def __init__(self):
## Simulation parameters ##
# Simulation time
self.simtime = 1000.0
# Number of trials
self.trials = 2
# Folder to save spike times
self.spike_folder = 'areaResponseCurves'
# Stimulus type
self.stim = '_disk_'
# Start time of plots
self.start_time = 200.0
# disk parameters
# self.disk_diameters = [0.0,0.05,0.15,0.2,0.3,0.4,0.5,0.6,0.7,0.8,1.0] # degrees
self.disk_diameters = [0.15, 0.5] # degrees
# Pulse parameters
self.pulse_duration = 500.0 # ms
self.pulse_tstart = 500.0 # ms (first 300 ms are used to fill the input and
# output buffers of linear filters)
self.bkg_illuminance = 250.0 # td
self.pulse_contrast = 0.8
self.pulse_amplitude = self.pulse_contrast * self.bkg_illuminance # td
# Cell to analyze
self.selected_cell = 0
# Cell to analyze is the center cell in every 2D grid
self.isCenterCell = True
# PSTH bin size
self.bin_size = 10.0 # ms
# Layers to track (labels for figures)
self.labels = [
'H1_Horizontal_cells', 'H2_Horizontal_cells',
'Midget_bipolar_cells_L_ON', 'Midget_bipolar_cells_L_OFF',
'Midget_bipolar_cells_M_ON', 'Midget_bipolar_cells_M_OFF',
'Diffuse_bipolar_cells_S_ON', 'S_cone_bipolar_cells_S_ON',
'AII_amacrine_cells', 'Midget_ganglion_cells_L_ON',
'Midget_ganglion_cells_L_OFF', 'Midget_ganglion_cells_M_ON',
'Midget_ganglion_cells_M_OFF',
'Small_bistratified_ganglion_cells_S_ON'
]
# Spiking layers
self.sp_labels = [
'Midget_ganglion_cells_L_ON', 'Midget_ganglion_cells_L_OFF',
'Midget_ganglion_cells_M_ON', 'Midget_ganglion_cells_M_OFF',
'Small_bistratified_ganglion_cells_S_ON'
]
# Parameters of the topographical plot
self.top_labels = [
'Midget_ganglion_cells_L_ON', 'Midget_ganglion_cells_L_OFF'
]
# Area-response labels
self.area_labels = [
'Midget_ganglion_cells_L_ON', 'Midget_ganglion_cells_L_OFF',
'Midget_ganglion_cells_M_ON', 'Midget_ganglion_cells_M_OFF'
]
## Graphical parameters ##
self.plot_intracellular = False
self.plot_PSTH = False
self.plot_topographical = False
# Individual intracellular traces
self.intracellular_rows = 6
self.intracellular_cols = 4
self.intracellular_video_step = 1.0 # ms
self.intracellular_starting_row = 2
self.intracellular_starting_col = 2
# PSTHs
self.PSTH_rows = 3
self.PSTH_cols = 2
self.PSTH_starting_row = 0
self.PSTH_starting_col = 1
# Topographical plot
self.topographical_rows = 2
self.topographical_cols = 4
self.topographical_time_intervals = [0.0, 250.0, 500.0, 750.0, 1000.0]
self.topographical_V_mins = [0.0, 0.0]
self.topographical_V_maxs = [150.0, 150.0]
self.topographical_isSpikes = True # (False = membrane potential, True = spikes)
# Area-response curve
self.area_rows = 2
self.area_cols = 2
## End of parameters ##
# Initialize PSTH
self.PSTHs = np.zeros(
(len(self.sp_labels), int(self.simtime / self.bin_size)))
# IDs from NEST
self.layers_to_record = [] # For membrane potentials
self.s_layers_to_record = [] # For spikes
# Data recorders used in NEST simulation
self.potentials = []
self.spikes = []
# Simulation object
self.newSimulation = run_network.runNetwork(self.simtime)
# Area-response curve
self.area_amp = np.zeros(
(len(self.area_labels), len(self.disk_diameters)))
self.area_ph = np.zeros(
(len(self.area_labels), len(self.disk_diameters)))
self.area_recorded_models = []
self.area_PSTH_index = []
# Input arrays
self.L_cone_input = np.zeros(
(self.newSimulation.Params['N'] * self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
self.M_cone_input = np.zeros(
(self.newSimulation.Params['N'] * self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
self.S_cone_input = np.zeros(
(self.newSimulation.Params['N'] * self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
# Video of the input stimulus
self.show_video = False
self.inputIm = np.zeros(
(self.newSimulation.Params['N'], self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
# Retinal mosaic (default: uniform distribution)
self.mosaic = np.ones(((3, self.newSimulation.Params['N'],
self.newSimulation.Params['N'])))
# Topographical plot
self.top_layers_to_record = []
self.top_PSTHs = np.zeros(
(int(self.newSimulation.Params['N'] *
self.newSimulation.Params['N']), len(self.sp_labels),
int(self.simtime / self.bin_size)))
self.top_PSTH_index = []
if (self.plot_topographical and self.topographical_isSpikes):
self.usetop_PSTHs = True # True = update PSTHS for topographical plot (slower)
else:
self.usetop_PSTHs = False
def __init__(self):
