#!/usr/bin/env python
'''Run a 1-variable sweep in which I->I connections are enabled.
Run a network for a short time; save data and plot activity.
'''
from __future__ import absolute_import, print_function, division
from grid_cell_model.submitting.noise.templates import ParameterSweep
from default_params import defaultParameters as dp
sweep = ParameterSweep('../common/simulation_stationary.py', dp)
parser = sweep.parser
parser.add_argument('--nthreads', type=int, default=1,
help='Number of simulation threads.')
parser.add_argument('--Ivel', type=float,
help='Velocity input (pA). Default is 50 pA.')
parser.add_argument('--g_AMPA_total', type=float,
help='Total E->I synapse strength (nS)')
parser.add_argument('--g_GABA_total', type=float,
help='Total I->E synapse strength (nS)')
o = parser.parse_args()
p = {}
p['master_seed'] = 123456
p['time'] = 10e3 if o.time is None else o.time # ms
p['nthreads'] = o.nthreads
p['verbosity'] = o.verbosity
p['Ivel'] = 50. if o.Ivel is None else o.Ivel # mA
p['use_II'] = 1
#!/usr/bin/env python
'''Parameter sweeps of E-surround network in which connections are generated
probabilistically with constant weight.
2D parameter sweep that simulates a stationary bump and records spiking
activity and synaptic currents from selected neurons.'''
from __future__ import absolute_import, print_function, division
from grid_cell_model.submitting.noise.templates import ParameterSweep
from default_params import defaultParameters as dp
sweep = ParameterSweep('../common/simulation_stationary.py', dp)
p = {}
p['master_seed'] = 123456
p['probabilistic_synapses'] = 1
sweep.update_user_parameters(p)
sweep.run()
for the duration of the simulation.
- The speed is controlled by IvelMax and dIvel parameters (currently
[0, 100] pA, with a step of 10 pA
- At the end of each run, spikes from E and I population are exported to
the output file.
.. note::
When simulating on a machine with a run time limit, use a limit around
08h:00:00
'''
from __future__ import absolute_import, print_function, division
from grid_cell_model.submitting.noise.templates import ParameterSweep
from default_params import defaultParameters as dp
sweep = ParameterSweep('simulation_velocity.py', dp)
p = {}
p['master_seed'] = 123456
p['EI_flat'] = 0
p['IE_flat'] = 0
p['use_EE'] = 1
p['g_EE_total'] = 510. # nS
p['pEE_sigma'] = 0.5 / 6
p['IvelMax'] = 100 # pA
p['dIvel'] = 10 # pA
sweep.update_user_parameters(p)
sweep.run()
- The bump moves around and (should) track the position of the animal.
- Spikes from the whole E population and some cells from the I population
are then exported to the output file.
.. note::
When simulating on a machine with a run time limit, use a limit around
08h:00:00 or more.
'''
from __future__ import absolute_import, print_function, division
import numpy as np
from grid_cell_model.submitting.noise.templates import ParameterSweep
from grid_cell_model.submitting.noise.slopes import ISurroundPastollSelector
from default_params import defaultParameters as dp
sweep = ParameterSweep('simulation_grids.py', dp)
parser = sweep.parser
parser.add_argument('--pc_conn_weight', type=float, help='Connection weight from PCs to E cells (pA).')
parser.parse_args()
sweep.set_bump_slope_selector(ISurroundPastollSelector('bump_slope_data',
-np.infty))
p = {}
p['master_seed'] = 123456
if parser.options.pc_conn_weight is not None:
p['pc_conn_weight'] = parser.options.pc_conn_weight
p['Iext_e_theta'] = 650.
p['Iext_i_theta'] = 50.
p['g_uni_GABA_frac'] = 0.3125
strong place cell input.
- The network receives velocity input for the rest of the simulation.
- The bump moves around and (should) track the position of the animal.
- Spikes from the whole E population and some cells from the I population
are then exported to the output file.
.. note::
When simulating on a machine with a run time limit, use a limit around
08h:00:00
'''
from __future__ import absolute_import, print_function, division
import numpy as np
from grid_cell_model.submitting.noise.templates import ParameterSweep
from grid_cell_model.submitting.noise.slopes import ProbabilisticConnectionsSelector
from default_params import defaultParameters as dp
sweep = ParameterSweep('simulation_grids.py', dp)
sweep.set_bump_slope_selector(
ProbabilisticConnectionsSelector('bump_slope_data', -np.infty))
p = {}
p['master_seed'] = 123456
p['velON'] = 1
p['pcON'] = 1
p['constantPosition'] = 0
p['probabilistic_synapses'] = 1
sweep.update_user_parameters(p)
sweep.run()
- The bump moves around and (should) track the position of the animal.
- Spikes from the whole E population and some cells from the I population
are then exported to the output file.
.. note::
When simulating on a machine with a run time limit, use a limit around
08h:00:00 or more (per trial).
