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Dr. David Whitehouse’s What Cat Sees

May 28, 2012

Scientists have literally seen the world through cat’s eyes by Dr David Whitehouse. In what is bound to become a much debated and highly controversial experiment, a team of US scientists have wired a computer to a cat’s brain and created videos of what the animal was seeing. According to a paper published in the Journal of Neuroscience, Garrett Stanley, Yang Dang and Fei Li, from the Department of Molecular and Cell Biology, University of California, Berkeley, have been able to “reconstruct natural scenes with recognizable moving objects”. The researchers attached electrodes to 177 cells in the so-called thalamus region of the cat’s brain and monitored their activity. The thalamus is connected directly to the cat’s eyes via the optic nerve. Each of its cells is programmed to respond to certain features in the cat’s field of view. Some cells “fire” when they record an edge in the cat’s vision, others when they see lines at certain angles, etc. This way the cat’s brain acquires the information it needs to reconstruct an image.

Recognisable objects – The scientists recorded the patterns of firing from the cells in a computer. They then used a technique they describe as a “linear decoding technique” to reconstruct an image.

Scientists saw recognisable objects – To their amazement they say they saw natural scenes with recognisable objects such as people’s faces. They had literally seen the world through cat’s eyes. Other scientists have hailed this as an important step in our understanding of how signals are represented and processed in the brain. It is research that has enormous implications.

Artificial brain extensions – It could prove a breakthrough in the hoped-for ability to wire artificial limbs directly into the brain. More amazingly, it could lead to artificial brain extensions. By understanding how information can be presented to the brain, some day, scientists may be able to build devices that interface directly with the brain, providing access to extra data storage or processing power or the ability to control devices just by thinking about them. One of the scientists behind this current breakthrough, Garrett Stanley, now working at Harvard University, has already predicted machines with brain interfaces. Such revolutionary devices should not be expected in the very near future. They will require decoding information from elsewhere in the brain looking at signals that are far more complicated than those decoded from the cat’s thalamus but, in a way, the principle has been demonstrated.

Figure 2. – Reconstruction of natural scenes from the responses of a population of neurons. (a), Receptive fields of 177 cells used in the reconstruction. Each receptive field was fitted with a two-dimensional Gaussian function. Each ellipse represents the contour at one standard deviation from the center of the Gaussian fit. Note that the actual receptive fields (including surround) are considerably larger than these ellipses. Red: On center. Blue: Off center. An area of 32 by 32 pixels (0.2 degrees/pixel) where movie signals were reconstructed is outlined in white. The grid inside the white square delineates the pixels. (b), Comparison between the actual and the reconstructed images in an area of 6.4 degrees by 6.4 degrees (white square in (a)). Each panel shows four consecutive frames (interframe interval: 31.1 msec) of the actual (upper) and the reconstructed (lower) movies. Top panel: scenes in the woods, with two trunks of trees as the most prominent objects. Middle panel: scenes in the woods, with smaller tree branches. Bottom panel: a face at slightly different displacements on the screen. (c), Quantitative comparison between the reconstructed and the actual movie signals. Top: histogram of temporal correlation coefficients between the actual and the reconstructed signals (both as functions of time) at each pixel. The histogram was generated from 1024 (32×32) pixels in the white square. Bottom: histogram of spatial correlation coefficients between the actual and the reconstructed signals (both as functions of spatial position) at each frame. The histogram was generated from 4096 frames (512 frames/movie, 8 movies) (reconstructed images).

Source: What Cat Sees by Dr David Whitehouse (Sci/Tech, October 8, 1999 BBC News Online Science UK)

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