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Visual Cortex Contex –Pouya Bashivan, Postdoctoral Associate, MIT, Department of Brain and Cognitive Sciences – Brain Activity & Advanced Visual Recognition
In this podcast, Pouya Bashivan, Postdoctoral Associate, Massachusetts Institute of Technology, in the Department of Brain and Cognitive Sciences, discusses brain activity and visual recognition.
Bashivan's lab is particularly interested in how the brain sees, how the visual cortex recognizes objects, etc. Working with Kohitij Kar, the neuroscientists have engaged in some of the most significant testing to date of computational models that seek to approximate a brain's visual cortex. Using their models of a brain's visual neural network, Bashivan and Kar provided a new way to control individual neurons as well as populations of neurons in the middle of the network. Their research shows that certain versions of these new models are similar enough to a brain that they could perhaps be used to control brain states in animals.
The MIT neuroscientist discusses upcoming developments in machine learning and artificial intelligence. He talks about self-driving cars and various new technologies, and the challenges developers and manufacturers face in regard to vision in their operating systems.
Continuing, Bashivan states that the future of machine vision could be focused well beyond basic object recognition. Specifically, Bashivan discusses new models that are being trained to play digital games, which could advance to training in more realistic style games that could provide significant data on how human brains are affected by various experiences.
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By Richard Jacobs
4.2
494494 ratings
In this podcast, Pouya Bashivan, Postdoctoral Associate, Massachusetts Institute of Technology, in the Department of Brain and Cognitive Sciences, discusses brain activity and visual recognition.
Bashivan's lab is particularly interested in how the brain sees, how the visual cortex recognizes objects, etc. Working with Kohitij Kar, the neuroscientists have engaged in some of the most significant testing to date of computational models that seek to approximate a brain's visual cortex. Using their models of a brain's visual neural network, Bashivan and Kar provided a new way to control individual neurons as well as populations of neurons in the middle of the network. Their research shows that certain versions of these new models are similar enough to a brain that they could perhaps be used to control brain states in animals.
The MIT neuroscientist discusses upcoming developments in machine learning and artificial intelligence. He talks about self-driving cars and various new technologies, and the challenges developers and manufacturers face in regard to vision in their operating systems.
Continuing, Bashivan states that the future of machine vision could be focused well beyond basic object recognition. Specifically, Bashivan discusses new models that are being trained to play digital games, which could advance to training in more realistic style games that could provide significant data on how human brains are affected by various experiences.
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