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.

“The Functional Architecture of Cognition Is Rhythmic”.  Indeed.

Randolph F. Helfrich, Robert T. Knight, Oscillatory Dynamics of Prefrontal Cognitive Control, In Trends in Cognitive Sciences, Volume 20, Issue 12, 2016, Pages 916-930, ISSN 1364-6613, https://doi.org/10.1016/j.tics.2016.09.007.

Rhythmic coupling across the cortex underlies perception.

Randolph F. Helfrich, Melody Huang, Guy Wilson, and Robert T. Knight
Prefrontal cortex modulates posterior alpha oscillations during top-down guided visual perception
PNAS 2017 114 (35) 9457-9462; published ahead of print August 14, 2017, doi:10.1073/pnas.1705965114

Distributed networks, not functional modules, are where it’s at in the 21st century.

This paper argues that decisions come from repeated computations that are distributed across many brain regions.  This fits with the distributed nature of neural coding.

Nat Rev Neurosci. 2017 Feb 17;18(3):172-182. doi: 10.1038/nrn.2017.7.
A distributed, hierarchical and recurrent framework for reward-based choice.
Hunt LT,  Hayden BY

For further reading:
Siegel, M., Buschman, T.J., and Miller, E.K. (2015) Cortical information flow during flexible sensorimotor decisions.  Science. 19 June 2015: 1352-1355. View PDF »

Neuron populations, not individual neurons, are where it’s at in the 21st century.

Iñigo Arandia-Romero, Ramon Nogueira, Gabriela Mochol, Rubén Moreno-Bote, What can neuronal populations tell us about cognition?, In Current Opinion in Neurobiology, Volume 46, 2017, Pages 48-57, ISSN 0959-4388, https://doi.org/10.1016/j.conb.2017.07.008.

 

Come see Pavlov’s Dogz at Songbyrd DC on Sunday Nov 12 of the SFN meeting.

9:30pm Songbyrd DC  11/12/17
http://www.songbyrddc.com/shows/2017-11-12-pavlolvs-dogz

Pavlov’s Dogz are a roaming band of neuroscientist-musicians who get together at conference locations around the world to play shows.
Band members:
Tim Bussey
Brad Postle
Earl Miller
Paula Croxson
Charan Ranganath
Joel Voss
Daniela Schiller
Jess Grahn
Mick Rugg
Andy Lee

We show how limitations in cognitive capacity (how many thoughts you can think at the same time – very few) may be due to changes in rhythmic coupling between cortical areas.  More specifically, feedback coupling breaks down when capacity is exceeded.

Working Memory Load Modulates Neuronal Coupling  Dimitris A Pinotsis, Timothy J Buschman, Earl K Miller
doi: https://doi.org/10.1101/192336

 

New manuscript submitted to bioRxiv:

Neuronal rhythms orchestrate cell assembles to distinguish perceptual categories
Morteza Moazami Goudarzi, Jason Cromer, Jefferson Roy, Earl K. Miller
doi: https://doi.org/10.1101/191247

Abstract
Categories are reflected in the spiking activity of neurons. However, how neurons form ensembles for categories is unclear. To address this, we simultaneously recorded spiking and local field potential (LFP) activity in the lateral prefrontal cortex (lPFC) of monkeys performing a delayed match to category task with two independent category sets (Animals: Cats vs Dogs; Cars: Sports Cars vs Sedans). We found stimulus and category information in alpha and beta band oscillations. Different category distinctions engaged different frequencies. There was greater spike field coherence (SFC) in alpha (~8-14 Hz) for Cats and in beta (~16-22 Hz) for Dogs. Cars showed similar differences, albeit less pronounced: greater alpha SFC for Sedans and greater beta SFC for Sports Cars. Thus, oscillatory rhythms can help coordinate neurons into different ensembles. Engagement of different frequencies may help differentiate the categories.

A very interesting theory from Randy O’Reilly and crew.  I don’t know how to summarize it better than they did in their abstract:

How does the neocortex learn and develop the foundations of all our high-level cognitive abilities? We present a comprehensive framework spanning biological, computational, and cognitive levels, with a clear theoretical continuity between levels, providing a coherent answer directly supported by extensive data at each level. Learning is based on making predictions about what the senses will report at 100 msec (alpha frequency) intervals, and adapting synaptic weights to improve prediction accuracy. The pulvinar nucleus of the thalamus serves as a projection screen upon which predictions are generated, through deep-layer 6 corticothalamic inputs from multiple brain areas and levels of abstraction. The sparse driving inputs from layer 5 intrinsic bursting neurons provide the target signal, and the temporal difference between it and the prediction reverberates throughout the cortex, driving synaptic changes that approximate error backpropagation, using only local activation signals in equations derived directly from a detailed biophysical model. In vision, predictive learning requires a carefully-organized developmental progression and anatomical organization of three pathways (What, Where, and What * Where), according to two central principles: top-down input from compact, high-level, abstract representations is essential for accurate prediction of low-level sensory inputs; and the collective, low-level prediction error must be progressively and opportunistically partitioned to enable extraction of separable factors that drive the learning of further high-level abstractions. Our model self-organized systematic invariant object representations of 100 different objects from simple movies, accounts for a wide range of data, and makes many testable predictions.

O’Reilly, R. C., Wyatte, D. R., & Rohrlich, J. (2017). Deep Predictive Learning: A Comprehensive Model of Three Visual Streams. arXiv preprint arXiv:1709.04654.

Paper showing different, yet complementary, effects of attention and value on alpha vs gamma oscillations in posterior cortex.

Marshall, T. R., den Boer, S., Cools, R., Jensen, O., Fallon, S. J., & Zumer, J. M. (2017). Occipital Alpha and Gamma Oscillations Support Complementary Mechanisms for Processing Stimulus Value AssociationsJournal of Cognitive Neuroscience.

And they show the same independence between the visual hemifields that we saw in primates.

Balakhonov, D., & Rose, J. (2017). Crows Rival Monkeys in Cognitive CapacityScientific Reports7.

For further reading:

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 »

Miller, E.K. and Buschman, T.J. (2015)  Working memory capacity: Limits on the bandwidth of cognition. Daedalus, Vol. 144, No. 1, Pages 112-122. 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 »

Driscoll et al tracked parietal cortex neurons over one month after mice learned and practiced a navigation task.  The activity of individual neurons changed but information on the population level was stable.  This is a nice demonstration of “mixed selectivity” in individual neurons and further evidence that the functional unit of the brain is neural ensembles, not individual neurons.

Driscoll, L. N., Pettit, N. L., Minderer, M., Chettih, S. N., & Harvey, C. D. (2017). Dynamic reorganization of neuronal activity patterns in parietal cortexCell.

For 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 »

When you search for something do you simply hold a static template of it in mind.  Apparently not.  Your search template waxes and wanes, waxing with the anticipated moment of search.  When the template is strong, your eyes move less.

Olmos-Solis, K., van Loon, A. M., Los, S. A., & Olivers, C. N. (2017). Oculomotor measures reveal the temporal dynamics of preparing for search. Progress in Brain Research.

A study using a combination of TMS and FMRI to asses functional connectivity.

Hawco, C., Armony, J. L., Daskalakis, Z. J., Berlim, M. T., Chakravarty, M. M., Pike, G. B., & Lepage, M. (2017). Differing Time of Onset of Concurrent TMS-fMRI during Associative Memory Encoding: A Measure of Dynamic ConnectivityFrontiers in Human Neuroscience11, 404.