Beta rhythms play a role in synaptic plasticity.

Zanos, S., Rembado, I., Chen, D., & Fetz, E. E. (2018). Phase-Locked Stimulation during Cortical Beta Oscillations Produces Bidirectional Synaptic Plasticity in Awake MonkeysCurrent Biology.

Super-cool paper by Andreas Nieder and crew.  Frontal-parietal beta synchrony encodes the most recent numerical input.  Theta synchrony distinguishes between different numerosities held in working memory.  The spiking of mixed-selectivity neurons multiplexed both task-relevant and irrelevant stimuli but they were separated in different phases of theta oscillations.  Powerful support that neural oscillations functionally organize spiking activty.

Jacob, S. N., Hähnke, D., & Nieder, A. (2018). Structuring of Abstract Working Memory Content by Fronto-parietal Synchrony in Primate CortexNeuron99(3), 588-597.

More evidence for mixed-selectivity in the cortex.  This time with voxels in the human brain.

Jackson, J., & Woolgar, A. (2018). Adaptive coding in the human brain: Distinct object features are encoded by overlapping voxels in frontoparietal cortex. Cortex.

Read more about mixed selectivity:
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 »        

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 »

Enhanced prefrontal-hippocampal spike-LFP coupling during learning of a spatial strategy (but not other strategies).

Negrón-Oyarzo, I., Espinosa, N., Aguilar, M., Fuenzalida, M., Aboitiz, F., & Fuentealba, P. (2018). Coordinated prefrontal–hippocampal activity and navigation strategy-related prefrontal firing during spatial memory formationProceedings of the National Academy of Sciences, 201720117.

A computational model of visual categorization in cortex that has properties similar to our lab’s results.  It must be true.

Abe, Y., Fujita, K., & Kashimori, Y. (2018). Visual and Category Representations Shaped by the Interaction Between Inferior Temporal and Prefrontal CorticesCognitive Computation, 1-16.

Big-ass survey of cortex by Gray and crew:

Dotson, N. M., Hoffman, S. J., Goodell, B., & Gray, C. M. (2018). Feature-Based Visual Short-Term Memory Is Widely Distributed and Hierarchically OrganizedNeuron.

Nice paper by Bressler and colleagues showing that top-down influences on visual cortex are mediated by beta-band oscillations.

Richter, C. G., Coppola, R., & Bressler, S. L. (2018). Top-down beta oscillatory signaling conveys behavioral context in early visual cortex. Scientific reports, 8(1), 6991.

Further reading on beta oscillations mediating top-down processing:
Buschman, T.J. and Miller, E.K. (2007) Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science. 315: 1860-1862  View PDF »

Bastos, A.M., Loonis, R., Kornblith, S., Lundqvist, M., and Miller, E.K. (2018)  Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory.  Proceedings of the National Academy of Sciences.  View PDF

 

Nice result from Buschman Lab.  Error-correcting dynamics introduce bias into working memory while reducing noise.

Error-correcting dynamics in visual working memory
Matthew F Panichello, Brian DePasquale, Jonathan W Pillow, Timothy Buschman
doi: https://doi.org/10.1101/319103

Press release for our new paper:
A heavy working memory load may sink brainwave ‘synch’

The paper:
Pinotsis, D.A., Buschman, T.J. and Miller, E.K. (2018) Working Memory Load Modulates Neuronal Coupling. Cerebral Cortex.  https://doi.org/10.1093/cercor/bhy065  View PDF

Pinotsis, D.A., Buschman, T.J. and Miller, E.K. (2018) Working Memory Load Modulates Neuronal Coupling. Cerebral Cortex, 2018 https://doi.org/10.1093/cercor/bhy065

Abstract: There is a severe limitation in the number of items that can be held in working memory. However, the neurophysiological limits remain unknown. We asked whether the capacity limit might be explained by differences in neuronal coupling. We developed a theoretical model based on Predictive Coding and used it to analyze Cross Spectral Density data from the prefrontal cortex (PFC), frontal eye fields (FEF), and lateral intraparietal area (LIP). Monkeys performed a change detection task. The number of objects that had to be remembered (memory load) was varied (1–3 objects in the same visual hemifield). Changes in memory load changed the connectivity in the PFC–FEF–LIP network. Feedback (top-down) coupling broke down when the number of objects exceeded cognitive capacity. Thus, impaired behavioral performance coincided with a break-down of Prediction signals. This provides new insights into the neuronal underpinnings of cognitive capacity and how coupling in a distributed working memory network is affected by memory load.

