miv_simulator.clamps.network#

Routines for Network Clamp simulation.

Functions

`dist_ctrl`(controller, init_params, ...)	Controller for distributed network clamp runs.
`dist_run`(init_params, cell_index_set[, ...])	Initialize workers for distributed network clamp runs.
`generate_weights`(env, weight_source_rules, ...)	Generates synaptic weights according to the rules specified in the Weight Generator section of network clamp configuration.
`go`(config_file, config_prefix, population, ...)	Runs network clamp simulation for the specified gid, or for all gids found in the input data file.
`init`(env, pop_name, cell_index_set[, ...])	Instantiates a cell and all its synapses and connections and loads or generates spike times for all synaptic connections.
`init_inputs_from_features`(env, ...[, ...])	Initializes presynaptic spike sources from a file with input selectivity features represented as firing rates.
`init_inputs_from_spikes`(env, presyn_sources, ...)	Initializes presynaptic spike sources from a file with spikes times.
`init_rate_dist_objfun`(config_file, ...)
`init_rate_objfun`(config_file, population, ...)
`init_state_objfun`(config_file, population, ...)
`mpi_excepthook`(type, value, traceback)	type type:
`optimize`(config_file, config_prefix, ...)	Optimize the firing rate of the specified cell in a network clamp configuration.
`optimize_run`(env, pop_name, ...[, ...])
`run`(env[, cvode, pc_runworker])	Runs network clamp simulation.
`run_with`(env, param_dict[, cvode, pc_runworker])	Runs network clamp simulation with the specified parameters for the given gid(s).
`set_union`(s, t, datatype)
`show`(config_file, config_prefix, population, ...)	Show configuration for the specified cell.
`update_params`(env, pop_param_dict)
`write_output`(env)
`write_params`(env, pop_params_dict)

miv_simulator.clamps.network.mpi_excepthook(type, value, traceback)[source]#

Parameters:

type –
value –
traceback –

Returns:

miv_simulator.clamps.network.generate_weights(env, weight_source_rules, this_syn_attrs)[source]#: Generates synaptic weights according to the rules specified in the Weight Generator section of network clamp configuration.

miv_simulator.clamps.network.init_inputs_from_spikes(env, presyn_sources, time_range, spike_events_path, spike_events_namespace, arena_id, stimulus_id, spike_train_attr_name='t', n_trials=1)[source]#: Initializes presynaptic spike sources from a file with spikes times.

miv_simulator.clamps.network.init_inputs_from_features(env, presyn_sources, time_range, input_features_path, input_features_namespaces, arena_id, stimulus_id, phase_mod=False, soma_positions_dict=None, spike_train_attr_name='t', n_trials=1, seed=None)[source]#: Initializes presynaptic spike sources from a file with input selectivity features represented as firing rates.

miv_simulator.clamps.network.init(env, pop_name, cell_index_set, arena_id=None, stimulus_id=None, n_trials=1, spike_events_path=None, spike_events_namespace='Spike Events', spike_train_attr_name='t', input_features_path=None, input_features_namespaces=None, coords_path=None, distances_namespace='Arc Distances', phase_mod=False, generate_weights_pops={}, t_min=None, t_max=None, write_cell=False, plot_cell=False, input_seed=None, cooperative_init=False, worker=None)[source]#

Instantiates a cell and all its synapses and connections and loads or generates spike times for all synaptic connections.

Parameters:

env – an instance of env.Env
pop_name – population name
gid_set – cell gids
spike_events_path –

miv_simulator.clamps.network.run(env, cvode=False, pc_runworker=False)[source]#

Runs network clamp simulation. Assumes that procedure init has been called with the network configuration provided by the env argument.

Parameters:

env – instance of env.Env
cvode – whether to use adaptive integration

miv_simulator.clamps.network.run_with(env, param_dict, cvode=False, pc_runworker=False)[source]#

Runs network clamp simulation with the specified parameters for the given gid(s). Assumes that procedure init has been called with the network configuration provided by the env argument.

Parameters:

env – instance of env.Env
param_dict – dictionary { gid: params }
cvode – whether to use adaptive integration

miv_simulator.clamps.network.dist_ctrl(controller, init_params, cell_index_set, param_path, pop_param_tuple_dicts)[source]#: Controller for distributed network clamp runs.

miv_simulator.clamps.network.dist_run(init_params, cell_index_set, results_file_id=None, pop_param_tuple_dict=None)[source]#: Initialize workers for distributed network clamp runs.

miv_simulator.clamps.network.show(config_file, config_prefix, population, gid, arena_id, stimulus_id, template_paths, dataset_prefix, results_path, spike_events_path, spike_events_namespace, spike_events_t, input_features_path, input_features_namespaces, use_coreneuron, plot_cell, write_cell, profile_memory, recording_profile)[source]#: Show configuration for the specified cell.

miv_simulator.clamps.network.go(config_file, config_prefix, population, dt, gids, gid_selection_file, arena_id, stimulus_id, generate_weights, t_max, t_min, template_paths, dataset_prefix, spike_events_path, spike_events_namespace, spike_events_t, coords_path, distances_namespace, phase_mod, input_features_path, input_features_namespaces, n_trials, params_path, params_id, results_path, results_file_id, results_namespace_id, use_coreneuron, plot_cell, write_cell, profile_memory, recording_profile, input_seed)[source]#: Runs network clamp simulation for the specified gid, or for all gids found in the input data file.

miv_simulator.clamps.network.optimize(config_file, config_prefix, population, dt, gids, gid_selection_file, arena_id, stimulus_id, generate_weights, t_max, t_min, nprocs_per_worker, opt_epsilon, opt_seed, opt_iter, template_paths, dataset_prefix, param_config_name, param_type, recording_profile, results_file, results_path, spike_events_path, spike_events_namespace, spike_events_t, coords_path, distances_namespace, phase_mod, input_features_path, input_features_namespaces, n_trials, trial_regime, problem_regime, target_features_path, target_features_namespace, target_state_variable, target_state_filter, use_coreneuron, cooperative_init, target)[source]#: Optimize the firing rate of the specified cell in a network clamp configuration.