#!/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()
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()