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):
  1. name - input file name given on command line
  2. r_Component - component index
  3. r_Pattern - pattern index
  4. z_Component - Fisher-Z for r_Pattern
  5. 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