vlt.neuro.vision.speed.tuningfunc
vlt.neuro.vision.speed.tuningfunc - produce responses given a speed tuning function
R = vlt.neuro.vision.speed.tuningfunc(SF,TF,P)
Produce the responses at given spatial (SF) and temporal (TF) frequencies
given parameters for a speed tuning curve.
Inputs:
SF is an array of spatial frequency values
TF is an array of temporal frequency values
P is an array with parameters:
---------------------------------------------
| A | Peak response of the neuron |
| | |
| zeta | Skew of the temporal |
| | frequency tuning curve |
| | |
| xi | Speed parameter |
| | |
| sigma_sf | Tuning width of the neuron |
| | for spatial frequency |
| | |
| sigma_tf | Tuning width of the neuron |
| | for temporal frequency |
| | |
| sf0 | Preferred spatial frequency |
| | averaged across temporal |
| | frequencies |
| | |
| tf0 | Preferred temporal frequency |
| | averaged across spatial |
| | frequencies |
---------------------------------------------
Outputs:
R is an array of calculated responses
Example:
[SF,TF] = meshgrid([0.05 0.08 0.1 0.2 0.4 0.8 1.2],[0.5 1 2 4 8 16 32]);
% Pick some parameters
A = 1;
zeta = 0;
xi = 0;
sigma_sf = 0.2; % Cycles per degree
sigma_tf = 4; % Cycles per second; this is the fall off
sf0 = 0.1;
tf0 = 4;
% Now calculate the responses
R = speed.tuningfunc(SF,TF,[A zeta xi sigma_sf sigma_tf sf0 tf0]);
% Now plot the responses
figure;
speed.plottuning(SF,TF,R);
See: Priebe et al. 2006