#!/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()