Freedman and Ibos give us a new general framework to think about the functions of the parietal cortex.

Freedman, D. J., & Ibos, G. (2018). An Integrative Framework for Sensory, Motor, and Cognitive Functions of the Posterior Parietal CortexNeuron97(6), 1219-1234.

Miller Lab alumnus Jonas Rose compares cognitive capacity across species.  Note that cognitive capacity correlates with intelligence but it is not the same thing.

Balakhonov, D., & Rose, J. (2017). Crows Rival Monkeys in Cognitive Capacity. Scientific reports, 7(1), 8809.

The effects of attention in the brain can be partitioned into changes in sensitivity of in the subject’s criterion.  In visual cortex, only changes in sensitivity are seen.  Here, Luo and Maunsell show that neurons in frontal cortex are sensitive to changes in sensitivity as well as criterion.

Luo, T. Z., & Maunsell, J. H. (2018). Attentional Changes in Either Criterion or Sensitivity Are Associated with Robust Modulations in Lateral Prefrontal Cortex. Neuron.

An article in Science News about new ideas on the role of brain waves.  It also discuss three new papers from the Miller Lab.

Brain waves may focus attention and keep information flowing  Science New March 13, 2018

Here are the papers that are discussed:

Lundqvist, M., Herman, P. Warden, M.R., Brincat, S.L., and Miller, E.K. (2018) Gamma and beta bursts during working memory read-out suggest roles in its volitional control. Nature Communications. 9, 394   View PDF

Wutz, A., Loonis, R., Roy, J.E., Donoghue, J.A., and Miller, E.K. (2018)  Different levels of category abstraction by different dynamics in different prefrontal areas. Neuron 97: 1-11.  View PDF

Bastos, A.M., Loonis, R., Kornblith, S., Lundqvist, M., and Miller, E.K. (2018)  Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory.  Proceedings of the National Academy of Sciences.  View PDF

Alexander and Brown show how frontal lobe function can be explained by a hierarchical stack of a computational motif based on predictive coding.

Alexander, W. H., & Brown, J. W. (2018). Frontal cortex function as derived from hierarchical predictive coding. Scientific reports, 8(1), 3843.

Dopamine alters the neural oscillations associated with executive functions but leave sensory-related evoked potential unchanged.

Ott, T., Westendorff, S., & Nieder, A. (2018). Dopamine Receptors Influence Internally Generated Oscillations during Rule Processing in Primate Prefrontal Cortex. Journal of cognitive neuroscience, (Early Access), 1-15.

27 Feb 2018
February 27, 2018

Neuromodulation of Attention

Neuroscience

A very nice review of how neuromodulation affects the mechanisms and circuits underlying attention.

Thiele, A., & Bellgrove, M. A. (2018). Neuromodulation of AttentionNeuron97(4), 769-785.

Martínez-Vázquez and Gail show different channels of influence in different frequency bands between frontal and parietal cortex.

Martínez-Vázquez, P., & Gail, A. (2018). Directed Interaction Between Monkey Premotor and Posterior Parietal Cortex During Motor-Goal Retrieval from Working Memory. Cerebral Cortex.

Interesting new study from the Moore Lab showing how spatial information is evident in different frequency bands in the prefrontal cortex. They also show a dissociation between high gamma/spiking and alpha.

Chen, X., Zirnsak, M., & Moore, T. (2018). Dissonant Representations of Visual Space in Prefrontal Cortex during Eye MovementsCell Reports22(8), 2039-2052.

