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.

Miller, Earl. “Earl K. Miller.” Neuron 95 (2017): 1237.
DOI: http://dx.doi.org/10.1016/j.neuron.2017.08.035

Earl Miller studies the neural basis of high-level cognitive functions. In an interview with Neuron, he discusses the need for a holistic approach to figure out the brain, how ideas don’t happen in a vacuum, and the challenge of convincing the public that science produces facts; he also shares an open invitation to see Pavlov’s Dogz.  View PDF

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.

12 Sep 2017
September 12, 2017

Howard Eichenbaum (1947–2017)

In The News

A wonderful tribute to Howard Eichenbaum by Mike Hasselmo and Chantal Stern.  You will be missed, Howard.

http://science.sciencemag.org/content/357/6354/875

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.