanin: %i, sources: %s\n' % (key[0], len(sources), sources))
    numDestinations += 1
    numLinks += len(sources)
ConnectionsFile.write('Total projection size %i, Mean fan-in per neuron %f\n' % (numLinks, float(numLinks)/float(numDestinations)))
ConnectionsFile.close()
"""

if multistage_pfc == True: print "%g - Creating pfc_pre->pfc connections" % timer.elapsedTime() for i in range(orientations): if i == preferred_orientation: pfc_in_weight = pfc_pre_weights_pfc_bias_pref elif i == aversive_orientation: pfc_in_weight = pfc_pre_weights_pfc_bias_avert else: pfc_in_weight = pfc_pre_weights*pfc_bias_neut projections.append(Projection(pfc_pre[i], pfc[i], OneToOneConnector(weights=pfc_in_weight, delays=delays), target='excitatory2'))

if active_pfc == True: print "%g - Creating v2->pfc connections" % timer.elapsedTime() pfc_filters = TunedGaussianConnectorList(1, 1, scale.pfc_filter_scale[0], scale.pfc_filter_gain, scale.pfc_eccentricities[0]) pfc_filter_conn_list = Filter2DConnector_jose(scale.v2_pop_size[0], scale.v2_pop_size[1], scale.pfc_pop_size[0], scale.pfc_pop_size[1], pfc_filters[0], int(math.floor(scale.pfc_filter_scale[0])), int(math.floor(scale.pfc_filter_scale[1])), scale.pfc_jumps[0], delays, gain=v2_pfc_weights) for i in range(orientations): projections.append(Projection(v2_pop[i], pfc[i], FromListConnector(pfc_filter_conn_list), label='v2pop%d->pfc%d' % (i,i)))

print "%g - Creating v1->v2 connections" % timer.elapsedTime()

for i in range(orientations): projections.append(Projection(v1_pop[i], v2_pop[i], OneToOneConnector(weights=v1_v2weights, delays=delays), label='v1->v2(pop%d)' % (i))) if feedback_on: projections.append(Projection(v2_pop[i], v1_pop[i], OneToOneConnector(weights=v1_v2_weights*feedback_weight_scaling, delays=delays), label='v2->v1(pop%d)' % (i)))

if(wta_v2 == True): print "%g - Creating Lateral inhibition for the v2 populations" % timer.elapsedTime() for i in range(orientations): # Cycles orientations for j in range(orientations): # Cycles orientations if (i!=j): # Avoid self connections

Creates lateral inhibition between the v2 populations

    print "%g - v2[%d]->v2[%d] lateral inhibition" % (timer.elapsedTime(), i, j)
        wta_between_list =  ProximityConnector(scale.v2_pop_size[0], scale.v2_pop_size[1], scale.v2_subfield, 
                                                    wta_between_v2_weight, 1, allow_self_connections=True)
    projections.append(Projection(  v2_pop[i], 
                                    v2_pop[j], 
                                    FromListConnector(wta_between_list), 
                                    target='inhibitory'))

print "%g - Creating within inhibition pools" % timer.elapsedTime() for i in range(orientations): # Cycles orientations wta_within_list = ProximityConnector(scale.v2_pop_size[0], scale.v2_pop_size[1], scale.v2_subfield, wta_within_v2_weight, 1, allow_self_connections=False) print "%g - v2[%d] within inhibition" % (timer.elapsedTime(), i) projections.append(Projection( v2_pop[i], v2_pop[i], FromListConnector(wta_within_list), target='inhibitory'))

print "%g - Creating v2->v4 projections" % timer.elapsedTime()

for i in range(orientations): # Cycles orientations v2_v4_conn_list = subSamplerConnector2D(scale.v2_pop_size[0], scale.v4_pop_size[0], weights_v2_v4, v2_v4_delays) print "%g - v2-v4[%d] subsampling projection" % (timer.elapsedTime(), i) projections.append(Projection( v2_pop[i], v4_pop[i], FromListConnector(v2_v4_conn_list), target='excitatory')) if plasticity_on: # added ability to set plasticity ADR

