SFN 2014 Note

155.09 Smith et al., from Fitzpatrick's lab, used optical image to test the development of cortical maps of ferrets at different stages of development. They found that the on-off map was developed first before the orientation map after eye opening.

155.18. Hermes et al used Kay's model for BOLD response to model ECoG activation. Their idea was that ECoG measured the field potential which is highly correlated with BOLD in fMRI. Thus, they can use a model for BOLD for ECoG.

236.13-16 A series of posters on T. Lee's lab used images with scene context to measure the RF properties of V2 neurons and model their distributed representation.

236.18 Nandy et al. showed that the sharpness of shape tuning is of a V4 cell is inversely proportional to its location tuning. Both tuning function changes with time after stimulus onset.     

332.11 In this curious study, Lafer-Sousa et al. measured BOLD activation on human and monkey with either black-and-white or colored pictures of scene and faces. They showed that in both species, color area is sandwiched between face area and scene area. Hence, the ventral stream of both species has a similar organization.

333.03 Emmerling et al. use 7T MRI to get 0.7x0.7 voxel size. They measured BOLD activation for images with central square that is either far or near from the background. Then, they did a MVPA on MT response for disparity tuning. They only see some weak effect of disparity tuning.

Their technology is impressive, but I think that they are on the wrong track. MVPA is a bad tool in this case. MT should contain all sorts of disparity selectivity cell. They may not favor one disparity over the other.

333.05 Zeater et al. should that there are many binocular cells in LGN. A result that is going to rewrite many textbooks.

333.07 Cooling caudal intraparietal area reduced the activation in many 3D related areas in the monkey brain and impair their 3D discrimination. It reveals a huge network for 3D scene processing.

489.08 Nassi et al. inject optogenetic virus into V1. They then stimulated V1 neurons either directly with laser (optogenetic stimulation) or with visual stimuli. Both drove excitatory and inhibitory responses. The interaction of the two effects can be explained with a normalization model.

582.04 Meyer et al. showed faces with different identity and viewpoints to monkeys. Then, measure single cell responses in different face patches. They then used pattern classification method to show what information is decoded in each patch. 

674.03 Felleman et al. used optical imaging to study color and orientation tuning, They showed a pinwheel and quasi-linear organization for both hus and orientation in V4. However, only 5% of V4 neuron show selectivity for both properties. Most V4 neurons are selective to either one of two.

674.04 There are two types of activation for texture segregation: Feedfoward: Feature extraction, boundary detection, feedback: region filling. The authors measured the V1 response while microstimulate the V4 neurons with the same receptive field. The microstimulation causes a brief excitation followed by inhibition at V4, and slightly drive V1 response. 

674.08 Jonas et al. from Rossion lab measured the intracranial responses to faces. They presented nonface object in 1.2Hz and face in 6Hz. From the FFT analysis, they showed that the energy ratio between face freq. and nonface Freq. increased gradually from V1 to FFA.

674.09 The excitatory and inhibitory features of V4 neurons have orthogonal orientation.  This produced curvature selectivity in V4

674.10 Lateral categorical regions biased toward lower visual field while ventral categorical regions biased toward upper visual fields. The authors then estimated pRF of voxels in several object areas used windowed scene images. They showed PPA pRF is biased toward upper visual field, while TOS, lower visual field.

772.01 Dumoulin group asked where visual cortex responding to retinal position of image location. They measured the pRF for images either the left and the right eye are of the same image or left and the right eye images has different location, but not at the same time. They then fit pRF model for either eye. The brain is able to respond to the offset. They then present images with small disparity. They showed V1 respond to retinal position while V2, visual field location. V3 did not respond to offset for all voxel. But a subset of voxel showing difference in left and right eye conditions, then, all areas from V2 to LO responding to visual field position, only V1 to retinal position.  

This study is striking similar to the one carried out in my lab.

772.05 Allen et al. asked whether amblyopia associated with anatomic abnormalities in white mater. The did DTI with CSE (to resolve crossing fiber issue, the result is similar to DSI). The ROIs are Pulvinar, V1, LGN and MT. They measure mean diffusivity and fractional anisotropy. Amblyopes has a greater diffusivity than the normal control in LGN-V1, V1-Mt and PLN-MT., ie., all pathways between thalamo-cortical connection, but not cortico-cortical connections. Is that More myelin degeneration for amblyopes?

Again, this study is almost identical to the research going on in my lab.

772.06  They used functionally defined White matter, which is functional MRI ROI diluted by 6mm . And then, used those area as seed for DTI. Separate white matter tracts for face and scene regions.  The normal and DP patients has the same tracts, but differ in diffusitivity.  

