## characterize_lms_sf

This analysis produces an estimate of cone weights based on three spatial frequency tuning curves collected using cone isolating drifting grating stimuli.

## Options:

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
## Parameter definitions:

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