MEG study in humans shows the functional significance of high alpha-band synchrony for visual attention.
Lobier, M., Palva, J. M., & Palva, S. (2017). High-alpha band synchronization across frontal, parietal and visual cortex mediates behavioral and neuronal effects of visuospatial attention. bioRxiv, 165563.
A study of the 100th most-cited papers in Neuroscience identified Miller and Cohen (2001) as the 5th most cited paper (by total citations; 23rd if normalized by publications/year).
Yeung, A. W. K., Goto, T. K., & Leung, W. K. (2017). At the Leading Front of Neuroscience: A Bibliometric Study of the 100 Most-cited Articles. Frontiers in Human Neuroscience, 11, 363.
Miller, E.K. and Cohen, J.D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24:167-202. Designated a Current Classic by Thomson Scientific as among the most cited papers in Neuroscience and Behavior. View PDF »
Neupane et al show that alpha oscillations in area V4 link sites that encode the location of a stimulus before and after an eye movement. The alpha oscillations can help create a stable representation of the visual world during eye movements.
Neupane, S., Guitton, D., & Pack, C. C. (2017). Coherent alpha oscillations link current and future receptive fields during saccades. Proceedings of the National Academy of Sciences, 201701672.
A model showing how neural coherence can flexibly route information. If you have a better idea of what underlies cognitive flexibility, I’d like to hear it.
Flexible information routing by transient synchrony
Agostina Palmigiano, Theo Geisel, Fred Wolf & Demian Battaglia
Neurons in the prefrontal cortex keeps track of elapsed time (even though time was not explicitly relevant) via sequential firing of neurons. The overlap of sequences depended on the degree of similarity of the item being held in memory. The time-keeping showed a Weber-fraction-like decrease in precision as time passed.
Compressed timeline of recent experience in monkey lPFC
Zoran Tiganj, Jason A Cromer, Jefferson E Roy, Earl K Miller, Marc W Howard
doi: https://doi.org/10.1101/126219
Well said, Howard Eichenbaum. Could agree more. The time is nigh.
Eichenbaum, H. (2017). Barlow versus Hebb: When is it time to abandon the notion of feature detectors and adopt the cell assembly as the unit of cognition?. Neuroscience Letters.
New result on bioRxiv:
Gamma and beta bursts during working memory read-out suggest roles in its volitional control
Mikael Lundqvist, Pawel Herman, Melissa R Warden, Scott L Brincat, Earl K Miller
doi: https://doi.org/10.1101/122598
Abstract
Working memory (WM) activity is not as stationary or sustained as previously thought. There are brief bursts of gamma (55 to 120 Hz) and beta (20 to 35 Hz) oscillations, the former linked to stimulus information in spiking. We examine these dynamics in relation to read-out from WM, which is still not well understood. Monkeys held a sequence of two objects and had to decide if they matched a subsequent sequence. Changes in the balance of beta/gamma suggested their role in WM control. In anticipation of having to use an object for the match decision, there was an increase in spiking information about that object along with an increase in gamma and a decrease in beta. When an object was no longer needed, beta increased and gamma as well as spiking information about that object decreased. Deviations from these dynamics predicted behavioral errors. Thus, turning up or down beta could regulate gamma and the information in working memory.
A discussion of how bottom-up sensory information elicits high-frequency gamma oscillations. By contrast, top-down processing, which provides the context that coordinates cortical processing, elicits lower-frequency theta, alpha, beta oscillations. We have drawn similar conclusions based on our own work.
The cross-frequency mediation mechanism of intracortical information transactions
RD Pascual-Marqui, P Faber, S Ikeda, R Ishii, T Kinoshita, Y Kitaura, K Kochi, P Milz, K Nishida, M Yoshimura
A recurrent network model captures the dynamics of frontal and parietal cortex activity during a categorization task. It reveals cortical motifs that underlie computations for categorical decision-making.
Chaisangmongkon, W., Swaminathan, S. K., Freedman, D. J., & Wang, X. J. (2017). Computing by Robust Transience: How the Fronto-Parietal Network Performs Sequential, Category-Based Decisions. Neuron, 93(6), 1504-1517.
