Episode 035: Visual Attention, Neural Networks and Computational Neuroscience with Dr. Rougier

RougierDr. Nicolas Rougier is a full-time research scientist at the French National Institute for Research in Computer Science and Control.  During the past decades he’s been working extensively on visual attention in order to understand how we visually explore a scene.  Dr. Rougier discusses his work and  visual attention and computation neuroscience in particular.  He also dives deeper into how seeing is mostly an illusion and that we do not process all visual information that passed through our eyes and we’re making deliberate (consciously or not) choices on what we concentrate.

 

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Bio:

Dr. Nicolas Rougier is a full-time research scientist at the French National Institute for Research in Computer Science and Control (INRIA).  He is working within the MNEMOSYNE project which lies at the frontier between integrative and computational neuroscience in association with the Institute of Neurodegenerative Diseases.

 

Recommended Book:

“The Man Who Mistook His Wife for a Hat” by Oliver Sack

 

Glimpse into the interview:

ES: What are you the most excited about in your field of work and why?

NR:Trying to understand how the brain works is exciting because, with the accompanying body, it is a wonderful machine that is able to learn, to adapt, to recover from lesion, etc. Today, even with the recent advances in neurosciences, we are still very far from understanding it. There is thus a real challenge to model a functional brain, i.e. a brain that do something.

ES: What makes this project/research special to you personally?

NR:I’ve been interested in these researches because vision is really counter-intuitive. In this framework computational model can really help us understand the basic mechanisms that make us to attend this or that stimulus or this or that spatial location. The challenge at the computational level is that some decision must be taken, but there is no grand supervisor to do that.

ES: What is the next big obstacle that you see in this field and what implications will this have on the world of science or your study in particular?

NR:The very complexity of the brain make it very difficult to model and to understand it. We generally use model to simplify reality in order to have a better understanding. However, in the case of the brain, it is extremely difficult to design simplify models that still give a fair account of the reality. Thus, the next big obstacle would be to have computational models that are much complex as the reality they pretend to model.

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