A trial by trial analysis showed that beta bursts, as opposed to power averaged across trials, is a good predictor of variations in motor behavior.

Torrecillos, F., Tinkhauser, G., Fischer, P., Green, A. L., Aziz, T. Z., Foltynie, T., … & Tan, H. (2018). Modulation of beta bursts in the subthalamic nucleus predicts motor performance. Journal of Neuroscience, 38(41), 8905-8917.

Nice review from Miler Lab alumnus Joni Wallis arguing for the importance of single-trial analyses.  Variability across trials may not be noise, it may be cognition.   Joni argues that ensembles, not single neurons, are the fundamental unit in the brain.  One needs to record from many neurons simultaneously to understand cognitive processes.

Wallis, J. D. (2018). Decoding Cognitive Processes from Neural EnsemblesTrends in Cognitive Sciences.

 

This review highlights work showing that spectrally distributed oscillations and their coupling have functional relevance for sensorimotor processing.

Palva, S., & Palva, J. M. (2018). Roles of brain criticality and multiscale oscillations in temporal predictions for sensorimotor processing. Trends in Neurosciences, 41(10), 729-743.

Nice FMRI study showing that working memory delay activity is primarily in the superficial, feedforward, cortical layers while behavioral response-related activity is primarily in deep, feedback layers.

Layer-dependent activity in human prefrontal cortex during working memory
Emily S. Finn, Laurentius Huber, David C. Jangraw, Peter A. Bandettini
doi: https://doi.org/10.1101/425249

This is very consistent with our recent work:
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

 

Holmes, C.D., Papadimitriou, C.,  Snyder, L.H.(2018)  Dissociation of LFP Power and Tuning in the Frontal Cortex during Memory  Journal of Neuroscience

Nice paper. Well done.  But with a caveat. The authors show that absolute power is dissociated from neural tuning in spiking activity.  From this, they conclude that “oscillatory activity by itself is likely not a substrate of memory” and “may be an epiphenomenon of a rate code in the circuit, rather than a direct substrate”.

Not quite.  No one is claiming that absolute power alone carries specific information. Rather, it is *patterns of coherence* that carry information (e.g., Buschman et al., 2012; Salazar et al 2012; Antzoulatos and Miller, 2014).  If so, there is no reason to think that information would be carried by absolute power.  For example, two different patterns of coherence for two different items could have equal global power because it is the pattern, not the global power, that matters.  In fact, we and others have shown that coherence and power can be dissociated (Buschman et al., 2012).  Using absolute power as a proxy to argue against a functional role for oscillations is a “straw man” argument. It tests a hypothesis that does not reflect the state-of-the-art of thinking on this matter.

Further reading:
Antzoulatos, E.G. and Miller, E.K. (2014) Increases in functional connectivity between the prefrontal cortex and striatum during category learning.  Neuron, 83:216-225. View PDF »

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 »

Salazar, R.F., Dotson, N.M., Bressler, S.L., and Gray, C.M. (2012). Content-Specific Fronto-Parietal Synchronization During Visual Working Memory. Science 1224000

Another point:  The reason they see “tuning” for contra vs ipsilateral targets in power is not because of stimulus tuning per se, it is because the right vs left visual hemifields are somewhat independent.  See:
Buschman,T.J., Siegel, M., Roy, J.E. and Miller, E.K. (2011) Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences. 108(27):11252-5. View PDF »

Kornblith, S., Buschman, T.J., and Miller, E.K. (2015)  Stimulus load and oscillatory activity in higher cortex. Cerebral Cortex. Published online August 18, 2015  doi: 10.1093/cercor/bhv182. View PDF »

Nice study showing that anterior parts of the prefrontal cortex and more plastic than posterior parts.

Anterior-posterior gradient of plasticity in primate prefrontal cortex
Mitchell R. Riley, Xue-Lian Qi, Xin Zhou & Christos Constantinidis
Nature Communications volume 9, Article number: 3790 (2018)

Zanos et al show that beta oscillations play a role in short-term synaptic plasticity in primate neocortex that may explain the role of oscillations in attention, learning, and cortical reorganization.

Zanos, S., Rembado, I., Chen, D., & Fetz, E. E. (2018). Phase-locked stimulation during cortical beta oscillations produces bidirectional synaptic plasticity in awake monkeys. Current Biology.

See discussion of this paper by Womelsdorf and Hoffman:
Latent Connectivity: Neuronal Oscillations Can Be Leveraged for Transient Plasticity

Bouchacourt and Buschman describe a two-layer model of working memory. A sensory layer feeds into an unstructured layer of neurons with random connections (i.e., “mixed-selectivity” type neurons).  It is flexible but interference between representations results in a capacity limit.  Sounds like working memory to me.

Bouchacourt, F., & Buschman, T. J. (2018). A Flexible Model of Working Memory. bioRxiv, 407700.

More 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 »

04 Sep 2018
September 4, 2018

Phase-coding memories in mind

Neuroscience

Nice summary of phase coding models of working memory by Hakim and Vogel, including a recent paper by Bahramisharif et al.

Hakim, N., & Vogel, E. K. (2018). Phase-coding memories in mindPLoS biology16(8), e3000012.

Bahramisharif, A., Jensen, O., Jacobs, J., & Lisman, J. (2018). Serial representation of items during working memory maintenance at letter-selective cortical sitesPLoS biology16(8), e2003805.

28 Aug 2018
August 28, 2018

Attention is rhythmic!

Neuroscience

Two new, exciting papers in Neuron that “put the last nail(s) in the coffin of sustained attention.”  They present compelling evidence that sustained attention is not sustained at all but fluctuates with theta rhythms and alpha/beta rhythms. This provides yet more evidence that the brain works by rhythmic switching between representations.

Ian C. Fiebelkorn, Mark A. Pinsk, Sabine Kastner

A Dynamic Interplay within the Frontoparietal Network Underlies Rhythmic Spatial Attention
Neuron, Volume 99, Issue 4, 22 August 2018, Pages 842-853.e8

Randolph F. Helfrich, Ian C. Fiebelkorn, Sara M. Szczepanski, Jack J. Lin, Josef Parvizi, Robert T. Knight, Sabine Kastner

Neural Mechanisms of Sustained Attention Are Rhythmic
Neuron, Volume 99, Issue 4, 22 August 2018, Pages 854-865.e5

An excellent Preview by Rufin VanRullen: Attention Cycles

For further reading:
Buschman, T.J. and Miller, E.K. (2009) Serial, covert, shifts of attention during visual search are reflected by the frontal eye fields and correlated with population oscillations. Neuron, 63: 386-396. View PDF »

Buschman,T.J. and Miller, E.K. (2010) Shifting the Spotlight of Attention: Evidence for Discrete Computations in Cognition. Frontiers in Human Neuroscience. 4(194): 1-9. View PDF »

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.