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 Associations. Journal 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 Capacity. Scientific Reports, 7.
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 cortex. Cell.
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 Connectivity. Frontiers in Human Neuroscience, 11, 404.