## Simulation parameters ##
# Simulation time
self.simtime = 1000.0
# Number of trials
self.trials = 4
# Folder to save spike times
self.spike_folder = 'flashing_spot'
# ID of the stimulus type
self.stim = '_disk_'
# Start time of plots
self.start_time = 200.0
# Select disk/annulus (0 = disk, 1 = annulus)
self.shape = 0
# spot type (0 = white spot, 1 = black spot)
self.spot_type = 0
# disk/annulus parameters
# self.inner_radius = 0.09 # degrees (L-M pathway)
# self.inner_radius = 0.15 # degrees (S pathway)
self.inner_radius = 0.5 # degrees (full-field flash)
self.outer_radius = 0.5 # degrees
# Pulse parameters
self.pulse_duration = 250.0 # ms
self.pulse_tstart = 500.0 # ms (first 300 ms are used to fill the input and
# output buffers of linear filters)
self.bkg_illuminance = 250.0 # td (value per cone type)
self.pulse_contrast = 0.8
self.pulse_amplitude = self.pulse_contrast * self.bkg_illuminance # td
# Cell to analyze
self.selected_cell = 0
# Cell to analyze is the center cell in every 2D grid
self.isCenterCell = True
# PSTH bin size
self.bin_size = 10.0 # ms
# Layers to track (labels for figures)
self.labels = [
'H1_Horizontal_cells', 'H2_Horizontal_cells',
'Midget_bipolar_cells_L_ON', 'Midget_bipolar_cells_L_OFF',
'Midget_bipolar_cells_M_ON', 'Midget_bipolar_cells_M_OFF',
'Diffuse_bipolar_cells_S_ON', 'S_cone_bipolar_cells_S_ON',
'AII_amacrine_cells', 'Midget_ganglion_cells_L_ON',
'Midget_ganglion_cells_L_OFF', 'Midget_ganglion_cells_M_ON',
'Midget_ganglion_cells_M_OFF'
# 'Small_bistratified_ganglion_cells_S_ON'
]
# Spiking layers
self.sp_labels = [
'Midget_ganglion_cells_L_ON', 'Midget_ganglion_cells_L_OFF',
'Midget_ganglion_cells_M_ON', 'Midget_ganglion_cells_M_OFF'
# 'Small_bistratified_ganglion_cells_S_ON'
]
# Parameters of the topographical plot
self.top_labels = [
# 'Midget_bipolar_cells_L_ON',
# 'Midget_bipolar_cells_L_OFF'
'Midget_ganglion_cells_L_ON',
'Midget_ganglion_cells_L_OFF'
# 'Midget_ganglion_cells_M_ON',
# 'Midget_ganglion_cells_M_OFF'
# 'Small_bistratified_ganglion_cells_S_ON'
]
## Graphical parameters ##
self.plot_intracellular = True
self.plot_PSTH = True
self.plot_topographical = True
# Individual intracellular traces
self.intracellular_rows = 6
self.intracellular_cols = 4
self.intracellular_video_step = 1.0 # ms
self.intracellular_starting_row = 2
self.intracellular_starting_col = 2
# PSTHs
self.PSTH_rows = 3
self.PSTH_cols = 2
self.PSTH_starting_row = 0
self.PSTH_starting_col = 1
# Topographical plot
self.topographical_rows = 2
self.topographical_cols = 5
self.topographical_time_intervals = [
450.0, 500.0, 550.0, 600.0, 750.0, 800.0
]
# self.topographical_V_mins = [-45.5, -45.5, -45.5, -45.5, -45.5]
# self.topographical_V_maxs = [-41.5, -41.5, -41.5, -41.5, -41.5]
self.topographical_V_mins = [0.0, 0.0, 0.0, 0.0, 0.0]
self.topographical_V_maxs = [200.0, 200.0, 200.0, 200.0, 200.0]
self.topographical_isSpikes = True # (False = membrane potential, True = spikes)
## End of parameters ##
# Initialize PSTH
self.PSTHs = np.zeros(
(len(self.sp_labels), int(self.simtime / self.bin_size)))
# IDs from NEST
self.layers_to_record = [] # For membrane potentials
self.s_layers_to_record = [] # For spikes
# Data recorders used in NEST simulation
self.potentials = []
self.spikes = []
# Simulation object
self.newSimulation = run_network.runNetwork(self.simtime)
# Input arrays
self.L_cone_input = np.zeros(
(self.newSimulation.Params['N'] * self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
self.M_cone_input = np.zeros(
(self.newSimulation.Params['N'] * self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
self.S_cone_input = np.zeros(
(self.newSimulation.Params['N'] * self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
# Video of the input stimulus
self.show_video = False
self.inputIm = np.zeros(
(self.newSimulation.Params['N'], self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
# Retinal mosaic (default: uniform distribution)
self.mosaic = np.ones(((3, self.newSimulation.Params['N'],
self.newSimulation.Params['N'])))
# Topographical plot
self.top_layers_to_record = []
self.top_PSTHs = np.zeros(
(int(self.newSimulation.Params['N'] *
self.newSimulation.Params['N']), len(self.sp_labels),
int(self.simtime / self.bin_size)))
self.top_PSTH_index = []
if (self.plot_topographical and self.topographical_isSpikes):
self.usetop_PSTHs = True # True = update PSTHS for topographical plot (slower)
else:
