Wolf Singer reviews recent work on cortical dynamics. He concludes that precise temporal coordination between neurons dynamically forms networks and provides a high-dimensional space for neural computations.
For further reading see:
- Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. “The importance of mixed selectivity in complex cognitive tasks”. Nature, 497, 585-590, 2013 doi:10.1038/nature12160. View PDF
- Miller, E.K. and Fusi, S. (2013) Limber neurons for a nimble mind. Neuron. 78:211-213. View PDF
- Miller, E.K. and Buschman, T.J. (2013) Cortical circuits for the control of attention. Current Opinion in Neurobiology. 23:216–222 View PDF »
- Buschman, T.J., Denovellis, E.L., Diogo, C., Bullock, D. and Miller, E.K. (2012) Synchronous oscillatory neural ensembles for rules in the prefrontal cortex. Neuron, 76: 838-846. View PDF
There was a segment titled “There’s No More Single Tasking”
Watch it here (archived):
http://live.huffingtonpost.com/r/segment/the-lost-ability-to-do-one-task-at-a-time/525fff6b78c90a6d7e00020f
The New Yorker reviews Sue Corkin’s book about H.M., the famous neurological patient who could not form new memories.
O’Neill and Schultz showed subjects visual stimuli that indicated different levels of risk. They found that orbitofrontal neurons reflected the discrepancy between current and predicted risk.
John Duncan provides an excellent review of the role of the frontal and parietal cortex in higher level cognition. He argues that they form a multiple-demand (MD) network. Neurons in this MD network have multiple functions, flexibly adapting their coding to signal different things in different tasks. Duncan argues that they play a critical role in subdividing complex problems and organizing a coherent sequence of focused parts or subgoals. The MD network construction of these attentional episodes is thought to be a core function for complex cognition and this function is central to fluid intelligence.
Crowe et al recorded simultaneously from the prefrontal and parietal cortices during a visuospatial categorization task. By examining simultaneous fluctuations in information, they provided evidence that the signals reflecting the rule-dependent categories were transmitted in a top-down fashion from the prefrontal to the parietal cortex. The prefrontal cortex has long been thought to be the source of top-down “executive” signals. However, until recently, direct evidence for this has been rare. This study adds to a growing body of literature that has provided this evidence by using multiple-electrode recording to examine the temporal dynamics of neural signals (e.g., Buschman and Miller, 2007; Ibos et al, 2013).
More evidence for domain-general processing in higher-level cortex. Federenko et al tested human subjects with seven tasks with different cognitive demands. FMRI revealed overlapping activation zones in the frontal and parietal cortex. This is consistent with neurophysiological studies showing that many neurons in these areas are multifunctional. Rigotti et al recently demonstrated that these multifunctional “mixed selectivity” neurons provide the computational power needed for high-level cognition.
For further reading:
Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. “The importance of mixed selectivity in complex cognitive tasks”. Nature, 497, 585-590, 2013 doi:10.1038/nature12160. View PDF
Miller, E.K. and Fusi, S. (2013) Limber neurons for a nimble mind. Neuron. 78:211-213. View PDF
Everybody knows that we can only hold a limited number of things in mind simultaneously. Is this capacity limit due to a limited number of “slots” in working memory or due a limited resource pool that is divided among the items held in mind? We found evidence for both (Buschman et al, 2011). Now, Roggeman et al use computational modeling to provide further evidence for a hybrid model for capacity limits of working memory.
Further reading:
Buschman,T.J., Siegel, M., Roy, J.E. and Miller, E.K. (2011) Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences. 108(27):11252-5. View PDF »
Van der Linden et al used computer generated images to study categorization in the human brain. They found that the frontal cortex showed sensitivity to the features diagnostic for the categories, which is consistent with results from animal studies at the neuron level.
Psychedelic drugs desynchronize oscillatory rhythms in the cortex. Like, wow.
Kramer and Eden offer a new method for assessing cross-frequency coupling between oscillatory neural signals.
Mirpour and Bisley recorded neural responses and local field potentials from the lateral intraparietal cortex (LIP) during visual search. Previously fixated non-target stimuli elicited greater lower frequency (alpha and beta) oscillations. This suggested that reduced neural responses (and attention) to previously seen stimuli results from oscillatory-based top-down influences from the frontal cortex.
John Duncan and colleagues examined dynamic allocation of attention in the prefrontal cortex. A behaviorally relevant target and non-target were simultaneously presented in both visual hemifields. At first, activity in each hemifield was dominated by the stimulus in the contralateral field but then all activity became dominated by the target alone. The speed and degree of attentional reallocation depend on relative attentional weights; more experience with a target led to faster and greater allocation to the target. Because neurons rapidly shifted their representation from an irrelevant to relevant stimulus in the opposite hemifield, these results are consistent with adaptive coding models of neural representation.
