July 27, 2010
[ Artificial Intelligence, Computational Modeling ]
"What we're seeking is not just one algorithm or one cool new trick - we're seeking a platform technology. In other words, we're not seeking the entirety of a collection of point solutions, what we're seeking is a platform technology on which we can build a wide variety of solutions."
Dharmendra Modha, manager of cognitive computing at IBM Research Almaden, discusses the Systems of Neuromorphic Adaptive Plastic Scalable Electronics ("SyNAPSE") project. Mad scientist eyes are also on display:
Video after the jump:
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Posted by Chris Chatham at 3:20 PM • 4 Comments • 0 TrackBacks
July 23, 2010
[ Cognitive Neuroscience ]
Decisions can be hard: the conflict you face in any decision can be increased if option A is not that much better than option B, or if option A is newly worse than option B. And then there are are just bad decisions, maybe hard only in retrospect. As illustrated by a 2009 J Neurosci article from Mitchell, Luo, Avny et al it seems that dorsal areas of the prefrontal cortex might help guide us in making tough decisions, whereas a ventrolateral prefrontal area might just alert us only after a bad decision was made.
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Posted by Chris Chatham at 8:55 AM • 2 Comments • 0 TrackBacks
July 22, 2010
[ Artificial Intelligence, Cognitive Neuroscience, Computational Modeling ]
Recent work has leveraged increasingly sophisticated computational models of neural processing as a way of predicting the BOLD response on a trial-by-trial basis. The core idea behind much of this work is that reinforcement learning is a good model for the way the brain learns about its environment; the specific idea is that expectations are compared with outcomes so that a "prediction error" can be calculated and minimized through reshaping expectations and behavior. This simple idea leads to exceedingly powerful insights into the way the brain works, with numerous applications to improving learning in artificial agents, to understanding the role of exploration in behavior and development, and to understanding how the brain exerts adaptive control over behavior.
So far, however, neuroimaging and electrophysiology suggest that these prediction error signals can be found through much of the cortex, including large swaths of parietal, frontal, and striatal areas.
This is where a 2010 Neuron paper by Gläscher, Daw, Dayan & O'Doherty comes to the rescue. Traditionally, reinforcement learning has been viewed as a somewhat monolithic entity, such that expected rewards are compared with reward outcomes to generate a "reward prediction error." It's easy to imagine that most of the brain might light up in response to rewards. But Gläscher take this a step farther, and dissociate between two flavors of reinforcement learning (RL):
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Posted by Chris Chatham at 6:08 PM • 0 Comments • 0 TrackBacks
July 15, 2010
July 14, 2010
[ Artificial Intelligence, Cognitive Neuroscience, Computational Modeling, Developmental Psychology ]
How can we enhance perception, learning, memory, and cognitive control? Any answer to this question will require a better understanding of the way they are best enhanced: through cognitive change in early development.
But we can't stop there. We also want to know more about the neural substrates that enable and reflect these cognitive transformations across development. Some information is provided by developmental neuroimaging, but even that's not enough, because the real question we have can only be answered via mechanisms ("how"/"why") - quite different than the "what" "where" and "roughly when" questions addressed by neuroimaging. For "how/why," we ultimately need a mathematical way of describing cognitive changes and how they unfold in tandem with changes in neural information processing. This, Russ Poldrack argues in his 2010 HBM paper, can come only from a computational integration of cognitive and neural development: something called "computational developmental cognitive neuroscience."
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Posted by Chris Chatham at 11:35 AM • 2 Comments • 0 TrackBacks
July 13, 2010
[ Cognitive Neuroscience, Developmental Psychology ]
A nice 2010 Human Brain Mapping paper by Church, Petersen & Schlaggar covers a number of interpretational issues confronting modern neuroimaging. Their particular application is pediatric neuroimaging (I will also use developmental examples), but the general issues apply to nearly all fMRI studies. So here are some important things to keep in mind whenever you read an fMRI study:
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Posted by Chris Chatham at 4:29 PM • 1 Comments • 0 TrackBacks
[ Cognitive Neuroscience, Developmental Psychology ]
A 2010 FINS paper from Cohen et al. demonstrates that multivariate patterns in neural recruitment during response inhibition across the brain are significantly predictive of response inhibition ability and age of the scanned subject, and shows that other factors (such as response variability and reaction times) cannot be similarly predicted from the same data.
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Posted by Chris Chatham at 2:08 PM • 0 Comments • 0 TrackBacks
July 12, 2010
[ Artificial Intelligence, Cognitive Neuroscience, Computational Modeling ]
What if we got the organization of prefrontal cortex all wrong - maybe even backwards? That seems to be a conclusion one might draw from a 2010 Neuroimage paper by Yoshida, Funakoshi, & Ishii. But that would be the wrong conclusion: thanks to an ingenious mistake, Yoshida et al have apparently managed to "reverse" the functional organization of prefrontal cortex.
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Posted by Chris Chatham at 8:25 PM • 2 Comments • 0 TrackBacks
July 9, 2010
[ Artificial Intelligence, Cognitive Neuroscience, Computational Modeling ]
Hierarchical views of prefrontal organization posit that some information processing principle, and not just task difficulty, determines which areas of prefrontal cortex will be recruited in a given task. Virtually all information processing accounts of the prefrontal hierarchy are agreed on this point, though they differ in whether the operative principle is thought to be the temporal duration over which information must be maintained, the relational complexity of that information, the number of conditionalities necessary to consider in behaving on that information, or the inherent abstraction of the underlying representations.
All of these ideas seem to mesh with an intuitive sense of what it means for something to be "difficult," such that one might as well say that more difficult things activate more anterior regions of the prefrontal cortex. Ruling out difficulty as an explanation has thus been a pernicious problem for much of this work. Typically, the attempted arguments rely on the behavioral expression of difficulty (long reaction times or high error rates) not following the same patterns as neural activation. But these arguments are complicated by the fact that the behavioral expression of difficulty might be muddled if subjects recruit additional regions of prefrontal cortex in response to perceived difficulty.
Thankfully, this issue has been definitively addressed by a 2010 Neuron paper from Badre, Kayser & D'Esposito. In the process they've provided numerous insights into the way an integrated prefrontal-striatal circuit might support an information processing hierarchy and "parallel search" in service of the discovery of rules for governing behavior.
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Posted by Chris Chatham at 3:11 PM • 4 Comments • 0 TrackBacks