Neurons in LIP contribute to two distinct stages of processing during a search task. The working memory for the sought-after feature and then the focusing of attention on target location.
Levichkina, E., Saalmann, Y. B., & Vidyasagar, T. R. (2017). Coding of spatial attention priorities and object features in the macaque lateral intraparietal cortex. Physiological Reports, 5(5), e13136.
A review on issues to consider when studying the brain by perturbing (and measuring) it. Very thoughtful.
Navigating the Neural Space in Search of the Neural Code
Mehrdad Jazayer and Arash Afraz
An excellent and comprehensive review of the brain basis of visual attention. Worthy of yours.
Moore, T., & Zirnsak, M. (2017). Neural Mechanisms of Selective Visual Attention. Annual Review of Psychology, 68, 47-72.
A model of the collaboration and distribution of function between the prefrontal cortex and parietal cortex.
Working memory and decision making in a fronto-parietal circuit model
John D Murray, Jorge H Jaramillo, Xiao-Jing Wang
doi: https://doi.org/10.1101/104802
The anterior cingulate and FEF coordinate through theta and beta phase synchronization between spikes in one and local field potential in the other.
Babapoor-Farrokhran, S., Vinck, M., Womelsdorf, T., & Everling, S. (2017). Theta and beta synchrony coordinate frontal eye fields and anterior cingulate cortex during sensorimotor mapping. Nature Communications, 8, 13967.
The brain monitors simultaneous sensory input by “time division multiplexing”, a rhythmic juggling of the two streams of information.
Evidence for time division multiplexing of multiple simultaneous items in a sensory coding bottleneck
Valeria C Caruso, Jeffrey T Mohl, Chris Glynn, JungAh Lee, Shawn M Willett, Azeem Zaman, Rolando Estrada, Surya Tokdar, Jennifer M Groh
Still think that single neurons with specific functions rule the brain? Let us persuade you otherwise. We argue that cognitive control stems from dynamic, context-dependent population coding.
Stokes, M., Buschman, T.J., and Miller, E.K. (2017) Dynamic coding for flexible cognitive control. The Wiley Handbook of Cognitive Control, The Wiley Handbook of Cognitive Control, Edited by Tobias Egner, John Wiley & Sons, 2017(Chichester, West Sussex, UK). View PDF
The title says it all. A comprehensive review on how salience is determined and used by the brain.
Veale, R., Hafed, Z. M., & Yoshida, M. (2017). How is visual salience computed in the brain? Insights from behaviour, neurobiology and modelling. Phil. Trans. R. Soc. B, 372(1714), 20160113.
This review of the neural basis of working memory argues that working memory is a property of many brain areas working in concert. Prefrontal vs sensory cortical areas differ in their degrees of abstraction and how they are tied to action. They argue that the persistent activity that seems to underlie working memory is a general product of cortical networks.
Christophel, T. B., Klink, P. C., Spitzer, B., Roelfsema, P. R., & Haynes, J. D. (2017). The Distributed Nature of Working Memory. Trends in Cognitive Sciences.
I would add that persistent activity may not be so persistent:
Lunqvist, M., Rose, J., Herman, P, Brincat, S.L, Buschman, T.J., and Miller, E.K. (2016) Gamma and beta bursts underlie working memory. Neuron, published online March 17, 2016. View PDF »
Stokes, M., & Spaak, E. (2016). The Importance of Single-Trial Analyses in Cognitive Neuroscience. Trends in cognitive sciences.
Stokes, M. G. (2015). ‘Activity-silent’working memory in prefrontal cortex: a dynamic coding framework. Trends in cognitive sciences, 19(7), 394-405.
Stokes, M., Buschman, T.J., and Miller, E.K. (in press) Dynamic coding for flexible cognitive control. Wiley Handbook of Cognitive Control.
