Enhanced prefrontal-hippocampal spike-LFP coupling during learning of a spatial strategy (but not other strategies).
Negrón-Oyarzo, I., Espinosa, N., Aguilar, M., Fuenzalida, M., Aboitiz, F., & Fuentealba, P. (2018). Coordinated prefrontal–hippocampal activity and navigation strategy-related prefrontal firing during spatial memory formation. Proceedings of the National Academy of Sciences, 201720117.
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 learning. bioRxiv, 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
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 Cortex. Journal 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
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
Review: Kei Igarashi argues that learning-related changes in synchrony between oscillatory activity in the cortex and hippocampus enhances neural communication and thus supports memory storage and recall.
We (Antoulatos and Miller) show increased beta-band synchrony between (but not within) the prefrontal cortex and striatum during category learning. By the time the categories were fully learned, the beta synchrony became category-specific. That is, different patterns of prefrontal cortex-striatum recording sites showed increased beta synchrony for one category or the other. Thus, category learning may depend on formation of oscillatory synchrony-aided functional circuits between the prefrontal cortex and striatum. Further, causality analysis suggested that the striatum exerted a greater influence on the prefrontal cortex than the other way around. This supports models positing that the basal ganglia “train” the prefrontal cortex (Pasupathy and Miller, 2005; Seger and Miller, 2010).
Antzoulatos, E.G. and Miller, E.K. (2014) “Increases in functional connectivity between the prefrontal cortex and striatum during category learning.” Neuron, 83:216-225 DOI: http://dx.doi.org/10.1016/j.neuron.2014.05.005 View PDF
For further reading:
Pasupathy, A. and Miller, E.K. (2005) Different time courses for learning-related activity in the prefrontal cortex and striatum. Nature, 433:873-876. View PDF »
Antzoulatos,E.G. and Miller, E.K. (2011) Differences between neural activity in prefrontal cortex and striatum during learning of novel, abstract categories. Neuron. 71(2): 243-249. View PDF »
Seger, C.A. and Miller, E.K. (2010) Category learning in the brain. Annual Review of Neuroscience, Vol. 33: 203-219. View PDF »
IFLScience: Brain Waves Synchronize for Faster Learning
Summary:
As our thoughts dart from this to that, our brains absorb and analyze new information at a rapid pace. According to a new study, these quickly changing brain states may be encoded by the synchronization of brain waves across different brain regions. Waves originating from two areas involved in learning couple to form new communication circuits when monkeys learn to categorize different patterns of dots.
Read more here
A (very brief) mention of the new paper by Antzoulatos and Miller (2014) on National Public Radio.
The paper:
Antzoulatos, E.G. and Miller, E.K. (in press) “Increases in functional connectivity between the prefrontal cortex and striatum during category learning.” Neuron. View PDF
New Miller Lab paper in press and online at Neuron:
Antzoulatos EG and Miller EK (in press) Increases in Functional Connectivity between Prefrontal Cortex and Striatum during Category Learning. Neuron, in press.
DOI: http://dx.doi.org/10.1016/j.neuron.2014.05.005
Animals were trained to learn new category groupings by trial and error. Once they started to “get” the categories, there was an increase in beta-band synchrony between the prefrontal cortex and striatum, two brain areas critical for learning. By the time the categories were well-learned, the beta synchrony between the areas became category-specific, that is, unique sets of sites in the prefrontal cortex and striatum showed increased beta synchrony for the two different categories. This suggests that synchronization of brain rhythms can quickly establish new functional brain circuits and thus support cognitive flexibility, a hallmark of intelligence.
MIT Press release:
Synchronized brain waves enable rapid learning
MIT study finds neurons that hum together encode new information.
Mike Hasselmo and colleagues examined how the brain generalizes and infers new behaviors from previous experience. They trained different styles of neural network models to learn context-dependent behaviors (i.e., the response to four stimuli, A B C D, mapped onto two different responses X Y differently in different contexts). There were previously unseen stimuli whose response could be inferred from the other stimuli. They analyzed a Deep Belief Network, a Multi-Layer Perceptron, and the combination of a Deep Belief Network with a Linear Perceptron. The combination of the Deep Belief Network with Linear Perceptron worked best.
A Deep Belief Network has multiple layers of hidden units with connections between, but not within, the layers.
Brown University researchers show how increases in sigma and delta brain oscillations during sleep correlate with learning new visual and motor skills. The sigma oscillations in particular were traced to the occipital representation of the visual quadrant where the learning took place.
The title says it all. Theta oscillations in humans increased with prediction error and predicted the subject’s learning rates.
Koralek et al show learning-related increases in oscillatory coherence between the motor cortex and striatum during learning. The increase in coherence was seen for neurons related to behavior. This supports the notion that oscillatory coherence plays a role in forming functional networks.
Eldar et al show that neural gain influences learning style. Subjects learned associations between pictures and reward. The association could be based on different stimulus dimensions and different people had different predispositions for one dimension or the other. Eldar et al assessed neural gain by pupil dilation (which is correlated with locus coeruleus norepinephrine activity) and found that the higher the gain, the more likely subjects were to follow their predispositions. The increase in gain was thought to boost the asymmetry of strength between different functional networks which are responsible for the predisposition in learning style.
Soltani et al (2013) explored the role of D1 and D2 dopamine receptors in saccade target selection. They find evidence that D1 receptors modulate the strength of inputs to the frontal eye fields and recurrent connectivity whereas D2 may modulate the output of the FEF. This may be because D1 seems to reduce LTP and LTD, which is consistent with observations that D1 receptors contribute to associative learning (Puig and Miller, 2012). Like Puig and Miller (2012), they also found that D1 blockade increases response perseveration.
Further reading:
Puig, M.V. and Miller, E.K. (2012) The role of prefrontal dopamine D1 receptors in the neural mechanisms of associative learning. Neuron. 74: 874-886. View PDF »