self.usetop_PSTHs = False
def __init__(self):
## Simulation parameters ##
# Simulation time
self.simtime = 1000.0
# Simulation object
self.newSimulation = run_network.runNetwork(self.simtime)
# Number of trials
self.trials = 2
# Folder to load/save spike data
self.spike_folder = 'flashing_square'
# Stimulus ID
# self.stim = '_disk_'
self.stim = '_square_'
# Start time of plots
self.start_time = 200.0
# Record membrane potentials
self.record_Vm = False
# Cell to analyze
self.selected_cell = []
# Cell to analyze is the center cell in every 2D grid
self.isCenterCell = True
# size of each layer (number of cells)
N_r = self.newSimulation.Params['N_LGN']
N_c = self.newSimulation.Params['N_cortex']
# PSTH bin size
self.bin_size = 10.0 # ms
# Layers to track
self.labels = [
'Parvo_LGN_relay_cell_L_ON', 'Parvo_LGN_relay_cell_L_OFF',
'Parvo_LGN_relay_cell_M_ON', 'Parvo_LGN_relay_cell_M_OFF',
'Parvo_LGN_interneuron_ON', 'Parvo_LGN_interneuron_OFF',
'Color_Luminance_L_ON_L_OFF_vertical',
'Color_Luminance_L_ON_L_OFF_horizontal',
'Color_Luminance_L_OFF_L_ON_vertical',
'Color_Luminance_L_OFF_L_ON_horizontal',
'Color_Luminance_M_ON_M_OFF_vertical',
'Color_Luminance_M_ON_M_OFF_horizontal',
'Color_Luminance_M_OFF_M_ON_vertical',
'Color_Luminance_M_OFF_M_ON_horizontal',
'Luminance_preferring_ON_OFF_vertical',
'Luminance_preferring_ON_OFF_horizontal',
'Luminance_preferring_OFF_ON_vertical',
'Luminance_preferring_OFF_ON_horizontal',
'Color_preferring_L_ON_M_OFF', 'Color_preferring_M_ON_L_OFF',
'Color_Luminance_inh_L_ON_L_OFF_vertical',
'Color_Luminance_inh_L_ON_L_OFF_horizontal',
'Color_Luminance_inh_L_OFF_L_ON_vertical',
'Color_Luminance_inh_L_OFF_L_ON_horizontal',
'Color_Luminance_inh_M_ON_M_OFF_vertical',
'Color_Luminance_inh_M_ON_M_OFF_horizontal',
'Color_Luminance_inh_M_OFF_M_ON_vertical',
'Color_Luminance_inh_M_OFF_M_ON_horizontal',
'Luminance_preferring_inh_ON_OFF_vertical',
'Luminance_preferring_inh_ON_OFF_horizontal',
'Luminance_preferring_inh_OFF_ON_vertical',
'Luminance_preferring_inh_OFF_ON_horizontal',
'Color_preferring_inh_L_ON_M_OFF',
'Color_preferring_inh_M_ON_L_OFF'
]
# Parameters of the topographical plot
self.top_labels = [
'Parvo_LGN_relay_cell_L_ON', 'Parvo_LGN_relay_cell_L_OFF',
'Color_Luminance_L_ON_L_OFF_vertical',
'Color_Luminance_M_ON_M_OFF_horizontal',
'Luminance_preferring_ON_OFF_vertical',
'Color_preferring_L_ON_M_OFF',
'Color_Luminance_inh_L_ON_L_OFF_vertical',
'Luminance_preferring_inh_ON_OFF_vertical',
'Color_preferring_inh_L_ON_M_OFF'
]
# Average activity of the population
self.pop_labels = [
'Color_Luminance_L_ON_L_OFF_vertical',
'Color_Luminance_L_ON_L_OFF_horizontal',
'Color_Luminance_L_OFF_L_ON_vertical',
'Color_Luminance_L_OFF_L_ON_horizontal',
'Color_Luminance_M_ON_M_OFF_vertical',
'Color_Luminance_M_ON_M_OFF_horizontal',
'Color_Luminance_M_OFF_M_ON_vertical',
'Color_Luminance_M_OFF_M_ON_horizontal',
'Luminance_preferring_ON_OFF_vertical',
'Luminance_preferring_ON_OFF_horizontal',
'Luminance_preferring_OFF_ON_vertical',
'Luminance_preferring_OFF_ON_horizontal',
'Color_preferring_L_ON_M_OFF', 'Color_preferring_M_ON_L_OFF',
'Color_Luminance_inh_L_ON_L_OFF_vertical',
'Color_Luminance_inh_L_ON_L_OFF_horizontal',
'Color_Luminance_inh_L_OFF_L_ON_vertical',
'Color_Luminance_inh_L_OFF_L_ON_horizontal',
'Color_Luminance_inh_M_ON_M_OFF_vertical',
'Color_Luminance_inh_M_ON_M_OFF_horizontal',
'Color_Luminance_inh_M_OFF_M_ON_vertical',
'Color_Luminance_inh_M_OFF_M_ON_horizontal',
'Luminance_preferring_inh_ON_OFF_vertical',
'Luminance_preferring_inh_ON_OFF_horizontal',
'Luminance_preferring_inh_OFF_ON_vertical',
'Luminance_preferring_inh_OFF_ON_horizontal',
'Color_preferring_inh_L_ON_M_OFF',
'Color_preferring_inh_M_ON_L_OFF'
]
## Graphical parameters ##
self.plot_intracellular = False
self.plot_PSTH = False
self.plot_topographical = True
# Individual intracellular traces
self.intracellular_rows = 4
self.intracellular_cols = 5
self.intracellular_starting_row = 0
self.intracellular_starting_col = 0
# PSTHs
self.PSTH_rows = 4
self.PSTH_cols = 3
self.PSTH_starting_row = 0
self.PSTH_starting_col = 0
# Topographical plot
self.topographical_rows = 9
self.topographical_cols = 5
self.topographical_time_intervals = [
450.0, 500.0, 550.0, 600.0, 750.0, 800.0
]
self.topographical_V_mins = [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]
self.topographical_V_maxs = [
150., 150., 50., 50., 50., 50., 100., 100., 100.
