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

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.”

Pinotsis, D.A., Loonis, R., Bastos, A. Miller, E.K, and Friston, K.J.  “Bayesian Modelling of Induced Responses and Neuronal Rhythms” Brain Topogr (2016). doi:10.1007/s10548-016-0526-y

Abstract:
Neural rhythms or oscillations are ubiquitous in neuroimaging data. These spectral responses have been linked to several cognitive processes; including working memory, attention, perceptual binding and neuronal coordination. In this paper, we show how Bayesian methods can be used to finesse the ill-posed problem of reconstructing—and explaining—oscillatory responses. We offer an overview of recent developments in this field, focusing on (i) the use of MEG data and Empirical Bayes to build hierarchical models for group analyses—and the identification of important sources of inter-subject variability and (ii) the construction of novel dynamic causal models of intralaminar recordings to explain layer-specific activity. We hope to show that electrophysiological measurements contain much more spatial information than is often thought: on the one hand, the dynamic causal modelling of non-invasive (low spatial resolution) electrophysiology can afford sub-millimetre (hyper-acute) resolution that is limited only by the (spatial) complexity of the underlying (dynamic causal) forward model. On the other hand, invasive microelectrode recordings (that penetrate different cortical layers) can reveal laminar-specific responses and elucidate hierarchical message passing and information processing within and between cortical regions at a macroscopic scale. In short, the careful and biophysically grounded modelling of sparse data enables one to characterise the neuronal architectures generating oscillations in a remarkable detail.

Morocos and Harvey reveal new depths in ongoing activity in parietal cortex in mice.  Information about cues, behavioral choices, etc were not represented by single neurons in a winner-take fashion (the traditional view).  Rather, different information is added to on-going patterns of activity that reflect the history of recent events. This could only be revealed via analysis of activity on single trials.

Morcos, Ari S., and Christopher D. Harvey. “History-dependent variability in population dynamics during evidence accumulation in cortex.” Nature Neuroscience (2016).

For another example of how single-trial analysis reveals much more than across-trial averaging, see:

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 »

Cavanagh et al show that characterizing the temporal receptive field of integration of individual PFC neurons from their resting activity (via autocorrelation) helps predict their coding for value.  In short, taking into account the temporal dynamics of neuron spiking yields more information about their role in representing value than spike rates alone.

Cavanagh, Sean E., et al. “Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice.” eLife 5 (2016): e18937.

Abstract:
As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with “internal” memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)—regions critical for sensory associations—of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11–27 Hz) oscillatory power and synchrony associated with “top-down” or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing.

Brincat, S.L. and Miller, E.K (2016) Prefrontal networks shift from external to internal modes during learning  Journal of Neuroscience. 36(37): 9739-9754, 2016 doi: 10.1523/JNEUROSCI.0274-16.2016. View PDF

Mixed selectivity in dopamine neurons:
Tian, Ju, et al. “Distributed and Mixed Information in Monosynaptic Inputs to Dopamine Neurons.” Neuron (2016).

For more on the importance of mixed selectivity see:
Fusi, S., Miller, E.K., and Rigotti, M. (2016) Why neurons mix: High dimensionality for higher cognition.  Current Opinion in Neurobiology. 37:66-74  doi:10.1016/j.conb.2016.01.010. View PDF »

Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. (2013) The importance of mixed selectivity in complex cognitive tasks. Nature, 497, 585-590, doi:10.1038/nature12160. View PDF »

Ott and Nieder show that stimulating dopamine D2 receptors enhancing working memory related activity in the prefrontal cortex.

Ott, Torben, and Andreas Nieder. “Dopamine D2 Receptors Enhance Population Dynamics in Primate Prefrontal Working Memory Circuits.”Cerebral Cortex (2016).

A very nice experiment from Matt Chafee et al (as usual).  They show that neurons in the prefrontal cortex don’t have fixed properties.  Instead, they show “mixed selectivity” that changes with behavioral context and is biased toward stimuli that inhibit prepotent responses.  Sounds like cognitive control to me.

Blackman, Rachael K., et al. “Monkey prefrontal neurons reflect logical operations for cognitive control in a variant of the AX continuous performance task (AX-CPT).” The Journal of Neuroscience 36.14 (2016): 4067-4079.