this turns on plasticity in the last projection to be appended

   # to the list (which was just done above)
   Proj_Plasticity = SynapseDynamics(slow=stdp_model)
   #projections[-1].set('synapse_dynamics', SynapseDynamics(slow=stdp_model))
   projections[-1].synapse_dynamics = Proj_Plasticity
   #projections[-1].plasticity_id = Proj_Plasticity.id
if feedback_on: # feedback adds top-down biassing of preferred/active stimuli
   v4_v2_conn_list =  overSamplerConnector2D(scale.v4_pop_size[0], scale.v2_pop_size[0], weights_v2_v4*feedback_weight_scaling, v2_v4_delays) # overSamplerConnector remaps the downscaled connections to their original sources
   projections.append(Projection(  v4_pop[i], 
                                   v2_pop[i], 
                                   FromListConnector(v4_v2_conn_list),
                                   target='excitatory'))
   #v4_v1_conn_list =  overSamplerConnector2D(scale.v4_pop_size[0], scale.v1_pop_size[0], feedback_weight_scaling, 1) # overSamplerConnector remaps the downscaled connections to their original sources
   #projections.append(Projection(  v4_pop[i], 
   #                                v1_pop[i], 
    #                               FromListConnector(v4_v2_conn_list), 
     #                              target='excitatory'))

print "%g - Creating v4->lip projections" % timer.elapsedTime() for i in range(orientations): # Cycles orientations projections.append(Projection( v4_pop[i], lip, OneToOneConnector(weights=weights_v4_lip, delays=delays), target='excitatory'))

print "%g - Creating LIP WTA" % timer.elapsedTime()

projections.append(Projection( lip,

lip,

OneToOneConnector(weights=wta_lip_weight, delays=delays),

target='inhibitory'))

ADR added WTA connections to neighbouring neurons. Original version had only the

self connection, which looks wrong (would be an "inverse WTA")

projections.append(Projection( lip,

lip,

AllToAllConnector(weights=wta_lip_weight*wta_bias, delays=delays, allow_self_connections=False),

target='inhibitory'))

Temporary workaround for PACMAN103 builds a FromListConnector until such time as allow_self_connections

option is properly supported

lip_WTA_conn_list = [(i, j, wta_lip_weight if i == j else wta_lip_weight_wta_bias, 1) for i in range(scale.lip_pop_size[0]_scale.lip_pop_size[1]) for j in range(scale.lip_pop_size[0]*scale.lip_pop_size[1])]

conn_list_pairs = [(conn[0], conn[1], conn[2]) for conn in lip_WTA_conn_list]

conn_list_pairs.sort()

conn_list_file=open('lip_lip_connections.txt', 'w')

conn_list_file.write('lip->lip connections: %s\n' % conn_list_pairs)

conn_list_file.close

projections.append(Projection( lip, lip, FromListConnector(lip_WTA_conn_list),
target='inhibitory'))

print "%g - Creating pfc->v4 projections" % timer.elapsedTime() for i in range(orientations): # Cycles orientations if i == aversive_orientation: # aversive orientation connectivity projects to other orientations ADR if aversive_inhibitory: if active_pfc: projections.append(Projection( pfc[i], v4_pop[i], OneToOneConnector(weights=-pfc_v4_weights, delays=delays), target='inhibitory')) else: projections.append(Projection( pfc[i], v4_pop[i], AllToAllConnector(weights=-pfc_v4_weights, delays=delays), target='inhibitory')) if top_down_priming: projections.append(Projection( pfc[i], v1_pop[i], AllToAllConnector(weights=-pfc_v1_weights, delays=delays), target='inhibitory')) else: for j in [orientation for orientation in range(orientations) if orientation != i]: if active_pfc: projections.append(Projection( pfc[i], v4_pop[j], OneToOneConnector(weights=pfc_v4_weights, delays=delays), target='excitatory')) else: projections.append(Projection( pfc[i], v4_pop[j], AllToAllConnector(weights=pfc_v4_weights, delays=delays), target='excitatory')) if top_down_priming: projections.append(Projection( pfc[i], v1_pop[j], AllToAllConnector(weights=pfc_v1_weights, delays=delays), target='excitatory')) else: if active_pfc: projections.append(Projection( pfc[i], v4_pop[i], OneToOneConnector(weights=pfc_v4_weights, delays=delays), target='excitatory')) else:
projections.append(Projection( pfc[i], v4_pop[i], AllToAllConnector(weights=pfc_v4_weights, delays=delays), target='excitatory')) if top_down_priming: projections.append(Projection( pfc[i], v1_pop[i], AllToAllConnector(weights=pfc_v1_weights, delays=delays), target='excitatory'))

