Working memory in crows. Many of the same neural properties as primates.
Hartmann, K., Veit, L., & Nieder, A. (2017). Neurons in the crow nidopallium caudolaterale encode varying durations of visual working memory periods. Experimental Brain Research, 1-12.
A model in which local parallel processors assemble to produce goal-directed behavior. A performance bottleneck comes from the routing stage, which learns to map inputs onto motor representations. This is very much like mixed-selectivity models of cortex.
Zylberberg, A., Slezak, D. F., Roelfsema, P. R., Dehaene, S., & Sigman, M. (2010). The brain’s router: a cortical network model of serial processing in the primate brain. PLoS computational biology, 6(4), e1000765.
For more about mixed selectivity see:
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 »
Enel et al use reservoir computing to understand how mixed selectivity dynamic in the prefrontal cortex support complex, flexible behavior. Reservoir computing (like mixed selectivity) involves inputs fed to a dynamical system that learns only at the output stage. They argue that this approach is good framework for understanding how cortical dynamics produce higher cognitive functions.
Enel, P., Procyk, E., Quilodran, R., & Dominey, P. F. (2016). Reservoir computing properties of neural dynamics in prefrontal cortex. PLoS computational biology, 12(6), e1004967.
For more about mixed selectivity see:
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 »
There is growing evidence that bottom-up sensory inputs are associated with gamma oscillations (30-120 Hz) while top-down control depends on lower frequencies from delta through beta (1-30 Hz). This review argues that phase-phase synchrony across different frequencies integrates, coordinates, and regulates the neural assemblies in different frequency bands.
Palva, J. M., & Palva, S. (2017). Functional integration across oscillation frequencies by cross‐frequency phase synchronization. European Journal of Neuroscience.
Groovy new paper from Erin Rich and Joni Wallis. They tested the relationship between information encoding in high gamma with that in spiking activity of neurons. They encode similar information but neurons only contribute to a small increase in gamma. Plus, there are large-scale temporal dynamics that can only be seen in gamma. In other words, might as well study gamma.
Rich, E. L., & Wallis, J. D. (2017). Spatiotemporal dynamics of information encoding revealed in orbitofrontal high-gamma. Nature Communications, 8(1), 1139.
Do rodents have one? The answer is not straightforward. Marie Carlén reviews the data for us.
Carlén, M. (2017). What constitutes the prefrontal cortex?. Science, 358(6362), 478-482.
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
There were clear differences.
Viswanathan, P., & Nieder, A. (2017). Comparison of visual receptive fields in the dorsolateral prefrontal cortex (dlPFC) and ventral intraparietal area (VIP) in macaques. European Journal of Neuroscience.
Press release for our new paper in Neuron: Brain waves reflect different types of learning
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