Interesting new work from Ito and Cole showing how network connectivity patterns is associated with representational flexibility.

Ito, T., & Cole, M. W. (2018). Network dimensionality underlies flexible representation of cognitive informationbioRxiv, 262626.

Interesting study showing that there are decreases in the frequency of alpha oscillations when a task requires require integration of two inputs that are separated in time.  The slowing fosters integration by making it more likely that two stimuli fall within one alpha cycle and are thus integrated.  Cool.

Wutz, A., Melcher, D., & Samaha, J. (2018). Frequency modulation of neural oscillations according to visual task demandsProceedings of the National Academy of Sciences, 201713318.

Lundqvist, M., Herman, P. Warden, M.R., Brincat, S.L., and Miller, E.K. (2018) Gamma and beta bursts during working memory read-out suggest roles in its volitional control. Nature Communications. 9, Article number: 394 doi:10.1038/s41467-017-02791-8

Abstract:
Working memory (WM) activity is not as stationary or sustained as previously thought. There are brief bursts of gamma (~50–120 Hz) and beta (~20–35 Hz) oscillations, the former linked to stimulus information in spiking. We examined these dynamics in relation to readout and control mechanisms of WM. Monkeys held sequences of two objects in WM to match to subsequent sequences. Changes in beta and gamma bursting suggested their distinct roles. In anticipation of having to use an object for the match decision, there was an increase in gamma and spiking information about that object and reduced beta bursting. This readout signal was only seen before relevant test objects, and was related to premotor activity. When the objects were no longer needed, beta increased and gamma decreased together with object spiking information. Deviations from these dynamics predicted behavioral errors. Thus, beta could regulate gamma and the information in WM.

Wutz, A., Loonis, R., Roy, J.E., Donoghue, J.A., and Miller, E.K. (2018) Different levels of category abstraction by different dynamics in different prefrontal areas. Neuron  published online Jan 25 2018.

SUMMARY

Categories can be grouped by shared sensory attributes (i.e. cats) or by a more abstract rule (i.e. animals). We explored the neural basis of abstraction by recording from multi-electrode arrays in prefrontal cortex (PFC) while monkeys performed a dot-pattern categorization task. Category abstraction was varied by the degree of exemplar distortion from the prototype pattern. Different dynamics in different PFC regions processed different levels of category abstraction. Bottom-up dynamics (stimulus-locked gamma power and spiking) in ventral PFC processed more low-level abstractions whereas top-down dynamics (beta power and beta spike-LFP coherence) in dorsal PFC processed more high-level abstractions. Our results suggest a two-stage, rhythm-based model for abstracting categories.

A new addition to the proposed circuitry for top-down control.

White, M. G., Panicker, M., Mu, C., Carter, A. M., Roberts, B. M., Dharmasri, P. A., & Mathur, B. N. (2018). Anterior Cingulate Cortex Input to the Claustrum Is Required for Top-Down Action ControlCell reports22(1), 84-95.

The authors find that dopamine increased power of beta-low gamma oscillations in cortex.  During visual stimulation, dopamine increased information encoding over a wide range of frequencies but most prominently in the feedforward supragranular layers and in the gamma band (50-100 Hz).

Zaldivar, D., Goense, J., Lowe, S. C., Logothetis, N. K., & Panzeri, S. (2018). Dopamine Is Signaled by Mid-frequency Oscillations and Boosts Output Layers Visual Information in Visual CortexCurrent Biology.

This must be correct.  It is very remarkably consistent with our recent study 🙂
Bastos, A.M., Loonis, R., Kornblith, S., Lundqvist, M., and Miller, E.K. (2018)  Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory.  Proceedings of the National Academy of Sciencesdoi:10.1073/pnas.1710323115   View PDF

as well as with our previous work showing that gamma is associated with bottom-up processing:
Buschman, T.J. and Miller, E.K. (2007) Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science. 315: 1860-1862  View PDF »