Computational Modeling

Developing Intelligence

Category archives for Computational Modeling

Stimulating the brain with high frequency electrical noise can supersede the beneficial effects observed from transcranial direct current stimulation, either anodal or cathodal (as well as those observed from sham stimulation), in perceptual learning, as newly reported by Fertonani, Pirully & Miniussi in the Journal of Neuroscience. The authors suggest that transcranial random noise stimulation…

In their wonderful Neuroimage article, Braun & Mattia present a comprehensive introduction to the possible neuronal implementations and cognitive sequelae of a particular dynamical phenomenon: the attractor state. In another excellent paper, just recently out in Frontiers, Itskov, Hansel and Tsodyks describe how such attractor dynamics may be insufficient to support working memory processing unless…

Owing to the low signal-to-noise ratio of functional magnetic resonance imaging, it is difficult to get a good estimate of neural activity elicited by task novelty: by the time one has collected enough trials for a good estimate, the task is no longer novel! However, a recent J Neurosci paper from Cole, Bagic, Kass &…

Two seemingly contradictory trends characterize brain development during childhood and adolescence: Diffuse to focal: a shift from relatively diffuse recruitment of neural regions to more focal and specific patterns of activity, whether in terms of the number of regions recruited, or the magnitude or spatial extent of that recruitment Local to distributed: a shift in…

“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…

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…

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…

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…

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…

How does the brain deal with the need to pursue multiple goals simultaneously, particularly if they are associated with different reward values? One idea, perhaps far-fetched, is that the brain might divvy up responsibility for tracking these goals & rewards: for example, the left hemisphere might respond to a primary goal, and the right hemisphere…