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.

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.

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.

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.

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.

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.


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.

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.

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 »

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.

Lindsay, G.W., Rigotti, M., Warden, M.R., Miller, E.K., and Fusi, S. (2017) Hebbian Learning in a Random Network Captures Selectivity Properties of Prefrontal CortexJournal of Neuroscience.  6 October 2017, 1222-17; DOI:   View PDF

A Meta-Analysis Suggests Different Neural Correlates for Implicit and Explicit Learning
Roman F. Loonis, Scott L. Brincat, Evan G. Antzoulatos, Earl K. Miller
Neuron, 96(2): p521-534, 2017.

Preview by Matthew Chafee and David Crowe:
Implicit and Explicit Learning Mechanisms Meet in Monkey Prefrontal Cortex

Parthasarathy et al found that a distractor stimulus caused neural representations in the prefrontal cortex to morph into a different pattern but while still retaining information about the item in memory.  This was due to mixed selectivity neurons.  By contrast, the FEF had less mixed selectivity and the distractor caused it to lose information.  Nice.

Mixed selectivity morphs population codes in prefrontal cortex
Aishwarya Parthasarathy, Roger Herikstad, Jit Hon Bong, Felipe Salvador Medina, Camilo Libedinsky & Shih-Cheng Yen
Nature Neuroscience (2017)

For further reading about mixed selectivity:
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 »

Increased theta synchrony between the prefrontal cortex and hippocampus when subjects encoded unexpected study items.  This is further evidence that theta-band (6-10 Hz) oscillations orchestrate communication between these brain areas.

Gruber, M. J., Hsieh, L. T., Staresina, B., Elger, C., Fell, J., Axmacher, N., & Ranganath, C. (2017). Theta Phase Synchronization Between The Human Hippocampus And The Prefrontal Cortex Supports Learning Of Unexpected InformationbioRxiv, 144634.

For further reading:

Brincat, S.L. and Miller, E.K. (2015)  Frequency-specific hippocampal-prefrontal interactions during associative learning.  Nature Neuroscience. Published online 23 Feb 2015 doi:10.1038/nn.3954. View PDF »

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

Miller Lab Alumnus, Wael Asaad, shows that neurons in the prefrontal cortex can figure out which prior events get credit for the consequences of our actions.

Asaad, W. F., Lauro, P. M., Perge, J. A., & Eskandar, E. N. (2017). Prefrontal Neurons Encode a Solution to the Credit-Assignment ProblemJournal of Neuroscience37(29), 6995-7007.

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


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 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

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

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

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.

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.

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

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.

For much of the history of modern neuroscience, it has been a assumed that the neuron is the functional unit of the brain.  But now there is increasing evidence that ensembles of neurons, not individuals, are the functional units.  One line of evidence is that many neurons in higher cortical areas have “mixed selectivity” , responses to diverse combinations of variables; they don’t signal one “message”.  Thus, their activity only makes sense when simultaneously considering the activity of other neurons.  In fact, we (Rigotti et al., 2013; Fusi et al., 2016) have shown that mixed selectivity gives the brain the computational horsepower needed for complex behavior.

In this paper, Dehaqani et al show that simultaneously recorded prefrontal cortex neurons have high-dimensional, mixed-selectivity, representations and convey more information as a population than even individuals.  This was especially true for parts of visual space that were weakly encoded by single neurons.  Less-informative neurons were recruited into ensemble to fully encode visual space.

Prefrontal neurons expand their representation of space by increase in dimensionality and decrease in noise correlation.  Mohammad-Reza Dehaqani, Abdol-Hossein Vahabie, Mohammadbagher Parsa, Behrad Noudoost, Alireza Soltani

Further reading:
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 »

Yuste, Rafael. “From the neuron doctrine to neural networks.” Nature Reviews Neuroscience 16.8 (2015): 487-497.

Woolgar et al provide a meta-analysis of experiments using multivoxel pattern analysis in FMRI.  They show that cortical areas traditionally though to be visual, auditory or motor, primarily (though not exclusively) code visual, auditory, and motor information.  However, the frontoparietal cortex is hypothesized to a multiple-demand network and it shows domain generality, coding multisensory and rule information.

Woolgar, Alexandra, Jade Jackson, and John Duncan. “Coding of visual, auditory, rule, and response information in the brain: 10 years of multivoxel pattern analysis.” Journal of cognitive neuroscience (2016).

Tsutsui et al shows how the prefrontal cortex integrates rule and category information for a behavioral decision.

Tsutsui, Ken-Ichiro, et al. “Representation of Functional Category in the Monkey Prefrontal Cortex and Its Rule-Dependent Use for Behavioral Selection.” The Journal of Neuroscience 36.10 (2016): 3038-3048.