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.

Interesting new results from Charan Ranganath and crew.  They show changes in oscillatory dynamics in humans as they learn new visuomotor associations.  There was a decrease in theta and an increase in alpha oscillations, much as has been seen in animals.

Clarke, A., Roberts, B. M., & Ranganath, C. (2017). Neural oscillations during conditional associative learningbioRxiv, 198838.

For further reading:
Loonis, R.F, Brincat, S.L., Antzoulatos, E.G., and Miller, E.K. (2017) A meta-analysis suggests different neural correlates for implicit and explicit learning. Neuron. 96:521-534  View PDF

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

There were clear differences.

Viswanathan, P., & Nieder, A. (2017). Comparison of visual receptive fields in the dorsolateral prefrontal cortex (dlPFC) and ventral intraparietal area (VIP) in macaques. European Journal of Neuroscience.

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: https://doi.org/10.1523/JNEUROSCI.1222-17.2017   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 »

Tomorrow, we say good bye to a great scientist and a great person.  I am going to miss you, Howard.

Remembering Howard Eichenbaum

 

Feb 14-17, 2018 in Washington, DC.  See you there!

INS Washington DC 2018

An example of mixed selectivity in a network model trained on 20 different cognitive tasks.
Yang, G. R., Song, H. F., Newsome, W. T., & Wang, X. J. (2017). Clustering and compositionality of task representations in a neural network trained to perform many cognitive tasksbioRxiv, 183632.

To learn more about mixed selectivity and its importance for cognition, see these papers:
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 »

There is a growing consensus that there may be more to working memory than simple maintenance of spiking.  On a single-trial, moment-to-moment basis, memory delay spiking is sparse, not sustained.  Instead, spiking may produce changes in synaptic weights and that is where the working memories are actually stored.

Trübutschek, D., Marti, S., Ojeda, A., King, J. R., Mi, Y., Tsodyks, M., & Dehaene, S. (2017). A theory of working memory without consciousness or sustained activity. Elife, 6.

For further reading 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 »

Review of the neural mechanisms behind persistent spiking activity and working memory.

Zylberberg, J., & Strowbridge, B. W. (2017). Mechanisms of persistent activity in cortical circuits: possible neural substrates for working memory. Annual Review of Neuroscience40.

There is little doubt that spiking during memory delays play a role in working memory.  But how persistent is the activity and how are the memories actually stored?  For another perspective 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 »

The title says it all.

Slater, J., Ashley, R., Tierney, A., & Kraus, N. (2017). Got Rhythm? Better Inhibitory Control Is Linked with More Consistent Drumming and Enhanced Neural Tracking of the Musical Beat in Adult Percussionists and NonpercussionistsJournal of Cognitive Neuroscience.

Due to a disruption of top-down attentional amplification.

Berkovitch, L., Dehaene, S., & Gaillard, R. (2017). Disruption of Conscious Access in SchizophreniaTrends in Cognitive Sciences.

Gradual progression from sensory to task-related processing in cerebral cortex
Scott L. Brincat*, Markus Siegel*, Constantin von Nicolai, Earl K. Miller
doi: https://doi.org/10.1101/195602

Abstract

Somewhere along the cortical hierarchy, behaviorally relevant information is distilled from raw sensory inputs. We examined how this transformation progresses along multiple levels of the hierarchy by comparing neural representations in visual, temporal, parietal, and frontal cortices in monkeys categorizing across three visual domains (shape, motion direction, color). Representations in visual areas MT and V4 were tightly linked to external sensory inputs. In contrast, prefrontal cortex (PFC) largely represented the abstracted behavioral relevance of stimuli (task rule, motion category, color category). Intermediate-level areas — posterior inferotemporal (PIT), lateral intraparietal (LIP), and frontal eye fields (FEF) — exhibited mixed representations. While the distribution of sensory information across areas aligned well with classical functional divisions — MT carried stronger motion information, V4 and PIT carried stronger color and shape information — categorical abstraction did not, suggesting these areas may participate in different networks for stimulus-driven and cognitive functions. Paralleling these representational differences, the dimensionality of neural population activity decreased progressively from sensory to intermediate to frontal cortex. This shows how raw sensory representations are transformed into behaviorally relevant abstractions and suggests that the dimensionality of neural activity in higher cortical regions may be specific to their current task.

A review of how the prefrontal cortex and high-level visual cortex interact during perception.

Kornblith, S., & Tsao, D. Y. (2017). How thoughts arise from sights: inferotemporal and prefrontal contributions to vision. Current Opinion in Neurobiology, 46, 208-218.