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 »
Increased theta synchrony between the prefrontal cortex and hippocampus when subjects encoded unexpected study items. This is further evidence that theta-band (6-10 Hz) oscillations orchestrate communication between these brain areas.
Gruber, M. J., Hsieh, L. T., Staresina, B., Elger, C., Fell, J., Axmacher, N., & Ranganath, C. (2017). Theta Phase Synchronization Between The Human Hippocampus And The Prefrontal Cortex Supports Learning Of Unexpected Information. bioRxiv, 144634.
For further reading:
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
Miller Lab Alumnus, Wael Asaad, shows that neurons in the prefrontal cortex can figure out which prior events get credit for the consequences of our actions.
Asaad, W. F., Lauro, P. M., Perge, J. A., & Eskandar, E. N. (2017). Prefrontal Neurons Encode a Solution to the Credit-Assignment Problem. Journal of Neuroscience, 37(29), 6995-7007.
New result on bioRxiv:
Gamma and beta bursts during working memory read-out suggest roles in its volitional control
Mikael Lundqvist, Pawel Herman, Melissa R Warden, Scott L Brincat, Earl K Miller
doi: https://doi.org/10.1101/122598
Working memory (WM) activity is not as stationary or sustained as previously thought. There are brief bursts of gamma (55 to 120 Hz) and beta (20 to 35 Hz) oscillations, the former linked to stimulus information in spiking. We examine these dynamics in relation to read-out from WM, which is still not well understood. Monkeys held a sequence of two objects and had to decide if they matched a subsequent sequence. Changes in the balance of beta/gamma suggested their role in WM control. In anticipation of having to use an object for the match decision, there was an increase in spiking information about that object along with an increase in gamma and a decrease in beta. When an object was no longer needed, beta increased and gamma as well as spiking information about that object decreased. Deviations from these dynamics predicted behavioral errors. Thus, turning up or down beta could regulate gamma and the information in working memory.
A model of the collaboration and distribution of function between the prefrontal cortex and parietal cortex.
Working memory and decision making in a fronto-parietal circuit model
John D Murray, Jorge H Jaramillo, Xiao-Jing Wang
doi: https://doi.org/10.1101/104802
Still think that single neurons with specific functions rule the brain? Let us persuade you otherwise. We argue that cognitive control stems from dynamic, context-dependent population coding.
Stokes, M., Buschman, T.J., and Miller, E.K. (2017) Dynamic coding for flexible cognitive control. The Wiley Handbook of Cognitive Control, The Wiley Handbook of Cognitive Control, Edited by Tobias Egner, John Wiley & Sons, 2017(Chichester, West Sussex, UK). View PDF
Stanley, D.A., Roy, J.E., Aoi, M.C., Kopell, N.J., and Miller, E.K. (2016) Low-beta oscillations turn up the gain during category judgments. Cerebral Cortex. doi: 10.1093/cercor/bhw356 View PDF
Abstract:
Synchrony between local field potential (LFP) rhythms is thought to boost the signal of attended sensory inputs. Other cognitive functions could benefit from such gain control. One is categorization where decisions can be difficult if categories differ in subtle ways. Monkeys were trained to flexibly categorize smoothly varying morphed stimuli, using orthogonal boundaries to carve up the same stimulus space in 2 different ways. We found evidence for category-specific patterns of low-beta (16–20 Hz) synchrony in the lateral prefrontal cortex (PFC). This synchrony was stronger when a given category scheme was relevant. We also observed an overall increase in low-beta LFP synchrony for stimuli that were near the category boundary and thus more difficult to categorize. Beta category selectivity was evident in partial field–field coherence measurements, which measure local synchrony, but the boundary enhancement was not. Thus, it seemed that category selectivity relied on local interactions while boundary enhancement was a more global effect. The results suggest that beta synchrony helps form category ensembles and may reflect recruitment of additional cortical resources for categorizing challenging stimuli, thus serving as a form of gain control.
Cavanagh et al show that characterizing the temporal receptive field of integration of individual PFC neurons from their resting activity (via autocorrelation) helps predict their coding for value. In short, taking into account the temporal dynamics of neuron spiking yields more information about their role in representing value than spike rates alone.
Cavanagh, Sean E., et al. “Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice.” eLife 5 (2016): e18937.
Abstract:
As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with “internal” memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)—regions critical for sensory associations—of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11–27 Hz) oscillatory power and synchrony associated with “top-down” or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing.
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