APCV2014 Note

Winawer

fMRI spatial summation is sublinear and pRF nonlinearity has a compressive nonlinearity.

Second order contrast (Kay et al. 2013, PLOS Computational Biology): V1 front end -> second order contrast (RMS contrast) -> decompose to low and high 2nd order contrast component -> predicted response.  The same model fits fMRI and eCog data.

Kreigeskorte

Cichy et al (2014, Nature neuroscience) showed MEG representation dynamics in which the correlation between the MEG response to different types of objects emerges at different time points.

Current computational models for vision cannot explain IT. They are all less categorical than IT.  He proposed a forward multiple convolution model to explain it.

Ziemba

V2 model: Nature image=> V1 model<==> iterative synthesis with filtered noise -> V2 model (correlation across position, orientation an scale <==> iterative synthesis with white  noise.

The measured naturalness of random noise pattern from different texture family. They found that V2 response is affected by naturalness of the texture  (between 150-250ms after stimulus onset). fMRI also showed V2 and V3 response correlated strongly  with naturalness. There is no correlation in V1.  V2 also decode different texture types better.

  

Zaidi suggested that unique colors have no psychophysical color or physiological uniqueness.  Instead, it may related to language. For instance, if one ask an observer to identify a color that is half way between unique colors A and B and between C and D, and find another color that is half way between the two just picked. The match is not the same as picking and C and B and D first. That is (A+B) + (C + D) ~= (A+C) + (B+D). The law of distribution holds only if the observer is instructed to use opponent colors, rather than unique colors.   

Koida et al. demonstrated a glaze illusion in which a white disk appears to be self-illuminated when it is surround by a luminance gradient, but not when it is surrounded by uniform gray. See below

[I am wondering how the effect will hold if the disk and the surround are isoluminance colors].

Isherwood, Schira & Spehar showed that BOLD activation in V1 is a U-shape function of 1/f slope of the (noise) image spectrum. There is an activation in the medial frontal cortex that is correlated with the aesthetic preference to the slope.

Schira constructed a software that can simulate and model BOLD response from an image.

[Schira's model has a good performance to small stimuli. Hence, it may resolve some problems we encountered in the lab on BOLD activation for small stimulus difference]

Beer & Greenlee studied the network of multiple sensory network by combining probabilistic fiber tracing and functional MRI. They seeded a voxel by fMRI, traced fibers from that voxel, and observed the terminations of the fibers. The termination was compared with the fMRI activation and showed a high correlation between the two measurements.

VSS2014 Note

• Kay et al. proposed a scheme to model fMRI BOLD response. He model includes a pRF, followed by contrast normalization and second order summation. This model can fit V1-V3 BOLD activation.

Many experiments in my lab should be able to use this model. Also, once we know the pRF, we can construct our own BOLD activation model.

• Freeman et al. used designer stimuli to explore the BOLD activation in the ventral stream. He used a lot stimuli to probe brain activation in many, many areas. Then, used simocelli's synthetic texture method to model the stimulus optimal for a brain region. Then, use that stimuli to explore the function of the target brain area.
• 24.25 Webster and Fine used a data driven approach to explore the brain function. They basically just use a huge bank of stimulus. Then get a correlation between voxels.  They then arrange the correlation matrix to find clusters of voxels with high correlations and define that as an ROI. This provides a way to detect brain regions without making assumption about the function of a particular area in advance.
•  26.11 - 26.14 many authors reported that grouping flankers with other stimuli reduces crowding effect.
• 26.16 BOLD response to target with iso-orientation collinear flankers is contrast dependent but not orthogonal flankers. This study is almost identical to mine submitted earlier this year.
• 26.573 Palmer believed the 1/3 rule of composition, or that the object should put at 1/3 position of the display for the best aesthetic value is not the whole story. This rule changes with other picture properties, such as inward bias or the position of background objects.
• 32.13 Yuvol-Greenberg & Heeger studied continuous flash suppression on the BOLD activation in early visual cortex. Their stimuli has the mask either at the same eye or different eye as the target and varied target contrast. They showed that the mask changes the gain to the target and suggested that the CFS may be just a kind of monocular noise masking.
• 32.14 Metzger used ERP to study the brain activation of a probe in the suppressed eye in binocular rivalry. They showed that the suppressed or unsuppressed probe had the same waveform in early ERP waveforms and only differed after 300ms later than probe onset. Hence, they conclude that the response to probe in binocular rivalry is due to top-down attentional process.
• 61.14 Marlow  et al. from Anderson's lab reported that not only the depth percept and  lighting condition perceived from a sinewave grating can be influenced by the shape of its contour, but also the perceived material (matt or metallic) can be affected by the contour if the grating has more black than white.
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