def run_experiment_nst(noisy=False, with_visualization=False):
"""
TODO: add experiment description.
"""
if noisy:
experiment_name = "NST_noisy"
else:
experiment_name = "NST"
experiment_duration = 10000 # in ms
dx = 44 # in pixels
dy = 30 # in pixels
max_d = 24 # in pixels
crop_xmin = 40 # in pixels
crop_ymin = 50 # in pixels
n_pops = 3 * (2 * (dx) * (max_d + 1) - (max_d + 1)**2 + max_d + 1) / 2
# Setup the simulation
Simulation = SNNSimulation(simulation_time=experiment_duration,
n_chips_required=n_pops / 16)
# Define the input source
if noisy:
path_to_input = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"../data/input/NSTlogo_disp12-8-3_noisy.dat")
else:
path_to_input = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"../data/input/NSTlogo_disp12-8-3.dat")
ExternalRetinaInput = ExternalInputReader(file_path=path_to_input,
dim_x=dx,
dim_y=dy,
crop_xmin=crop_xmin,
crop_xmax=crop_xmin + dx,
crop_ymin=crop_ymin,
crop_ymax=crop_ymin + dy,
sim_time=experiment_duration,
is_rawdata_time_in_ms=False)
# Create two instances of Retinas with the respective inputs
RetinaL = Retina(label="RetL",
dimension_x=dx,
dimension_y=dy,
spike_times=ExternalRetinaInput.retinaLeft,
record_spikes=False,
experiment_name=experiment_name)
RetinaR = Retina(label="RetR",
dimension_x=dx,
dimension_y=dy,
spike_times=ExternalRetinaInput.retinaRight,
record_spikes=False,
experiment_name=experiment_name)
# Create a cooperative network for stereo vision from retinal disparity
SNN_Network = CooperativeNetwork(retinae={
'left': RetinaL,
'right': RetinaR
},
max_disparity=max_d,
record_spikes=True,
record_v=False,
experiment_name=experiment_name)
# Start the simulation
Simulation.run()
# Store the results in a file
SNN_Network.get_spikes(sort_by_time=True, save_spikes=True)
# Finish the simulation
Simulation.end()
if with_visualization:
from visualizer import Visualizer
network_dimensions = SNN_Network.get_network_dimensions()
viz = Visualizer(network_dimensions=network_dimensions,
experiment_name=experiment_name,
spikes_file="./spikes/NST_0_spikes.dat")
# viz.microensemble_voltage_plot(save_figure=True)
viz.disparity_histogram(over_time=True, save_figure=True)
def run_experiment_pendulum(with_visualization=False):
"""
TODO: add experiment description.
"""
experiment_name = "Pendulum30"
experiment_duration = 10000 # in ms
dx = 72 # in pixels
dy = 84 # in pixels
max_d = 42 # in pixels
crop_xmin = 32 # in pixels
crop_ymin = 22 # in pixels
# Setup the simulation
Simulation = SNNSimulation(simulation_time=experiment_duration)
# Define the input source
path_to_input = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"../data/input/pendulum_left_30cm_2.tsv.npz")
ExternalRetinaInput = ExternalInputReader(file_path=path_to_input,
dim_x=dx,
dim_y=dy,
crop_xmin=crop_xmin,
crop_xmax=crop_xmin + dx,
crop_ymin=crop_ymin,
crop_ymax=crop_ymin + dy,
sim_time=experiment_duration,
is_rawdata_time_in_ms=False)
# Create two instances of Retinas with the respective inputs
RetinaL = Retina(label="RetL",
dimension_x=dx,
dimension_y=dy,
spike_times=ExternalRetinaInput.retinaLeft,
record_spikes=False,
experiment_name=experiment_name)
RetinaR = Retina(label="RetR",
dimension_x=dx,
dimension_y=dy,
spike_times=ExternalRetinaInput.retinaRight,
record_spikes=False,
experiment_name=experiment_name)
# Create a cooperative network for stereo vision from retinal disparity
SNN_Network = CooperativeNetwork(retinae={
'left': RetinaL,
'right': RetinaR
},
max_disparity=max_d,
record_spikes=True,
record_v=False,
experiment_name=experiment_name)
# Start the simulation
Simulation.run()
# Store the results in a file
SNN_Network.get_spikes(sort_by_time=True, save_spikes=True)
# Finish the simulation
Simulation.end()
if with_visualization:
from visualizer import Visualizer
network_dimensions = SNN_Network.get_network_dimensions()
# network_dimensions = {'dim_x':dx, 'dim_y':dy, 'min_d':0, 'max_d':max_d}
viz = Visualizer(network_dimensions=network_dimensions,
experiment_name=experiment_name,
spikes_file="./spikes/Pendulum30_0_spikes.dat")
# viz.microensemble_voltage_plot(save_figure=True)
viz.disparity_histogram(over_time=True, save_figure=True)
