characterize_lms_sfThis analysis produces an estimate of cone weights based on three spatial frequency tuning curves collected using cone isolating drifting grating stimuli.
- plot - the output file will contain DC, F1 and phase tuning curves for the three cone isolating stimulus sequences.
and write the following parameters to the statistics file (listed by column number):
- name - input file name given on command line
- r_Component - component index
- r_Pattern - pattern index
- z_Component - Fisher-Z for r_Pattern
- z_Pattern - Fisher-Z for r_Component
- lms_sf_val - If a non-negative value is given, then this will be the SF value at which the cone weights are determined. Otherwise, -1 indicates that the response is to be taken at the SF that has the maximum response across all three SF curves, whereas -2 indicates that the responses should be taken at the individual maxima for each curve.
- f1_param - Specifies the name of the parameter indicating the stimulus temporal frequency.
Example:# # lms_sf.nda # characterize_lms_sf group 2 cone sf plot chan unit0 start 100 period 3900 f1_param tf baseline0 groupdef contrast 0 # # These parameters control the computation of the normalized cone weights # stim_contrast_l 10.0 # If > 0, will divide raw L response before norm stim_contrast_m 10.0 # If > 0, will divide raw M response before norm stim_contrast_s 10.0 # If > 0, will divide raw S response before norm lms_stat_file zz.cw.stat # File name to append lms_norm_flag 2 # 3-Norm by L+M+S; 2-Norm by L+M; [Default 3] lms_sf_val -1.0 # SF value; -1.0 for overall max; -2.0 separate max condition param_range contrast 0.01 1.1