]
self.pop_V_mins = [0.0]
self.pop_V_maxs = [10.0]
self.topographical_isSpikes = True # (False = membrane potential, True = spikes)
## End of parameters ##
# Initialize PSTH
self.PSTHs = np.zeros(
(len(self.labels), int(self.simtime / self.bin_size)))
# IDs from NEST
self.layers_to_record = []
# Data recorders used in NEST simulation
self.potentials = []
self.spikes = []
# Retina references
self.retina_labels = [
'Midget_ganglion_cells_L_ON', 'Midget_ganglion_cells_L_OFF',
'Midget_ganglion_cells_M_ON', 'Midget_ganglion_cells_M_OFF'
]
# 'Small_bistratified_ganglion_cells_S_ON']
# Topographical plot
self.top_layers_to_record = []
self.top_PSTHs = np.zeros(
(int(self.newSimulation.Params['N_cortex'] *
self.newSimulation.Params['N_cortex']), len(self.labels),
int(self.simtime / self.bin_size)))
self.top_PSTH_index = []
if (self.plot_topographical and self.topographical_isSpikes):
self.usetop_PSTHs = True # True = update PSTHS for topographical plot (slower)
else:
self.usetop_PSTHs = False
# Population average
self.pop_layers_to_record = []
self.pop_PSTH_index = []
def __init__(self):
## Simulation parameters ##
# Simulation time
self.simtime = 600.0
# Number of trials
self.trials = 2
# Folder to save spike times
self.spike_folder = 'Receptive_field'
# Stimulus type
self.stim = '_disk_'
# Square mask where stimuli are displayed (side = 2*mask_side+1)
self.mask_side = 4
# Type of stimulus:
# 0 = red, 1 = green, 2 = black/white
self.stimulus_type = 2
# Intervals to average
self.RF_intervals = [[320.0, 340.0], [340.0, 360.0], [360.0, 380.0],
[380.0, 400.0], [400.0, 420.0]]
# Start time of plots
self.start_time = 200.0
# disk parameters
self.radius = 0.09 # degrees
# Pulse parameters
self.pulse_duration = 40.0 # ms
self.pulse_tstart = 300.0 # ms (first 300 ms are used to fill the input and
# output buffers of linear filters)
self.bkg_illuminance = 250.0 # td
self.pulse_contrast = 0.8
self.pulse_amplitude = self.pulse_contrast * self.bkg_illuminance # td
# Cell to analyze
self.selected_cell = 210
# PSTH bin size
self.bin_size = 10.0 # ms
# Layers to track (labels for figures)
self.labels = [
'Midget_ganglion_cells_L_ON', 'Midget_ganglion_cells_L_OFF',
'Midget_ganglion_cells_M_ON', 'Midget_ganglion_cells_M_OFF'
]
# Spiking layers
self.sp_labels = [
'Midget_ganglion_cells_L_ON', 'Midget_ganglion_cells_L_OFF',
'Midget_ganglion_cells_M_ON', 'Midget_ganglion_cells_M_OFF'
]
# Parameters of the topographical plot
self.top_labels = [
'Midget_ganglion_cells_L_ON', 'Midget_ganglion_cells_L_OFF',
'Midget_ganglion_cells_M_ON', 'Midget_ganglion_cells_M_OFF'
]
## Graphical parameters ##
self.plot_intracellular = False
self.plot_PSTH = False
self.plot_topographical = False
# Individual intracellular traces
self.intracellular_rows = 3
self.intracellular_cols = 4
self.intracellular_video_step = 1.0 # ms
self.intracellular_starting_row = 2
self.intracellular_starting_col = 2
# PSTHs
self.PSTH_rows = 3
self.PSTH_cols = 2
self.PSTH_starting_row = 0
self.PSTH_starting_col = 1
# Topographical plot
self.topographical_rows = 2
self.topographical_cols = 4
self.topographical_time_intervals = [0.0, 300.0, 350.0, 400.0, 600.0]
self.topographical_V_mins = [0.0, 0.0]
self.topographical_V_maxs = [150.0, 150.0]
self.topographical_isSpikes = True # (False = membrane potential, True = spikes)
## End of parameters ##
# Initialize PSTH
self.PSTHs = np.zeros(
(len(self.sp_labels), int(self.simtime / self.bin_size)))
# IDs from NEST
self.layers_to_record = [] # For membrane potentials
self.s_layers_to_record = [] # For spikes
# Data recorders used in NEST simulation
self.potentials = []
self.spikes = []
# Simulation object
self.newSimulation = run_network.runNetwork(self.simtime)
# Input arrays
self.L_cone_input = np.zeros(
(self.newSimulation.Params['N'] * self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
self.M_cone_input = np.zeros(
(self.newSimulation.Params['N'] * self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
self.S_cone_input = np.zeros(
(self.newSimulation.Params['N'] * self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
