SFN 2013 note

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.