Hubel and Wiesel's research in the 1950s and 1960s showed that cat visual cortices include neurons that react to tiny parts of the visual field separately. The region of visual space within which visual inputs impact the firing of a single neuron is known as its receptive field while the eyes are not moving. Neighboring cells have receptive fields that are comparable and overlap. The size and location of receptive fields vary consistently across the cortex to generate a full map of visual space. The contralateral visual field is represented by the cortex in each hemisphere.

There's a video that's shown in almost every introductory neuroscience course. It doesn't look like much--a bar of light shifting and rotating across a black screen while the background audio pops and crackles like the sound of a faraway fireworks show. Dry stuff, until you learn that the pops represent the firing of a single neuron in the brain of a cat, who is watching the bar move on the screen. When the bar reaches a specific location and lies at a particular angle, the popping explodes in a grand finale of frantic activity. The message is clear: This neuron really, really cares about that bar.

Compared to computers, humans and most other vertebrates (including some invertebrates), can learn internal representations of things, such as objects, or concepts, unbelievably fast. Instead of requiring millions of labeled data points, a toddler will understand the concept of a chair with only a handful of examples. How? Do most organisms have a large set of hard-coded procedures encoded in their neural circuitry, that were created and accumulated overtime through evolutionary forces? Considering the evidence, this seems to be very unlikely. We know that organisms do have some hard-coded memories that influence their behaviors and actions, but the number of such procedures is limited.

For something so effortless and automatic, vision is a tough job for the brain. It's remarkable that we can transform electromagnetic radiation--light--into a meaningful world of objects and scenes. After all, light focused into an eye is merely a stream of photons with different wave properties, projecting continuously on our retinas, a layer of cells on the backside of our eyes. Before it's transduced by our eyes, light has no brightness or color, which are properties of animal perception. Our retinas transform this energy into electrical impulses that propagate within our nervous system. Somehow this comes out as a world: skies, children, art, auroras, and occasionally ghosts and UFOs.

When neuroscientists David Hubel and Torsten Wiesel wanted to figure out how the brain parses its visual environment, they went as simple as they could go. In a Harvard lab crammed with electrical equipment, they positioned cats in front of a screen and showed them extremely basic images: dots in particular locations, lines at various angles. At the same time, they used implanted electrodes to, quite literally, "listen" to neurons in the areas of the brain devoted to vision. By observing which neurons fired in response to which shapes, they were able to unlock a part of the brain's "visual code," the way in which it represents visual information about its environment. For their achievement, Hubel and Wiesel won the Nobel Prize in 1981, and their discoveries kick-started the rich, diverse field of visual neuroscience.

Although Computer Vision (CV) has only exploded recently (the breakthrough moment happened in 2012 when AlexNet won ImageNet), it certainly isn't a new scientific field. Computer scientists around the world have been trying to find ways to make machines extract meaning from visual data for about 60 years now, and the history of Computer Vision, which most people don't know much about, is deeply fascinating. In this article, I'll try to shed some light on how modern CV systems, powered primarily by convolutional neural networks, came to be. I'll start with a work that came out in the late 1950s and has nothing to do with software engineering or software testing. One of the most influential papers in Computer Vision was published by two neurophysiologists -- David Hubel and Torsten Wiesel -- in 1959.

Although Computer Vision (CV) has only exploded recently (the breakthrough moment happened in 2012 when AlexNet won ImageNet), it certainly isn't a new scientific field. Computer scientists around the world have been trying to find ways to make machines extract meaning from visual data for about 60 years now, and the history of Computer Vision, which most people don't know much about, is deeply fascinating. In this article, I'll try to shed some light on how modern CV systems, powered primarily by convolutional neural networks, came to be. I'll start with a work that came out in the late 1950s and has nothing to do with software engineering. One of the most influential papers in Computer Vision was published by two neurophysiologists -- David Hubel and Torsten Wiesel -- in 1959.

How vision operates is a complex task the human brain (and now "computer brains" have to take on). We take much of what our brains do for granted. For instance, there is depth perception, object tracking, differences in lighting, edge detection, and many other features that our brains keep track of. Scanning the environment and localizing where we are in space in is an undertaking that our brain is constantly doing. At some point in the past, researchers may have never thought it possible to create systems that can perform similar tasks to that of our own brains.