The Automatic Neuroscientist

By Neuroskeptic | January 30, 2016 6:01 am

We’ve learned this week that computers can play Go. But at least there’s one human activity they will never master: neuroscience. A computer will never be a neuroscientist. Except… hang on. A new paper just out in Neuroimage describes something called The Automatic Neuroscientist. Oh.

So what is this new neuro-robot? According to its inventors, Romy Lorenz and colleagues of Imperial College London, it’s a framework for using “real-time fMRI in combination with modern machine-learning techniques to automatically design the optimal experiment to evoke a desired target brain state.”

It works like this. You put someone in an MRI scanner and start an fMRI sequence to record their brain activity. The Automatic Neuroscientist (TAN) shows them a series of different stimuli (e.g. images or sounds) and measures the neural responses. It then learns which stimuli activate different parts of the brain, and works out the best stimuli in order to elicit a particular target pattern of brain activity (which is specified at the outset.) This is not an entirely new idea as Lorenz et al. acknowledge, but they say that theirs is the first general framework.

automatic neuroscientist

Lorenz et al. conducted a proof-of-concept study in which they asked TAN to maximize the difference in brain activity between the lateral occipital cortex (LOC) and superior temporal cortex, by presenting visual and auditory stimuli of varying levels of complexity.

They show that TAN successfully learned to do this, converging to a solution in under 10 minutes in most cases. TAN learned to increase the complexity of the visual stimuli while turning off the auditory stimuli entirely. This makes sense, because LOC is a visual area, while superior temporal lobe encodes sounds. Complex images and no sounds will maximize the difference between those regions. So the answer was obvious to a human neuroscientist – but the point is that TAN worked the solution out on its own.

Why would you want to find stimuli that activate a particular brain region? Lorenz et al. say that TAN has many potential uses. Researchers could use it to find the optimal stimuli as part of a pilot study, in order to maximize statistical power. Or, when it comes to clinical treatments for neurological disorders, TAN could help to optimize interventions, such as cognitive training tasks, or even brain stimulation parameters – there’s no reason that TAN needs to be limited to controlling visual and auditory stimuli.

I would say however that much will depend on how many variables TAN is asked to optimize simultaneously – in other words, how many dimensions the “experiment space” has. In the proof-of-concept study, there were just two dimensions. I feel that if TAN is going to be able to find new solutions, ones that human neuroscientists don’t already know, it would need to be applied to much larger experimental spaces. It’s not clear how well it would perform in that case.

The authors conclude that

With the work we present here, we aim to stimulate the field, to explore the wide range of novel applications involving closed-loop real-time fMRI. We envision that the framework explained here, will be added to the standard toolkit of modern functional imaging.

ResearchBlogging.orgLorenz R, Monti RP, Violante IR, Anagnostopoulos C, Faisal AA, Montana G, & Leech R (2016). The automatic neuroscientist: A framework for optimizing experimental design with closed-loop real-time fMRI. NeuroImage PMID: 26804778

CATEGORIZED UNDER: fMRI, methods, papers, science, select, Top Posts
  • D Samuel Schwarzkopf

    I remember we discussed a similar idea in a cognitive drinks session a few years ago. The meeting was about a behavioural experiment on category learning by Maarten Speekenbrink in which the stimuli were updated to optimise the behavioural discriminations – but I think Hugo Spiers suggested that this would work also for fMRI localiser type studies.

    I guess it could work but I wonder what real benefit this would bring?

    • Brad Wyble

      Having not read the article, I would guess that the space of possible experiments and stimuli one could create is so vast that this won’t generalize far beyond sensory paradigms. So I agree that the usefulness is questionable.

      • D Samuel Schwarzkopf

        Yes this could perhaps be useful for sensory designs but then it’s exploratory. I wonder how prone this might be to getting stuck in local minima. Perhaps not any less so that the crude way we’ve done it till now (“Let’s compare faces and houses”, “How about we measure the response to headless bodies because that’s -really- ecological?” ;)). Could be interesting but in the long run I think we need to more targeted hypotheses understanding what processing brain regions/networks are actually doing.

  • OWilson

    This is old hat.

    Professor Hugh Hefner found the same effect. Certain “images” evoked certain “responses” in certain parts of the brain, responsible for certain bodily functions..

    It seems that the most natural images (unadorned) pruduced the most acute responses :)

  • Joe Devlin

    Years ago Doug Lenat produced the Automated Mathematician but it so pissed off his Stanford math colleagues he immediately started calling it AM instead. When asked what AM stood for, Doug would reply “I am what I AM” TAN doesn’t have the same panache, sadly, and besides, Broca got there first (in 1861)

  • @causalseft

    I thought neuroscientists were already automates using basic reinforcement-learning algorithms combined with impact factor cost functions to design and interpret their experiments, but apparently, progress never stops ;).

  • JG

    why does this seem much more like another statistically-backed ontology rather than an ‘algorithm’?

  • Pingback: Morsels For The Mind – 05/02/2016 › Six Incredible Things Before Breakfast()



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Neuroskeptic is a British neuroscientist who takes a skeptical look at his own field, and beyond. His blog offers a look at the latest developments in neuroscience, psychiatry and psychology through a critical lens.


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