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 »

 

High frequency waves (Davis on trumpet) carry sensory inputs from the back of the brain to the front. Low frequency waves (Mingus on bass) carry executive (top-down) information from the front to the back of the brain. The low frequencies control the expression of high frequencies. That’s how you choose what sensory inputs to hold in mind (working memory). (Image: Andre Bastos)

It makes sense because we all know that the bass should guide the lead instruments. Am I right?

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. published ahead of print January 16, 2018, doi:10.1073/pnas.1710323115

Read MIT press release here.

 

Persistent activity (indexed by broadband gamma) across human cortex encodes stimulus features and predicts motor output.

Haller, Matar, John Case, Nathan E. Crone, Edward F. Chang, David King-Stephens, Kenneth D. Laxer, Peter B. Weber, Josef Parvizi, Robert T. Knight, and Avgusta Y. Shestyuk. “Persistent neuronal activity in human prefrontal cortex links perception and action.” Nature Human Behaviour (2017): 1.

But how persistent is it?
Lundqvist, 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 »

Coarse visuospatial categories are represented in the posterior parietal cortex whereas fine-scale discrimination are in primary visual cortex with the latter depending on feedback from the former.

Li, Y., Hu, X., Yu, Y., Zhao, K., Saalmann, Y. B., & Wang, L. (2017). Feedback from human posterior parietal cortex enables visuospatial category representations as early as primary visual cortexBrain and Behavior.

The authors report different effects of stimulation of the lateral prefrontal cortex.  Stimulation at or near the FEF prolonged or decreased saccade reaction time, depending on task instructions.  More rostral stimulation affected the attention weighting of saccade targets.

Schwedhelm, P., Baldauf, D., & Treue, S. (2017). Electrical stimulation of macaque lateral prefrontal cortex modulates oculomotor behavior indicative of a disruption of top-down attentionScientific reports7(1), 17715.

Gamma and beta bursts during working memory readout suggest roles in its volitional control
Lundqvist et al  Nature Communications, in press.

Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory
Bastos et al   PNAS, in press

Different levels of category abstraction by different dynamics in different prefrontal areas
Wutz et al   Neuron, in press

Stay tuned for what they are about and what they mean.  They add up to a new model of working memory.

New paper accepted:
“Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory”
Bastos, Loonis, Kornblith, Lundqvist, and Miller. PNAS, in press  
Coming soon. Stay tuned.

The authors suggest a hybrid model of working memory.  The current focus of attention is encoded by spiking activity.  Other items held in the working memory that are not the current focus of attention are held by temporary changes in synaptic weights per the activity-silent models of Lundqvist and Stokes.

Manohar, S. G., Zokaei, N., Fallon, S. J., Vogels, T., & Husain, M. (2017). A neural model of working memorybioRxiv, 233007.

For more on activity-silent models, 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 »

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, (Chichester, West Sussex, UK). View PDF

Wasmuht, D. F., Spaak, E., Buschman, T. J., Miller, E. K., & Stokes, M. G. (2017). Intrinsic neuronal dynamics predict distinct functional roles during working memorybioRxiv, 233171.

Stokes, M. G. (2015). ‘Activity-silent’working memory in prefrontal cortex: a dynamic coding frameworkTrends in Cognitive Sciences19(7), 394-405.

Cavanagh et al test the sustained vs dynamic activity models of working memory.  They find that sustained activity does not maintain the memory of a cue location/response past a distractor.  Instead of sustained activity, they conclude that a dynamic population-level process underlies working memory.

Cavanagh, S. E., Towers, J. P., Wallis, J. D., Hunt, L. T., & Kennerley, S. W. (2017). Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex. bioRxiv, 231506.

Intrinsic neuronal dynamics predict distinct functional roles during working memory
Dante Francisco Wasmuht, Eelke Spaak, Timothy J. Buschman, Earl K. Miller, Mark G. Stokes
doi: https://doi.org/10.1101/233171

Abstract
Working memory (WM) is characterized by the ability to maintain stable representations over time; however, neural activity associated with WM maintenance can be highly dynamic. We explore whether complex population coding dynamics during WM relate to the intrinsic temporal properties of single neurons in lateral prefrontal cortex (lPFC), the frontal eye fields (FEF) and lateral intraparietal cortex (LIP) of two monkeys (Macaca mulatta). We found that cells with short timescales carried memory information relatively early during memory encoding in lPFC; whereas long timescale cells played a greater role later during processing, dominating coding in the delay period. We also observed a link between functional connectivity at rest and intrinsic timescale in FEF and LIP. Our results indicate that individual differences in the temporal processing capacity predicts complex neuronal dynamics during WM; ranging from rapid dynamic encoding of stimuli to slower, but stable, maintenance of mnemonic information.