Kadohisa et al (2013) Dynamic Construction of a Coherent Attentional State in a Prefrontal Cell Population
Further reading on adaptive coding:
Miller, E.K. and Cohen, J.D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24:167-202. View PDF »
Duncan, J. and Miller, E.K. (2013) Adaptive neural coding in frontal and parietal cortex. In: Stuss, D.T. and Knight, R.T. (Eds). Principles of Frontal Lobe Function: Second Edition.
Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. “The importance of mixed selectivity in complex cognitive tasks”. Nature, 497, 585-590, 2013 doi:10.1038/nature12160. View PDF
Bea Luna and colleagues used graph theory to examine the development of functional hubs in the human brain. The hub architecture develops earlier, but connections between the hubs and “spokes” continue to develop and change into adulthood.
Jack Gallant and crew used FMRI to examine scene processing in the human brain. They found that scenes activated many regions of anterior visual cortex and that the scene categories capture the co-occurrence of the objects that compose the scenes.
Adam Gazzaley and company show, for the first time, that training on a video game results in benefits that transfer to other tests of cognition. Training on the NeuroRacer game produced long-lasting improvements in cognitive abilities of older adults (age 65-80). How did they do it? Their trick was to focus on multitasking and attention.
Anguera et al (2013) Nature
Matt Chafee and colleagues used multiple-electrode recording in the prefrontal and parietal cortices to examine the temporal dynamics of their neural activity during a categorization task. They decoded category signals from patterns of simultaneously recorded in small bins and asked whether the resulting information time series in one area could predict the other. This showed that “executive” top-down signals flow from the prefrontal to parietal cortex.
Max Riesenhuber and colleagues used EEG to examine the time course of shape and category signals in the human brain. Neural adaptation for category changes was seen in frontal cortex and then subsequently in temporal cortex. This supports the hypothesis that shape categories are formed by shape signals from temporal cortex that converge and form explicit category representations in frontal cortex. A late category signal in temporal cortex is consistent with category signals feeding back from frontal to temporal cortex.
Ranulfo Romo and crew show delta band (1-4 Hz) synchrony between frontal and parietal cortex that varies with decisions. When there were no decisions to be made, frontal-parietal delta was reduced.
An article in MIT’s Technology Review magazine about our work on how multitasking “mixed selectivity” neurons may be key for cognition.
Do-It-All Neurons – A key to cognitive flexibility by Anne Trafton
Markov et al provide an excellent review and analysis of the anatomy of visual cortex and beyond. The show that supragranular layers contain highly segregated feedforward and feedback pathways. Their analysis of the detailed anatomy revealed that feedback connections are more numerous and have more levels than feedforward connections. By contrast, infragranular layers are less hierarchical and may be more involved in point-to-point cross-talk than feedforward or feedback processing. Markov et al map the feedforward and feedback pathways to recent observations that feedforward vs feedback communication is supported by gamma vs beta cortical oscillations.
For more on the role of oscillations in feedforward and feedback cortical communication, see our review:
Miller, E.K. and Buschman, T.J. (2013) Cortical circuits for the control of attention. Current Opinion in Neurobiology. 23:216–222 View PDF »
Ann Graybiel and crew show that the role of dopamine in reinforcement learning is not so straightforward. Rather than just give short bursts tied to reward prediction errors, dopamine ramps up as rats near a goal. It could reflect a motivational drive.
Arimura et al compared neural responses in the globus pallidus (GP), prefrontal cortex (PFC) and premotor cortex (PMC) during a task in which a visual cue instructed a goal and then another cue instructed which action to perform. The GP reflected the visual cue and goal as soon as the cortex. However, action selection occurred later in the GP than cortex. Thus, the GP seems to play a more important role in goal determination than action selection.
Miller Lab alumnus Andreas Nieder shows that dopamine (DA) has different effects on two different classes of neurons in the prefrontal cortex. For neurons with a short latency visual response, DA suppressed activity but preserved their signal to noise ratio. For neurons with a longer visual latency (exclusively broad-spiking, putative pyramidal neurons), DA increased excitability and enhanced signal/noise ratio. Thus, DA can shape how the prefrontal cortex processes bottom-up sensory inputs.
Jacob et al
A Neuron Preview for Miller Lab graduate student Simon Kornblith’s paper on a network for scene processing:
Scene Areas in Humans and Macaques by Epstein and Julian
Here’s the original post on Simon’s paper and a link to it:
A Network For Scene Processing