After a rest, when we suddenly open our eyes, we a given an incredible visual experience of the world around us – yet we take this miraculous process for granted. So how does it actually work? Well, it begins with: “photons hitting the retina, and ends with seeing,” , but the complexities involved, are a long way from simple! “The brain’s fundamental task in ‘seeing’ is to reconstruct relevant information about the world from the light that hits the eyes. Because this process is rather complex, nerve cells in the brain — neurons — also react to images in complex ways”. So let’s take a look at what we know so far.
Getting Over the Challenges
Observational initiatives to distinguish the brain cells’ reactions to different images, have not been easy. Researchers have been challenged in some ways, due to the infinite number of potential images that humans and other species, can see. However, of late, new cutting-edge research involving a computational method to speed up finding these optimal stimuli, has been put to work by scientists at Germany’s University of Tübingen, and Baylor College of Medicine.
How Did They Do It?
The researchers came up with a novel strategy of constructing deep artificial brain cell networks, which can: “accurately predict the brain cell responses produced by a biological brain to arbitrary visual stimuli. These networks can be thought of as a ‘virtual avatar’ of a population of biological brain cells, which can be used to dissect the neural mechanisms of sensation”. When new images were synthesized, certain brain cells reacted by responding very strongly.
A report of this fascinating study, which was recently published in the journal, Nature Neuroscience, explained how the researchers tried to comprehend the way our vision works. Dr. Andreas Tolias, who was the lead study author, and who currently serves as a Brown Foundation Endowed Chair of Neuroscience at Baylor, stated: “We want to understand how vision works. We approached this study by developing an artificial neural network that predicts the neural activity produced when an animal looks at images. If we can build such an avatar of the visual system, we can perform essentially unlimited experiments on it. Then we can go back and test in real brains with a method we named inception loops”.
Dr. Edgar Y. Walker, the study’s first author, who is currently a postdoctoral scientist at University of Tübingen and Baylor, remarked: “First, we showed mice about 5,000 natural images and recorded the neural activity from thousands of neurons as they were seeing the images. Then, we used these images and the corresponding recordings of brain activity to train a deep artificial neural network to mimic how real neurons responded to visual stimuli”.
Adding to this, Dr. Fabian Sinz, the study’s co-first author; group leader at the University of Tübingen, and adjunct assistant professor of neuroscience at Baylor, noted: “To test whether the network had indeed learned to predict neural responses to visual images like a living mouse brain would do, we showed the network images it had not seen during learning and saw that it predicted the biological neuronal responses with high accuracy”.
And while it has to be said that this is just the beginning, Sinz believes that: “this framework of fitting highly accurate artificial neural networks, performing computational experiments on them, and verifying the resulting predictions in physiological experiments can be used to investigate how neurons represent information throughout the brain. This will eventually give us a better idea of how the complex neurophysiological processes in the brain allow us to see”.