# Video of the input stimulus
self.show_video = False
self.inputIm = np.zeros(
(self.newSimulation.Params['N'], self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
# Retinal mosaic (default: uniform distribution)
self.mosaic = np.ones(((3, self.newSimulation.Params['N'],
self.newSimulation.Params['N'])))
# Topographical plot
self.top_layers_to_record = []
self.top_PSTHs = np.zeros(
(int(self.newSimulation.Params['N'] *
self.newSimulation.Params['N']), len(self.sp_labels),
int(self.simtime / self.bin_size)))
self.top_PSTH_index = []
if (self.plot_topographical and self.topographical_isSpikes):
self.usetop_PSTHs = True # True = update PSTHS for topographical plot (slower)
else:
self.usetop_PSTHs = False
# Receptive_field
self.RF_mask = []
self.RF_bright = np.zeros(
(len(self.RF_intervals), len(self.labels),
self.newSimulation.Params['N'], self.newSimulation.Params['N']))
self.RF_dark = np.zeros(
(len(self.RF_intervals), len(self.labels),
self.newSimulation.Params['N'], self.newSimulation.Params['N']))
def __init__(self):
## Simulation parameters ##
# Simulation time
self.simtime = 1000.0
# Simulation object
self.newSimulation = run_network.runNetwork(self.simtime)
# Number of trials
self.trials = 2
# Folder to save spike times
self.spike_folder = 'areaResponseCurves'
# Start time of plots
self.start_time = 200.0
# Pulse parameters
self.pulse_duration = 500.0 # ms
self.pulse_tstart = 500.0 # ms (first 300 ms are used to fill the input and
# output buffers of linear filters)
# Record membrane potentials
self.record_Vm = False
# disk parameters
# self.disk_diameters = [0.0,0.05,0.15,0.2,0.3,0.4,0.5,0.6,0.7,0.8,1.0] # degrees
self.disk_diameters = [0.15, 0.5] # degrees
# Cell to analyze
self.selected_cell = []
# Cell to analyze is the center cell in every 2D grid
self.isCenterCell = True
# size of each layer (number of cells)
N_r = self.newSimulation.Params['N_LGN']
N_c = self.newSimulation.Params['N_cortex']
# PSTH bin size
self.bin_size = 10.0 # ms
# Layers to track
self.labels = [
'Parvo_LGN_relay_cell_L_ON',
# 'Parvo_LGN_relay_cell_L_OFF',
'Parvo_LGN_relay_cell_M_ON',
# 'Parvo_LGN_relay_cell_M_OFF',
# 'Parvo_LGN_interneuron_ON',
# 'Parvo_LGN_interneuron_OFF',
'Color_Luminance_L_ON_L_OFF_vertical',
'Color_Luminance_L_ON_L_OFF_horizontal',
'Color_Luminance_L_OFF_L_ON_vertical',
# 'Color_Luminance_L_OFF_L_ON_horizontal',
'Color_Luminance_M_ON_M_OFF_vertical',
# 'Color_Luminance_M_ON_M_OFF_horizontal',
'Color_Luminance_M_OFF_M_ON_vertical',
# 'Color_Luminance_M_OFF_M_ON_horizontal',
'Luminance_preferring_ON_OFF_vertical',
'Luminance_preferring_ON_OFF_horizontal',
'Luminance_preferring_OFF_ON_vertical',
'Luminance_preferring_OFF_ON_horizontal',
'Color_preferring_L_ON_M_OFF',
'Color_preferring_M_ON_L_OFF',
'Color_Luminance_inh_L_ON_L_OFF_vertical',
# 'Color_Luminance_inh_L_ON_L_OFF_horizontal',
# 'Color_Luminance_inh_L_OFF_L_ON_vertical',
# 'Color_Luminance_inh_L_OFF_L_ON_horizontal',
# 'Color_Luminance_inh_M_ON_M_OFF_vertical',
# 'Color_Luminance_inh_M_ON_M_OFF_horizontal',
# 'Color_Luminance_inh_M_OFF_M_ON_vertical',
# 'Color_Luminance_inh_M_OFF_M_ON_horizontal',
'Luminance_preferring_inh_ON_OFF_vertical'
# 'Luminance_preferring_inh_ON_OFF_horizontal',
# 'Luminance_preferring_inh_OFF_ON_vertical',
# 'Luminance_preferring_inh_OFF_ON_horizontal',
# 'Color_preferring_inh_L_ON_M_OFF',
# 'Color_preferring_inh_M_ON_L_OFF'
]
# Area-response labels
self.area_labels = [
'Parvo_LGN_relay_cell_L_ON',
# 'Parvo_LGN_relay_cell_L_OFF',
'Parvo_LGN_relay_cell_M_ON',
# 'Parvo_LGN_relay_cell_M_OFF',
# 'Parvo_LGN_interneuron_ON',
# 'Parvo_LGN_interneuron_OFF',
'Color_Luminance_L_ON_L_OFF_vertical',
'Color_Luminance_L_ON_L_OFF_horizontal',
'Color_Luminance_L_OFF_L_ON_vertical',
# 'Color_Luminance_L_OFF_L_ON_horizontal',
'Color_Luminance_M_ON_M_OFF_vertical',
# 'Color_Luminance_M_ON_M_OFF_horizontal',
'Color_Luminance_M_OFF_M_ON_vertical',
# 'Color_Luminance_M_OFF_M_ON_horizontal',
'Luminance_preferring_ON_OFF_vertical',
'Luminance_preferring_ON_OFF_horizontal',
'Luminance_preferring_OFF_ON_vertical',
'Luminance_preferring_OFF_ON_horizontal',
