#!/usr/bin/python # # The following script replicates the results of Rong et al (2011). # # We have two types of stimuli: Tonal Contours (TC) and Tones # We have two conditions: Delayed Match to Sample (DMS) and passive listening (PSL) # # The script contains an experiment with the following "sessions": # # (1) TCs during PSL # (2) Tones during PSL # (3) TCs during DMS # (4) Tones during PSL # # We have a total of 4 trials in each session # # The number of timesteps in each trial is 740 = 3.7 seconds # # The total number of timesteps is 11840 = 59.2 seconds # # Each session is 2960 timesteps = 14.8 seconds # # Trials contained in DMS each session are ordered as follows: MATCH, MISMATCH, MISMATCH, MATCH. # # The attention parameter in DMS trials is constant at 0.3 # # The PSL trials constitute control: low attention (0.05) is used in these trials # # To maintain consistency with Husain et al (2004) and Tagamets and Horwitz (1998), # we are assuming that each simulation timestep is equivalent to 5 milliseconds # of real time. # # We present stimuli to the auditory LSNM network by manually inserting it into the LGN module # and leaving the stimuli there for 100 timesteps (500 ms). # define the simulation time in total number of timesteps LSNM_simulation_time = 11840 # Define list of parameters the script is going to need to modify the LSNM neural network # They are organized in the following order: # [lo_att_level, hi_att_level, lo_inp_level, hi_inp_level, att_step, ri1, ri2] script_params = [0.05, 0.3, 0.0, 1.0, 0.0, [], []] def s1_up_01(modules, script_params): """ generates an up sweep to neural network using the given parameters """ modules['atts'][8][0][0][0] = script_params[1] # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][42][0] = script_params[3] modules['mgns'][8][0][43][0] = script_params[3] def s1_up_02(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # reset previous activation modules['mgns'][8][0][42][0] = 0. # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][43][0] = script_params[3] modules['mgns'][8][0][44][0] = script_params[3] def s1_up_03(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # reset previous activation modules['mgns'][8][0][43][0] = 0. # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][44][0] = script_params[3] modules['mgns'][8][0][45][0] = script_params[3] def s1_up_04(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # reset previous activation modules['mgns'][8][0][44][0] = 0. # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][45][0] = script_params[3] modules['mgns'][8][0][46][0] = script_params[3] def s1_up_05(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][45][0] = script_params[3] modules['mgns'][8][0][46][0] = script_params[3] def s1_down_01(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][46][0] = script_params[3] modules['mgns'][8][0][45][0] = script_params[3] def s1_down_02(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][46][0] = script_params[3] modules['mgns'][8][0][45][0] = script_params[3] def s1_down_03(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # reset previous activation modules['mgns'][8][0][46][0] = 0. # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][45][0] = script_params[3] modules['mgns'][8][0][44][0] = script_params[3] def s1_down_04(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # reset previous activation modules['mgns'][8][0][45][0] = 0. # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][44][0] = script_params[3] modules['mgns'][8][0][43][0] = script_params[3] def s1_down_05(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # reset previous activation modules['mgns'][8][0][44][0] = 0. # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][43][0] = script_params[3] modules['mgns'][8][0][42][0] = script_params[3] def s2_down_01(modules, script_params): """ generates an up sweep to neural network using the given parameters """ modules['atts'][8][0][0][0] = script_params[1] # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][58][0] = script_params[3] modules['mgns'][8][0][57][0] = script_params[3] def s2_down_02(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # reset previous activation modules['mgns'][8][0][58][0] = 0. # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][57][0] = script_params[3] modules['mgns'][8][0][56][0] = script_params[3] def s2_down_03(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # reset previous activation modules['mgns'][8][0][57][0] = 0. # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][56][0] = script_params[3] modules['mgns'][8][0][55][0] = script_params[3] def s2_down_04(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # reset previous activation modules['mgns'][8][0][56][0] = 0. # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][55][0] = script_params[3] modules['mgns'][8][0][54][0] = script_params[3] def s2_down_05(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][55][0] = script_params[3] modules['mgns'][8][0][54][0] = script_params[3] def s2_up_01(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][54][0] = script_params[3] modules['mgns'][8][0][55][0] = script_params[3] def s2_up_02(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][55][0] = script_params[3] modules['mgns'][8][0][54][0] = script_params[3] def s2_up_03(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # reset previous activation modules['mgns'][8][0][54][0] = 0. # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][55][0] = script_params[3] modules['mgns'][8][0][56][0] = script_params[3] def s2_up_04(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # reset previous activation modules['mgns'][8][0][55][0] = 0. # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][56][0] = script_params[3] modules['mgns'][8][0][57][0] = script_params[3] def s2_up_05(modules, script_params): """ generates an up sweep to neural network using the given parameters """ # reset previous activation modules['mgns'][8][0][56][0] = 0. # insert the inputs stimulus into MGN and see what happens # the following stimulus is an up sweep modules['mgns'][8][0][57][0] = script_params[3] modules['mgns'][8][0][58][0] = script_params[3] def delay_period(modules, script_params): """ modifies neural network with delay period parameters given """ # turn off input stimulus but leave small level of activity there for x in range(modules['mgns'][0]): for y in range(modules['mgns'][1]): modules['mgns'][8][x][y][0] = 0. def intertrial_interval(modules, script_params): """ resets the visual inputs and short-term memory using given parameters """ # reset D1 for x in range(modules['efd1'][0]): for y in range(modules['efd1'][1]): modules['efd1'][8][x][y][0] = script_params[2] # turn off input stimulus but leave small level of activity there for x in range(modules['mgns'][0]): for y in range(modules['mgns'][1]): modules['mgns'][8][x][y][0] = script_params[2] # turn attention to 'LO', as the current trial has ended modules['atts'][8][0][0][0] = script_params[0] # define a dictionary of simulation events functions, each associated with # a specific simulation timestep simulation_events = { # REST BLOCK OF 200 TIMESTEPS '0' : intertrial_interval, ####### FIRST BLOCK OF 4 DMS TRIALS (MATCH, MISMATCH, MISMATCH, MATCH) '100': s1_up_01, '110': s1_up_02, '120': s1_up_03, '130': s1_up_04, '140': s1_up_05, '150': s1_down_01, '160': s1_down_02, '170': s1_down_03, '180': s1_down_04, '190': s1_down_05, '200': delay_period, '400': s2_down_01, '410': s2_down_02, '420': s2_down_03, '430': s2_down_04, '440': s2_down_05, '450': s2_up_01, '460': s2_up_02, '470': s2_up_03, '480': s2_up_04, '490': s2_up_05, '500': intertrial_interval, '900': s1_up_01, '910': s1_up_02, '920': s1_up_03, '930': s1_up_04, '940': s1_up_05, '950': s1_down_01, '960': s1_down_02, '970': s1_down_03, '980': s1_down_04, '990': s1_down_05, '1000': delay_period, '1200': s1_up_01, '1210': s1_up_02, '1220': s1_up_03, '1230': s1_up_04, '1240': s1_up_05, '1250': s1_down_01, '1260': s1_down_02, '1270': s1_down_03, '1280': s1_down_04, '1290': s1_down_05, '1300': intertrial_interval, ################### END OF DMS BLOCK #################################### } ##- EoF -##