'''
from __future__ import absolute_import, print_function, division
import numpy as np
from grid_cell_model.submitting.noise.templates import ParameterSweep
from grid_cell_model.submitting.noise.slopes import PickedISurroundPastollSelector
from default_params import defaultParameters as dp
sweep = ParameterSweep('simulation_grids.py', dp)
parser = sweep.parser
parser.add_argument('--g_AMPA_total', type=float)
parser.add_argument('--g_AMPA_row', type=float, help='Row index into bump slope data.')
parser.add_argument('--g_GABA_total', type=float)
parser.add_argument('--g_GABA_col', type=float, help='Column index into bump slope data.')
parser.add_argument('--velON', type=int, choices=[0, 1], default=1, help='Set velocity input ON or OFF.')
parser.parse_args()
o = parser.options
sweep.set_bump_slope_selector(PickedISurroundPastollSelector(
'bump_slope_data',
-np.infty,
o.g_AMPA_row,
o.g_GABA_col,
parser.dimensions))
- The bump moves around and (should) track the position of the animal.
- Spikes from the whole E population and some cells from the I population
are then exported to the output file.
.. note::
When simulating on a machine with a run time limit, use a limit around
08h:00:00
'''
from __future__ import absolute_import, print_function, division
import numpy as np
from grid_cell_model.submitting.noise.templates import ParameterSweep
from grid_cell_model.submitting.noise.slopes import IIConnectionsSelector
from default_params import defaultParameters as dp
sweep = ParameterSweep('simulation_grids.py', dp)
sweep.set_bump_slope_selector(IIConnectionsSelector('bump_slope_data',
-np.infty))
p = {}
p['master_seed'] = 123456
p['velON'] = 1
p['pcON'] = 1
p['constantPosition'] = 0
p['use_II'] = 1
p['g_II_total'] = 70. # nS
sweep.update_user_parameters(p)
sweep.run()
- Bump is initialized, in the beginning [0, theta_start_t], by a very
strong place cell input.
- The network receives velocity input for the rest of the simulation.
- The bump moves around and (should) track the position of the animal.
- Spikes from the whole E population and some cells from the I population
are then exported to the output file.
.. note::
When simulating on a machine with a run time limit, use a limit around
08h:00:00
"""
from __future__ import absolute_import, print_function, division
import numpy as np
from grid_cell_model.submitting.noise.templates import ParameterSweep
from grid_cell_model.submitting.noise.slopes import ProbabilisticConnectionsSelector
from default_params import defaultParameters as dp
sweep = ParameterSweep("simulation_grids.py", dp)
sweep.set_bump_slope_selector(ProbabilisticConnectionsSelector("bump_slope_data", -np.infty))
p = {}
p["master_seed"] = 123456
p["velON"] = 1
p["pcON"] = 1
p["constantPosition"] = 0
p["probabilistic_synapses"] = 1
sweep.update_user_parameters(p)
sweep.run()
#!/usr/bin/env python
'''Parameter sweeps of network with extra E->E connections and flat E-I
profiles.
2D parameter sweep that simulates a stationary bump and records spiking
activity and synaptic currents from selected neurons.'''
from __future__ import absolute_import, print_function, division
from grid_cell_model.submitting.noise.templates import ParameterSweep
from default_params import defaultParameters as dp
sweep = ParameterSweep('../common/simulation_stationary.py', dp)
parser = sweep.parser
parser.add_argument('--g_AMPA_total', type=float,
help='Strength of E->I synapses')
parser.add_argument('--g_GABA_total', type=float,
help='Strength of I->E synapses')
parser.add_argument('--g_EE_total', type=float,
help='Strength of E->E synapses')
parser.add_argument('--pEE_sigma', type=float, help='Width of E->E synapses')
sweep.parse_args()
o = sweep.options
p = {}
p['master_seed'] = 123456
p['EI_flat'] = 1
p['IE_flat'] = 1
p['use_EE'] = 1
if o.g_AMPA_total is not None:
- The network receives velocity input for the rest of the simulation.
- The bump moves around and (should) track the position of the animal.
- Spikes from the whole E population and some cells from the I population
are then exported to the output file.
.. note::
When simulating on a machine with a run time limit, use a limit around
08h:00:00 or more.
'''
from __future__ import absolute_import, print_function, division
import numpy as np
from grid_cell_model.submitting.noise.templates import ParameterSweep
from grid_cell_model.submitting.noise.slopes import ISurroundOrigSelector
from default_params import defaultParameters as dp
sweep = ParameterSweep('simulation_grids.py', dp)
sweep.set_bump_slope_selector(ISurroundOrigSelector('bump_slope_data',
-np.infty))
p = {}
p['master_seed'] = 123456
p['velON'] = 1
p['pcON'] = 1
p['constantPosition'] = 0
p['AMPA_gaussian'] = 1
sweep.update_user_parameters(p)
sweep.run()