'Color_preferring_L_ON_M_OFF',
'Color_preferring_M_ON_L_OFF',
'Color_Luminance_inh_L_ON_L_OFF_vertical',
# 'Color_Luminance_inh_L_ON_L_OFF_horizontal',
# 'Color_Luminance_inh_L_OFF_L_ON_vertical',
# 'Color_Luminance_inh_L_OFF_L_ON_horizontal',
# 'Color_Luminance_inh_M_ON_M_OFF_vertical',
# 'Color_Luminance_inh_M_ON_M_OFF_horizontal',
# 'Color_Luminance_inh_M_OFF_M_ON_vertical',
# 'Color_Luminance_inh_M_OFF_M_ON_horizontal',
'Luminance_preferring_inh_ON_OFF_vertical'
# 'Luminance_preferring_inh_ON_OFF_horizontal',
# 'Luminance_preferring_inh_OFF_ON_vertical',
# 'Luminance_preferring_inh_OFF_ON_horizontal',
# 'Color_preferring_inh_L_ON_M_OFF',
# 'Color_preferring_inh_M_ON_L_OFF'
]
## Graphical parameters ##
self.plot_intracellular = False
self.plot_PSTH = False
# Individual intracellular traces
self.intracellular_rows = 4
self.intracellular_cols = 5
self.intracellular_starting_row = 0
self.intracellular_starting_col = 0
# PSTHs
self.PSTH_rows = 4
self.PSTH_cols = 5
self.PSTH_starting_row = 0
self.PSTH_starting_col = 0
# Area-response curve
self.area_rows = 3
self.area_cols = 5
## End of parameters ##
# Initialize PSTH
self.PSTHs = np.zeros(
(len(self.labels), int(self.simtime / self.bin_size)))
# IDs from NEST
self.layers_to_record = [] # For membrane potentials
# Data recorders used in NEST simulation
self.potentials = []
self.spikes = []
# Retina references
self.retina_labels = [
'Midget_ganglion_cells_L_ON', 'Midget_ganglion_cells_L_OFF',
'Midget_ganglion_cells_M_ON', 'Midget_ganglion_cells_M_OFF',
'Small_bistratified_ganglion_cells_S_ON'
]
# Area-response curve
self.area_amp = np.zeros(
(len(self.area_labels), len(self.disk_diameters)))
self.area_ph = np.zeros(
(len(self.area_labels), len(self.disk_diameters)))
self.area_recorded_models = []
self.area_PSTH_index = []
def __init__(self):
## Simulation parameters ##
# Simulation time
self.simtime = 1000.0
# Number of trials
self.trials = 2
# Folder to save spike times
self.spike_folder = 'Luminance_Grating'
# Type of grating:
# (0 = luminance grating, 1 = chromatic isoluminant grating (L vs M), 2 =
# chromatic isoluminant grating (LM vs S), 3 = L cone-isolating, 4 =
# M cone-isolating, 5 = S cone-isolating)
self.grating_type = 0
# Grating parameters
# self.spatial_frequency = np.array([0.1,0.2,0.3,0.4,0.5,1.0,1.5,2.0,2.5,3.0,3.5,4.0,4.5,5.0,5.5,6.0]) # cpd
self.spatial_frequency = np.array([1.0, 3.0]) # cpd
self.temporal_frequency = 2.0 # Hz
self.bkg_illuminance = 250.0 # td
# Michelson contrast:
# Imax = bkg + ampl
# Imin = bkg - ampl
# contrast = (Imax - Imin)/(Imax + Imin) = ampl/bkg
self.contrast = 0.8
# Random distribution of cones
self.generate_random_mosaic = False
self.density_ratio = [0.6, 0.3, 0.1] # L-M-S cones
self.load_mosaic = False
# Start time of plots
self.start_time = 200.0
# Cell to analyze
self.selected_cell = 0
# Cell to analyze is the center cell in every 2D grid
self.isCenterCell = True
# PSTH bin size
self.bin_size = 10.0 # ms
# Layers to track (labels for figures)
self.labels = [
'H1_Horizontal_cells', 'H2_Horizontal_cells',
'Midget_bipolar_cells_L_ON', 'Midget_bipolar_cells_L_OFF',
'Midget_bipolar_cells_M_ON', 'Midget_bipolar_cells_M_OFF',
'Diffuse_bipolar_cells_S_ON', 'S_cone_bipolar_cells_S_ON',
'AII_amacrine_cells', 'Midget_ganglion_cells_L_ON',
'Midget_ganglion_cells_L_OFF', 'Midget_ganglion_cells_M_ON',
'Midget_ganglion_cells_M_OFF',
'Small_bistratified_ganglion_cells_S_ON'
]
# Spiking layers
self.sp_labels = [
'Midget_ganglion_cells_L_ON', 'Midget_ganglion_cells_L_OFF',
'Midget_ganglion_cells_M_ON', 'Midget_ganglion_cells_M_OFF',
'Small_bistratified_ganglion_cells_S_ON'
]
# FFT labels
self.FFT_labels = [
'Midget_ganglion_cells_L_ON',
'Midget_ganglion_cells_L_OFF',
# 'Midget_ganglion_cells_M_ON'
# 'Midget_ganglion_cells_M_OFF'
]
## Graphical parameters ##
self.plot_intracellular = False
self.plot_PSTH = False
# Individual intracellular traces
self.intracellular_rows = 6
self.intracellular_cols = 4
self.intracellular_video_step = 1.0 # ms
self.intracellular_starting_row = 2
self.intracellular_starting_col = 2
# PSTHs
self.PSTH_rows = 3
