Andre Bastos was selected at a Rising Star by the Association for Psychological Science. Indeed, he is. Congrats, Andre!
http://www.psychologicalscience.org/rising-stars/stars.cfm
Check out Andre’s latest paper:
Bastos, A.M., Loonis, R., Kornblith, S., Lundqvist, M., and Miller, E.K. (2018) Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory. Proceedings of the National Academy of Sciences. doi:10.1073/pnas.1710323115 View PDF
High frequency waves (Davis on trumpet) carry sensory inputs from the back of the brain to the front. Low frequency waves (Mingus on bass) carry executive (top-down) information from the front to the back of the brain. The low frequencies control the expression of high frequencies. That’s how you choose what sensory inputs to hold in mind (working memory). (Image: Andre Bastos)
It makes sense because we all know that the bass should guide the lead instruments. Am I right?
Bastos, A.M., Loonis, R., Kornblith, S., Lundqvist, M., and Miller, E.K. (2018) Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory. Proceedings of the National Academy of Sciences. published ahead of print January 16, 2018, doi:10.1073/pnas.1710323115
Read MIT press release here.
This is the first of three papers that all lead to the same general conclusion: Sensory (bottom-up) information is fed forward through cortex by gamma (>50 Hz) waves in superficial cortical layers. Executive (top-down) information is fed back through cortex by alpha/beta waves (4-22 Hz) in deep cortical layers. The beta waves in deep layers regulate superficial layer gamma in a push-pull fashion thereby allowing top-down information to control the flow of bottom-up sensory information. This allows volitional control over what we hold in mind. Stayed tuned for the other two papers. They will appear in the next few weeks.
Bastos, A.M., Loonis, R., Kornblith, S., Lundqvist, M., and Miller, E.K. (2018) Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory. Proceedings of the National Academy of Sciences. published ahead of print January 16, 2018, doi:10.1073/pnas.1710323115
Read MIT press release here.
Gamma and beta bursts during working memory readout suggest roles in its volitional control
Lundqvist et al Nature Communications, in press.
Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory
Bastos et al PNAS, in press
Different levels of category abstraction by different dynamics in different prefrontal areas
Wutz et al Neuron, in press
Stay tuned for what they are about and what they mean. They add up to a new model of working memory.
New paper accepted:
“Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory”
Bastos, Loonis, Kornblith, Lundqvist, and Miller. PNAS, in press
Coming soon. Stay tuned.
Intrinsic neuronal dynamics predict distinct functional roles during working memory
Dante Francisco Wasmuht, Eelke Spaak, Timothy J. Buschman, Earl K. Miller, Mark G. Stokes
doi: https://doi.org/10.1101/233171
Abstract
Working memory (WM) is characterized by the ability to maintain stable representations over time; however, neural activity associated with WM maintenance can be highly dynamic. We explore whether complex population coding dynamics during WM relate to the intrinsic temporal properties of single neurons in lateral prefrontal cortex (lPFC), the frontal eye fields (FEF) and lateral intraparietal cortex (LIP) of two monkeys (Macaca mulatta). We found that cells with short timescales carried memory information relatively early during memory encoding in lPFC; whereas long timescale cells played a greater role later during processing, dominating coding in the delay period. We also observed a link between functional connectivity at rest and intrinsic timescale in FEF and LIP. Our results indicate that individual differences in the temporal processing capacity predicts complex neuronal dynamics during WM; ranging from rapid dynamic encoding of stimuli to slower, but stable, maintenance of mnemonic information.
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 »
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
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