120.4 Ress &
Greene used a back projection method and sweeping method to provide a model
free method to estimate pRF. That is, they used a wenier kernel to estimate HRF
from data, and then used back projection to decompose BOLD data into pRF. They
then fit the pRF with an ellipse. They showed that pRF is only mildly elongated
(aspect ration <1.3) and has weak suppression zone. Hence, the traditional
Gaussian model for pRF is basically right.
120.5 The cortical
reorganized itself after scotoma. Such recovery may reflect in the change of
pRF in lesion projection zone. Brewer, however, used a nature scotoma to study
the effect of scotoma on pRF. That is, the rod is absent in the fovea. She showed
that the pRF for Scotopic was shifted compared with that for the photopic. The
activity for scotopic is also weaker.
** 120.10 Petridou
at al. used a methodology derived from pRF to show that numerosity is
topographically mapped in the parietal cortex. Their model is that the neural
response in the parietal cortex increased with the number of elements. Hence, They then convolved this linear function with
HRF to derived the model prediction and fit the model to the data. In this way,
they can calculate the "numerosity" tuning of each voxel, and thus
determined the topogrphic map.
120.12 & 120.13
These two studies investigated what MVPA can do and cannot do. The MVPA is best
to use when the response is relatively homogeneous in the ROI. MVPA cannot
"access sparse, non-clustered neuronal responses".
259.17 Curtis Baker
found three types of simple cells. One without orientation tuning but respond
nonlinearly to contrast. The other two are orientation tuned. One of the oriented cell has an expansive
while the other one, compressive. They (359.15) further suggested that the
non-oriented cell may be responsible for second-order processing.
311.07 Use the
voltage sensitive dye technique, Slovin et al. is able to image the population
neural response with a much better spatial resolution than ordinary optical
imaging methods. In Gilad & Solvin, they compared the V1 response to two
lines joined together as one feature by an extra line and to two separated
lines. They showed that the v1 response to one of the two separate lines are
greater than the same line that belong to one big feature. Thus, they concluded
that V1 encoded different features by activation strength.
The same group also
compared the imaged response to a black and a white squared. They showed a
response bias toward dark. They proposed a linear-nonlinear-linear model with
on, off, and edge receptive fields with a weighting 0.1:0.22:1. They were able
to fit the response map with such model.
358.14 the position
of v1 rf shift to reflect perceived object size in corridor illusion.
555.02 Kay et al
from Wandell's group showed a two-stage model to fit fmri response to a
stimulus. Their model is an LNL model. The BOLD activation is proportional to
the second order response.
555.21 Komatsu et
al. measured color selectivity at low and high luminance in V4, AIT and PIT.
They showed that the color selectivity in V4 changes with luminance while that
in PIT is invariant to luminance. Thus, V4 neurons should carry both luminance
and color information. Only neurons in AIT are true color selective and is
invariant to luminance.
The Presidential
lecture was given by Dorothy Tsao, who showed that there are six areas in the
monkey's temporal cortex for face processing. Among them, the anterior areas
are view point invariant while the posterior one depends on viewpoints.