lip.set('tau_syn_E', 20)

pfc[3].set('i_offset', 1)

pfc[3].set('tau_refrac', 50)

setup_time = timer.elapsedTime()

Run the model

run(runtime) # Simulation time

run_time = timer.elapsedTime()

if output_to_file: output_file = open("./VA_runs_IEEE.txt", "a+") output_file.write("--------------------------------------------------------------------------------\n") output_file.write("NETWORK PARAMETERS:\n") output_file.write("-------------------------------\n") output_file.write("Input file name: %s\n" % InputFilePol1) output_file.write("Network base input size: %d\n" % base_num_neurons) output_file.write("Feedback on? %s\n" % feedback_on) output_file.write("FEF PFC? %s\n" % active_pfc) output_file.write("Learning on? %s\n" % plasticity_on) output_file.write("Preferred orientation: %d\n" % preferred_orientation) output_file.write("Aversive orientation: %d\n" % aversive_orientation) output_file.write("WV2->V4_init: %f\n" % weights_v2_v4) output_file.write("-------------------------------\n") output_file.write("TIMINGS:\n") output_file.write("Setup time: %f s\n" % setup_time) print type(run_time) print type(setup_time) print type(runtime) output_file.write("Load time: %f s \n" % (run_time-setup_time-(runtime/1000.0))) output_file.write("Run time: %f s \n" % (runtime/1000.0))
else: print "Setup time", setup_time print "Load time", (run_time - setup_time - runtime/1000.0) print "Run time", (runtime/1000.0)

get spikes and plot

For layers with sub-populations (V1, V2, PFC, V4)

''' print "V1 size is", scale.v1_pop_size[0], 'x', scale.v1_pop_size[1]

for i in xrange(orientations): V1_spikes = v1_pop[i].getSpikes(gather=True, compatible_output=True) print "V1, orientation", i, len(V1_spikes) print print "V2 size is", scale.v2_pop_size[0], 'x', scale.v2_pop_size[1] for i in xrange(orientations): V2_spikes = v2_pop[i].getSpikes(gather=True, compatible_output=True) print "V2, orientation", i, len(V2_spikes), v2_pop[i].meanSpikeCount(gather=True) print print "PFC size is", scale.pfc_pop_size[0], 'x', scale.pfc_pop_size[1] for i in xrange(orientations): PFC_spikes = pfc[i].getSpikes(gather=True, compatible_output=True) print "PFC, orientation", i, len(PFC_spikes) print print "V4 size is", scale.v4_pop_size[0], 'x', scale.v4_pop_size[1] for i in xrange(orientations): V4_spikes = v4_pop[i].getSpikes(gather=True, compatible_output=True) print "V4, orientation", i, len(V4_spikes), v4_pop[i].meanSpikeCount(gather=True) ''' if layer_to_observe == 'input_pol_1': data = numpy.asarray(input_pol_1.getSpikes())

data[:,0] = data[(all_rows, column_0)]