self.PSTH_cols = 2
self.PSTH_starting_row = 0
self.PSTH_starting_col = 1
# FFT plot
self.FFT_rows = 1
self.FFT_cols = 2
## End of parameters ##
# Initialize PSTH
self.PSTHs = np.zeros(
(len(self.sp_labels), int(self.simtime / self.bin_size)))
# IDs from NEST
self.layers_to_record = [] # For membrane potentials
self.s_layers_to_record = [] # For spikes
# Data recorders used in NEST simulation
self.potentials = []
self.spikes = []
# Simulation object
self.newSimulation = run_network.runNetwork(self.simtime)
# FFT
self.FFTamp = np.zeros(
(len(self.FFT_labels), len(self.spatial_frequency)))
self.FFTph = np.zeros(
(len(self.FFT_labels), len(self.spatial_frequency)))
self.FFT_recorded_models = []
self.FFT_PSTH_index = []
# Input arrays
self.L_cone_input = np.zeros(
(self.newSimulation.Params['N'] * self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
self.M_cone_input = np.zeros(
(self.newSimulation.Params['N'] * self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
self.S_cone_input = np.zeros(
(self.newSimulation.Params['N'] * self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
# Video of the input stimulus
self.show_video = False
self.inputIm = np.zeros(
(self.newSimulation.Params['N'], self.newSimulation.Params['N'],
int(self.newSimulation.Params['simtime'] /
self.newSimulation.Params['resolution'])))
# Retinal mosaic (default: uniform distribution)
self.mosaic = np.ones(((3, self.newSimulation.Params['N'],
self.newSimulation.Params['N'])))
def __init__(self):
## Simulation parameters ##
# Simulation time
self.simtime = 600.0
# Simulation object
self.newSimulation = run_network.runNetwork(self.simtime)
# Number of trials
self.trials = 2
# Folder to save spike times
self.spike_folder = 'Receptive_field'
# Square mask where stimuli are displayed (side = 2*mask_side+1)
self.mask_side = 4
# Intervals to average
self.RF_intervals = [[320.0, 340.0], [340.0, 360.0], [360.0, 380.0],
[380.0, 400.0], [400.0, 420.0]]
# Start time of plots
self.start_time = 200.0
# Record membrane potentials
self.record_Vm = False
# Cell to analyze
self.isCenterCell = True
self.selected_cell = []
# size of each layer (number of cells)
N_r = self.newSimulation.Params['N_LGN']
N_c = self.newSimulation.Params['N_cortex']
# PSTH bin size
self.bin_size = 20.0 # ms
# Layers to track
self.labels = [
'Parvo_LGN_relay_cell_L_ON', 'Parvo_LGN_relay_cell_L_OFF',
'Color_Luminance_L_ON_L_OFF_vertical',
'Luminance_preferring_ON_OFF_vertical',
'Color_preferring_L_ON_M_OFF'
]
## Graphical parameters ##
self.plot_intracellular = False
self.plot_PSTH = False
# Individual intracellular traces
self.intracellular_rows = 1
self.intracellular_cols = 5
self.intracellular_starting_row = 0
self.intracellular_starting_col = 0
# PSTHs
self.PSTH_rows = 1
self.PSTH_cols = 5
self.PSTH_starting_row = 0
self.PSTH_starting_col = 0
## End of parameters ##
# Initialize PSTH
self.PSTHs = np.zeros(
(len(self.labels), int(self.simtime / self.bin_size)))
# IDs from NEST
self.layers_to_record = [] # For membrane potentials
# Data recorders used in NEST simulation
self.potentials = []
self.spikes = []
# Retina references
self.retina_labels = [
'Midget_ganglion_cells_L_ON', 'Midget_ganglion_cells_L_OFF',
'Midget_ganglion_cells_M_ON', 'Midget_ganglion_cells_M_OFF'
]
# Receptive_field
self.RF_mask = []
self.RF_bright = np.zeros(
(len(self.RF_intervals), len(self.labels), N_r, N_r))
self.RF_dark = np.zeros(
(len(self.RF_intervals), len(self.labels), N_r, N_r))
def __init__(self):
## Simulation parameters ##
# Simulation time
self.simtime = 1000.0
# Simulation object
self.newSimulation = run_network.runNetwork(self.simtime)
# Number of trials
self.trials = 2
# Folder to save spike times
self.spike_folder = 'Luminance_Grating'
# Grating parameters
# self.spatial_frequency = np.array([0.1,0.2,0.3,0.4,0.5,1.0,1.5,2.0,2.5,3.0,3.5,4.0,4.5,5.0,5.5,6.0]) # cpd
self.spatial_frequency = np.array([1.0, 3.0]) # cpd
self.temporal_frequency = 2.0 # Hz
# Start time of plots
self.start_time = 200.0
# Record membrane potentials
self.record_Vm = False
# Cell to analyze
self.selected_cell = []
