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Posts Tagged ‘Artificial Intelligence’

echoblog: understanding how neuromodulator (genes) help the brain compute

Posted in 5HTT, acetylcholine, Dopamine, Noradrenaline, tagged , , , , , , , , on September 29, 2009| Leave a Comment »

Brainstorm
Image by jurvetson via Flickr

pointer to: Computational Models of Basal Ganglia Function where Kenji Doya provides computational explanations for neuromodulators and their role in reinforcement learning. In his words, “Dopamine encodes the temporal difference error — the reward learning signal. Acetylcholine affects learning rate through memory updates of actions and rewards. Noradrenaline controls width or randomness of exploration. Serotonin is implicated in “temporal discounting,” evaluating if a given action is worth the expected reward.”

This type of amazing research provides a pathway to better understand how genes contribute to how the brain “works” as a 3-dimensional biochemical computational machine.

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Interview with Professor Michael Frank: Computational Neuroscience (and Genetics) of Decision Making

Posted in COMT, DARPP32, DRD2, Uncategorized, tagged , , , , , , , , , on August 18, 2009| Leave a Comment »

MFrankIf you’re interested in the neurobiology of learning and decision making, then you might be interested in this brief interview with Professor Michael Frank who runs the Laboratory of Neural Computation and Cognition at Brown University.

From his lab’s website: “Our research combines computational modeling and experimental work to understand the neural mechanisms underlying reinforcement learning, decision making and working memory. We develop biologically-based neural models that simulate systems-level interactions between multiple brain areas (primarily basal ganglia and frontal cortex and their modulation by dopamine). We test theoretical predictions of the models using various neuropsychological, pharmacological, genetic, and neuroimaging techniques.”

In this interview, Dr. Frank provides some overviews on how genetics fits into this research program and the genetic results in his recent research article “Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation”. Lastly, some lighthearted, informal thoughts on the wider implications and future uses of genetic information in decision making.

To my mind, there is no one else in the literature who so seamlessly and elegantly interrelates genetics with the modern tools of cognitive science and computational neurobiology.  His work really allows one to cast genetic variation in terms of its influence on neural computation – which is the ultimate way of understanding how the brain works.  It was a treat to host this interview!

Click here for the podcast and here, here, here for previous blog posts on Dr. Frank’s work.

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