if plot_time_window: plt.scatter(data[(data[:,1] >= p_t_w[0]) & (data[:,1] < p_t_w[1]), 1], data[(data[:,1] >= p_t_w[0]) & (data[:,1] < p_t_w[1]), 0], color='green', s=4) else: plt.scatter(data[:,1], data[:,0], color='green', s=4) # s=1 elif layer_to_observe == 'input_pol_2': data = numpy.asarray(input_pol_2.getSpikes()) if plot_time_window: plt.scatter(data[(data[:,1] >= p_t_w[0]) & (data[:,1] < p_t_w[1]), 1], data[(data[:,1] >= p_t_w[0]) & (data[:,1] < p_t_w[1]), 0], color='green', s=4) else: plt.scatter(data[:,1], data[:,0], color='green', s=4) # s=1 if layer_to_observe == 'v1': id_accumulator=0 data_vector = [] for i in range(len(v1_pop)): data = numpy.asarray(v1_pop[i].getSpikes()) if vector_plot: if plot_time_window: data_vector.append(data[(data[:,1] >= p_t_w[0]) & (data[:,1] < p_t_w[1]),:]) else: data_vector.append(data) else: if len(data) > 0: if plot_time_window: if compatible_output: plt.scatter(data[(data[:,1] >= p_t_w[0]) & (data[:,1] < p_t_w[1]),1], data[(data[:,1] >= p_t_w[0]) & (data[:,1] < p_t_w[1]),0]

if layer_to_observe == 'lip' or layer_to_observe == 'all':

make a 2D array for plotting (lip)

print "LIP size is", scale.lip_pop_size[0], 'x', scale.lip_pop_size[1]

lip_array = numpy.zeros((int(scale.lip_pop_size[0]),int(scale.lip_pop_size[1])),float)

# Analysis and plotting of LIP spikes

lip_spikes = lip.getSpikes()

lip_counts = numpy.zeros((int(scale.lip_pop_size[0])*int(scale.lip_pop_size[1])),int)

lip_id = []
lip_times = []
xvals_lip = []
yvals_lip = []

# Do a plot of all spikes
if compatible_output:
   for sp in lip_spikes:
       lip_id.append(sp[0])
       lip_counts[sp[0]] +=1
       lip_times.append(sp[1])
       xpos = lip_x_array[sp[0]]
       ypos = lip_y_array[sp[0]]
       xvals_lip.append(xpos)
       yvals_lip.append(ypos)
else:
   for sp in lip_spikes:
       lip_id.append(sp[0])
       lip_counts[sp[0]] +=1
       lip_times.append(sp[1])
       xpos = lip_x_array[sp[0]]
       ypos = lip_y_array[sp[0]]
       xvals_lip.append(xpos)
       yvals_lip.append(ypos)

print lip_counts

lip_total = sum(lip_counts)

print "Total activity", lip_total

# Get the coordinates of the most active area
# and do a coarse mapping up to input resolution
activeLip = numpy.argmax(lip_counts)
lip_mag = round(float(scale.input_size[0])/float(scale.lip_pop_size[0])) 
print activeLip, lip_counts[activeLip], lip_x_array[activeLip],lip_y_array[activeLip],lip_x_array[activeLip]*lip_mag, lip_y_array[activeLip]*lip_mag

x_attend = double(lip_x_array[activeLip]*lip_mag)
y_attend = double(lip_y_array[activeLip]*lip_mag)
print "Salient position in Input Space", x_attend, y_attend

max_activation = float(lip_counts[activeLip])/float(lip_total)

print "Max Activation", max_activation

if lip_map_on:
   data = numpy.asarray(lip_spikes)
   if plot_time_window:
      if compatible_output:
         data_vector = data[(data[:,1] >= p_t_w[0]) & (data[:,1] < p_t_w[1]),:]
      else:
         data_vector = data[(data[:,1] >= p_t_w[0]) & (data[:,1] < p_t_w[1]),:]
   else:
      data_vector = data

   mapped_boxplot(data=data_vector, x_dim=scale.lip_pop_size[0], y_dim=scale.lip_pop_size[1], t_max=plot_t_max, t_min=plot_t_min, tau=16, compatible_output=compatible_output)

else:
   # Plotting of LIP saliency map

   # Calculate LIP map

   for nrn in xrange(scale.lip_pop_size[0]*scale.lip_pop_size[1]):
       xpos = lip_x_array[nrn]
       ypos = lip_y_array[nrn]
       lip_array[xpos,ypos] = float(lip_counts[nrn])/float(lip_total)
       #print nrn, xpos,ypos, float(lip_counts[nrn])/float(lip_total)

   print lip_array

   # make a custom colormap for plotting

   colormap = numpy.array([(0.0,0.0,0.0),
                   (0.1,0.1,0.1),
               (0.2,0.2,0.2),
               (0.3,0.3,0.3), 
               (0.4,0.4,0.4),        
               (0.5,0.5,0.5),
               (0.6,0.6,0.6),
               (0.7,0.7,0.7),
               (0.8,0.8,0.8),
               (0.9,0.9,0.9),
               (1.0,1.0,1.0)])

