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====================== USAGE ====================== To compile: user$>make To run: user$>./threaded [config_file] [output_filename] OR user$>./unthreaded [config_file] [output_filename] The executable "threaded" and "unthreaded" differ exactly how their names imply; "threaded" is a threaded version of "unthreaded." If config_file is not specified, template.txt is used. If output_filename is given, the state of the network at the end of all calculations is output to output_filename as a "defined" type network. ====================== CONFIG FILE ====================== @NETWORK_SIZE [long number_of_neurons_in_network] [long number_of_compartments_per_neuron] @RUNTIME [double simulation_start_time] [double simulation_start_time] @PARAMS [int number_of_global_params] [char* param_name] [double param_value] ... @STIMULI [long num_neuron] [long num_compartment] [int 0_for_electrode_current_or_1_for_presynaptic_input] [double from_time] [double to_time] [double electrode_current] ... @TETANUS [long num_neuron] [long num_compartment] [int direct?] [double start_time] [double stimulus_length] [double periodicity] [double stimulus_current] @DEFAULT_STATE [long compartment_number] [int number_of_params_for_each_compartment] [char * param_name] [ double param_value] ... (multiple [@DEFAULT_STATE long compartment_number] for multiple compartments' default states) @DEFAULT_LINK_STATE [long num_variables] [char* param_name] [double param_value] ... @INIT_STATE [long number_of_neurons_with_differing_initial_states] [long neuron_id] [long compartment_number_of_neuron] [double* list_of_parameter_values_in_same_order_as_in_@DEFAULT_STATE] ... @LINKS [total | random | defined] [ if total: [double initial weight] [default conductance time] if random: [double probability two neurons are connected] [0|1 random weights?] [default weight] [0|1 random ctimes?] [default ctime] if defined: [long from_neuron_number] [long from_neuron_compartment_number] [long to_neuron_number] [long to_neuon_compartment_number] [double weight] [conductance_time] ... ]
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Simulation of LTP/LTD in neural networks
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