# Cell to analyze is the center cell in every 2D grid
self.isCenterCell = True
# size of each layer (number of cells)
N_r = self.newSimulation.Params['N_LGN']
N_c = self.newSimulation.Params['N_cortex']
# PSTH bin size
self.bin_size = 10.0 # ms
# Layers to track
self.labels = [
'Parvo_LGN_relay_cell_L_ON',
# 'Parvo_LGN_relay_cell_L_OFF',
'Parvo_LGN_relay_cell_M_ON',
# 'Parvo_LGN_relay_cell_M_OFF',
# 'Parvo_LGN_interneuron_ON',
# 'Parvo_LGN_interneuron_OFF',
'Color_Luminance_L_ON_L_OFF_vertical',
'Color_Luminance_L_ON_L_OFF_horizontal',
'Color_Luminance_L_OFF_L_ON_vertical',
# 'Color_Luminance_L_OFF_L_ON_horizontal',
'Color_Luminance_M_ON_M_OFF_vertical',
# 'Color_Luminance_M_ON_M_OFF_horizontal',
'Color_Luminance_M_OFF_M_ON_vertical',
# 'Color_Luminance_M_OFF_M_ON_horizontal',
'Luminance_preferring_ON_OFF_vertical',
'Luminance_preferring_ON_OFF_horizontal',
'Luminance_preferring_OFF_ON_vertical',
'Luminance_preferring_OFF_ON_horizontal',
'Color_preferring_L_ON_M_OFF',
'Color_preferring_M_ON_L_OFF',
'Color_Luminance_inh_L_ON_L_OFF_vertical',
# 'Color_Luminance_inh_L_ON_L_OFF_horizontal',
# 'Color_Luminance_inh_L_OFF_L_ON_vertical',
# 'Color_Luminance_inh_L_OFF_L_ON_horizontal',
# 'Color_Luminance_inh_M_ON_M_OFF_vertical',
# 'Color_Luminance_inh_M_ON_M_OFF_horizontal',
# 'Color_Luminance_inh_M_OFF_M_ON_vertical',
# 'Color_Luminance_inh_M_OFF_M_ON_horizontal',
'Luminance_preferring_inh_ON_OFF_vertical'
# 'Luminance_preferring_inh_ON_OFF_horizontal',
# 'Luminance_preferring_inh_OFF_ON_vertical',
# 'Luminance_preferring_inh_OFF_ON_horizontal',
# 'Color_preferring_inh_L_ON_M_OFF',
# 'Color_preferring_inh_M_ON_L_OFF'
]
# FFT labels
self.FFT_labels = [
'Parvo_LGN_relay_cell_L_ON',
# 'Parvo_LGN_relay_cell_L_OFF',
'Parvo_LGN_relay_cell_M_ON',
# 'Parvo_LGN_relay_cell_M_OFF',
# 'Parvo_LGN_interneuron_ON',
# 'Parvo_LGN_interneuron_OFF',
'Color_Luminance_L_ON_L_OFF_vertical',
'Color_Luminance_L_ON_L_OFF_horizontal',
'Color_Luminance_L_OFF_L_ON_vertical',
# 'Color_Luminance_L_OFF_L_ON_horizontal',
'Color_Luminance_M_ON_M_OFF_vertical',
# 'Color_Luminance_M_ON_M_OFF_horizontal',
'Color_Luminance_M_OFF_M_ON_vertical',
# 'Color_Luminance_M_OFF_M_ON_horizontal',
'Luminance_preferring_ON_OFF_vertical',
'Luminance_preferring_ON_OFF_horizontal',
'Luminance_preferring_OFF_ON_vertical',
'Luminance_preferring_OFF_ON_horizontal',
'Color_preferring_L_ON_M_OFF',
'Color_preferring_M_ON_L_OFF',
'Color_Luminance_inh_L_ON_L_OFF_vertical',
# 'Color_Luminance_inh_L_ON_L_OFF_horizontal',
# 'Color_Luminance_inh_L_OFF_L_ON_vertical',
# 'Color_Luminance_inh_L_OFF_L_ON_horizontal',
# 'Color_Luminance_inh_M_ON_M_OFF_vertical',
# 'Color_Luminance_inh_M_ON_M_OFF_horizontal',
# 'Color_Luminance_inh_M_OFF_M_ON_vertical',
# 'Color_Luminance_inh_M_OFF_M_ON_horizontal',
'Luminance_preferring_inh_ON_OFF_vertical'
# 'Luminance_preferring_inh_ON_OFF_horizontal',
# 'Luminance_preferring_inh_OFF_ON_vertical',
# 'Luminance_preferring_inh_OFF_ON_horizontal',
# 'Color_preferring_inh_L_ON_M_OFF',
# 'Color_preferring_inh_M_ON_L_OFF'
]
## Graphical parameters ##
self.plot_intracellular = False
self.plot_PSTH = False
# Individual intracellular traces
self.intracellular_rows = 4
self.intracellular_cols = 5
self.intracellular_starting_row = 0
self.intracellular_starting_col = 0
# PSTHs
self.PSTH_rows = 4
self.PSTH_cols = 5
self.PSTH_starting_row = 0
self.PSTH_starting_col = 0
# FFT plot
self.FFT_rows = 3
self.FFT_cols = 5
## End of parameters ##
# Initialize PSTH
self.PSTHs = np.zeros(
(len(self.labels), int(self.simtime / self.bin_size)))
# IDs from NEST
self.layers_to_record = []
# Data recorders used in NEST simulation
self.potentials = []
self.spikes = []
# Retina references
self.retina_labels = [
'Midget_ganglion_cells_L_ON', 'Midget_ganglion_cells_L_OFF',
'Midget_ganglion_cells_M_ON', 'Midget_ganglion_cells_M_OFF',
'Small_bistratified_ganglion_cells_S_ON'
]
# FFT
self.FFTamp = np.zeros(
(len(self.FFT_labels), len(self.spatial_frequency)))
self.FFTph = np.zeros(
(len(self.FFT_labels), len(self.spatial_frequency)))
self.FFT_recorded_models = []
self.FFT_PSTH_index = []