Randoph Helfrich and Robert Knight review evidence that the infrastructure of cognitive control is rhythmic. The general idea is that the prefrontal cortex controls large-scale oscillatory dynamics in the cortex and subcortex. But there is much more. Do yourself a favor: Read it.
Helfrich, R. F., & Knight, R. T. (2016). Oscillatory Dynamics of Prefrontal Cognitive Control. Trends in Cognitive Sciences.
Alavash et al show how changes in network dynamics in the beta (16-28 Hz) band. Faster perceptual decisions occurred when beta-coupling became more local than global. The also found different network states in different cortical areas were associated with faster decisions. This paper lends support for recent suggestions that cortical communication is regulated via beta synchrony.
Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision making
Mohsen Alavash, Christoph Daube, Malte Woestmann, Alex Brandmeyer, Jonas Obleser
doi: https://doi.org/10.1101/095356
See also:
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 »
Buschman T.J., Miller E.K. (2014) Goal-direction and top-down control. Philos Trans R Soc Lond B Biol Sci. 2014 Nov 5;369(1655). View PDF »
Antzoulatos, E. G., & Miller, E. K. (2016). Synchronous beta rhythms of frontoparietal networks support only behaviorally relevant representations. eLife, 5, e17822.
Abstract:
Categorization has been associated with distributed networks of the primate brain, including the prefrontal cortex (PFC) and posterior parietal cortex (PPC). Although category-selective spiking in PFC and PPC has been established, the frequency-dependent dynamic interactions of frontoparietal networks are largely unexplored. We trained monkeys to perform a delayed-match-to-spatial-category task while recording spikes and local field potentials from the PFC and PPC with multiple electrodes. We found category-selective beta- and delta-band synchrony between and within the areas. However, in addition to the categories, delta synchrony and spiking activity also reflected irrelevant stimulus dimensions. By contrast, beta synchrony only conveyed information about the task-relevant categories. Further, category-selective PFC neurons were synchronized with PPC beta oscillations, while neurons that carried irrelevant information were not. These results suggest that long-range beta-band synchrony could act as a filter that only supports neural representations of the variables relevant to the task at hand.
Here’s Why You Shouldn’t Multitask, According to a MIT Neuroscientist – Fortune, December 7, 2016
Stanley, D.A., Roy, J.E., Aoi, M.C., Kopell, N.J., and Miller, E.K. (2016) Low-beta oscillations turn up the gain during category judgments. Cerebral Cortex. doi: 10.1093/cercor/bhw356 View PDF
Abstract:
Synchrony between local field potential (LFP) rhythms is thought to boost the signal of attended sensory inputs. Other cognitive functions could benefit from such gain control. One is categorization where decisions can be difficult if categories differ in subtle ways. Monkeys were trained to flexibly categorize smoothly varying morphed stimuli, using orthogonal boundaries to carve up the same stimulus space in 2 different ways. We found evidence for category-specific patterns of low-beta (16–20 Hz) synchrony in the lateral prefrontal cortex (PFC). This synchrony was stronger when a given category scheme was relevant. We also observed an overall increase in low-beta LFP synchrony for stimuli that were near the category boundary and thus more difficult to categorize. Beta category selectivity was evident in partial field–field coherence measurements, which measure local synchrony, but the boundary enhancement was not. Thus, it seemed that category selectivity relied on local interactions while boundary enhancement was a more global effect. The results suggest that beta synchrony helps form category ensembles and may reflect recruitment of additional cortical resources for categorizing challenging stimuli, thus serving as a form of gain control.
Miller Lab alum Andreas Nieder and crew show how dopamine receptors in the prefrontal cortex regulate access to working memory and its protection from interference.
Jacob, Simon N., Maximilian Stalter, and Andreas Nieder. “Cell-type-specific modulation of targets and distractors by dopamine D1 receptors in primate prefrontal cortex.” Nature Communications (2016): 13218.
Castejon and Nunez propose a theoretical framework in which cortical oscillations produce computation by quantizing information into “discrete results”. Interesting stuff.
Castejon, Carlos, and Angel Nuñez. “Cortical Neural Computation by Discrete Results Hypothesis.”