To further explore the neural code underlying putatively different types of information (identification vs categorization) expressed by the same population of neurons we used a standard variable selection technique (see Methods). Here we show the SNR for identification (y-axis) as a function of the SNR for categorization (x-axis) for each site (see below for definition of SNR). The black dashed line shows the diagonal y = x and the red dashed line represents a linear fit to the data (r = 0.54). The bottom follows the same format and corresponds to the data exploring invariance to object scale and position changes (r = 0.46).
Parameters
Signal = MUA, Time interval = [100;300) ms, Bin size = 200 ms, Total number
of sites = 367
The signal-to-noise ratio was defined as:
where:
sSNRg is the signal-to-noise ratio for site s and group/picture g
<s_r_g> and s_s_g is the mean
and standard deviation of the response of site s to group/picture g
<s_r_notg> and s_s_notg is
the mean and standard deviation of the response of site s to stimuli
not in group/picture g