   ColMap = ListedColormap(colormap, name='attncolmap')

   register_cmap(cmap=ColMap)

   x = numpy.arange(0,scale.lip_pop_size[0]+1)
   y = numpy.arange(0,scale.lip_pop_size[1]+1)
   X,Y = numpy.meshgrid(x,y)

   plt.figure()
   plt.pcolor(X, Y, lip_array, shading='faceted', cmap=ColMap, vmin=0.0, vmax=max_activation)
   plt.colorbar()
   plt.title("LIP map")

   plt.figure()
   plt.plot(lip_times,lip_id,'.b')
   plt.xlim(0,runtime)
   plt.ylim(0,scale.lip_pop_size[0]*scale.lip_pop_size[1])
   plt.title("LIP spikes")

if metrics_on:
   met_data = numpy.asarray(lip_spikes)
   actual_objs = visual_metrics.get_annotations(input_file_name=MetricFile)
   rescaled_objs = visual_metrics.scale_annotations(annotations=actual_objs, scale_x=1/(1.6*2.0), scale_y=1/(1.6*2.0))
   biassed_objs = visual_metrics.bias_annotations(annotations=rescaled_objs, preferred=preferred_orientation, aversive=aversive_orientation)
   performance = visual_metrics.attn_performance_monitor(data=met_data, objects=biassed_objs, y_dim=scale.pfc_pop_size[1], t_window=metric_window, t_w_offset=metric_start_offset, t_start=metric_t_start, t_stop=metric_t_stop)
   if output_to_file:
      output_file.write("Metric time window: %d ms\n" % metric_window) 
      output_file.write("Metric window offset %d ms\n" % metric_start_offset)
      output_file.write("Start recording metrics at: %d ms\n" % metric_t_start)
      output_file.write("Stop recording metrics at: %d ms\n" % metric_t_stop)
      output_file.write("--------------------------------------\n")
      output_file.write("PERFORMANCE: \n")
      output_file.write("Time reference    Metric\n")
      output_file.write("__________________________\n")
      t_ref = metric_t_start+metric_start_offset
      for t_m in performance:
          output_file.write("%d ms             %f\n" % (t_ref, t_m))
          t_ref += metric_window
      output_file.write("--------------------------------------------------------------------------------\n\n")
      output_file.close() 
   else:
      print "Computed network performance(s) for this trial: %s\n" % performance
else:
   if output_to_file: 
      output_file.write("--------------------------------------------------------------------------------\n\n")
      output_file.close()

if layer_to_observe == 'input_pol_1' or layer_to_observe == 'all':

make a 2D array for plotting (input)

plotting_array = numpy.zeros((int(scale.input_size[0]),int(scale.input_size[1])),int)

pop_1_spikes = input_pol_1.getSpikes()

pop_1_id = [] pop_1_times = [] xvals_1 = [] yvals_1 = []

indicate areas of input activation

for sp in pop_1_spikes: pop_1_id.append(sp[1]) pop_1_times.append(sp[0]) xpos = input_x_array[sp[1]] ypos = input_y_array[sp[1]] xvals_1.append(xpos) yvals_1.append(ypos) plotting_array[xpos,ypos] = 3

indicate salient position in input space

calculated from LIP activity

plotting_array[int(x_attend),int(y_attend)] = 2

x = numpy.arange(0,scale.input_size[0]+1) y = numpy.arange(0,scale.input_size[1]+1) X,Y = numpy.meshgrid(x,y)

plt.figure() plt.pcolor(X, Y, plotting_array, shading='faceted', cmap=cmap.spectral) plt.xlim(0,scale.input_size[0]) plt.ylim(0,scale.input_size[1]) plt.title("Input pop 1")

plt.show()

end()

rowleya commented 8 years ago

I believe this has been fixed in a released version by adjusting the spike source array parameters