Low-frequency synchrony between the anterior cingulate and orbitofrontal cortex is diminished when errors are made.

Fatahi, Z., Haghparast, A., Khani, A., & Kermani, M. (2017). Functional connectivity between Anterior Cingulate cortex and Orbitofrontal cortex during value-based decision making. Neurobiology of Learning and Memory.

An interesting contrast between the prefrontal cortex (PFC) and medial temporal lobe (MTL) in encoding temporal order.  PFC neurons showed stronger “mixed selectivity” type encoding. They responded to a combination of an item and the order in which in appeared, only responding to specific items at specific times.  By contrast, MTL neurons were mainly item-selective.  They typically responded to an item, regardless of its order, but their firing rate was modulated by order.

Naya, Y., Chen, H., Yang, C., & Suzuki, W. A. (2017). Contributions of primate prefrontal cortex and medial temporal lobe to temporal-order memory. Proceedings of the National Academy of Sciences, 201712711.

Further reading on 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 »

A review by Mike Halassa and Sabine Kastner about our emerging understanding of the role of the thalamus in cognitive control.

Halassa, M. M., & Kastner, S. (2017). Thalamic functions in distributed cognitive control. Nature Neuroscience, 20(12), 1669.

The Dean of Prefrontal Cortex, Joaquin Fuster, breaks down prefrontal function along three lines: Executive attention, working memory, and decision-making.

Fuster, J. M. (2017). Prefrontal Executive Functions Predict and Preadapt. In Executive Functions in Health and Disease(pp. 3-19).

A thoughtful review and discussion of the issues involved in analyzing brain rhythms.

Jones, S. R. (2016). When brain rhythms aren’t ‘rhythmic’: implication for their mechanisms and meaning. Current opinion in neurobiology40, 72-80.

Working memory in crows.  Many of the same neural properties as primates.

Hartmann, K., Veit, L., & Nieder, A. (2017). Neurons in the crow nidopallium caudolaterale encode varying durations of visual working memory periodsExperimental Brain Research, 1-12.

 

A model in which local parallel processors assemble to produce goal-directed behavior.   A performance bottleneck comes from the routing stage, which learns to map inputs onto motor representations.  This is very much like mixed-selectivity models of cortex.

Zylberberg, A., Slezak, D. F., Roelfsema, P. R., Dehaene, S., & Sigman, M. (2010). The brain’s router: a cortical network model of serial processing in the primate brainPLoS computational biology6(4), e1000765.

For more about 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 »

Enel et al use reservoir computing to understand how mixed selectivity dynamic in the prefrontal cortex support complex, flexible behavior.  Reservoir computing (like mixed selectivity) involves inputs fed to a dynamical system that learns only at the output stage.  They argue that this approach is good framework for understanding how cortical dynamics produce higher cognitive functions.

Enel, P., Procyk, E., Quilodran, R., & Dominey, P. F. (2016). Reservoir computing properties of neural dynamics in prefrontal cortexPLoS computational biology12(6), e1004967.

For more about 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 »

There is growing evidence that bottom-up sensory inputs are associated with gamma oscillations (30-120 Hz) while top-down control depends on lower frequencies from delta through beta (1-30 Hz).  This review argues that phase-phase synchrony across different frequencies integrates, coordinates, and regulates the neural assemblies in different frequency bands.

Palva, J. M., & Palva, S. (2017). Functional integration across oscillation frequencies by cross‐frequency phase synchronizationEuropean Journal of Neuroscience.

Groovy new paper from Erin Rich and Joni Wallis.  They tested the relationship between information encoding in high gamma with that in spiking activity of neurons.  They encode similar information but neurons only contribute to a small increase in gamma.  Plus, there are large-scale temporal dynamics that can only be seen in gamma.  In other words, might as well study gamma.

Rich, E. L., & Wallis, J. D. (2017). Spatiotemporal dynamics of information encoding revealed in orbitofrontal high-gammaNature Communications8(1), 1139.

 

Do rodents have one?  The answer is not straightforward.  Marie Carlén reviews the data for us.

Carlén, M. (2017). What constitutes the prefrontal cortex?Science358(6362), 478-482.