Rodu, J., Klein, N., Brincat, S. L., Miller, E. K., & Kass, R. E. (2018). Detecting Multivariate Cross-Correlation Between Brain RegionsJournal of neurophysiology.

Abstract

The problem of identifying functional connectivity from multiple time series data recorded in each of two or more brain areas arises in many neuroscientific investigations. For a single stationary time series in each of two brain areas statistical tools such as cross-correlation and Granger causality may be applied. On the other hand, to examine multivariate interactions at a single time point, canonical correlation, which finds the linear combinations of signals that maximize the correlation, may be used. We report here a new method that produces interpretations much like these standard techniques and, in addition, 1) extends the idea of canonical correlation to 3-way arrays (with dimensionality number of signals by number of time points by number of trials), 2) allows for nonstationarity, 3) also allows for nonlinearity, 4) scales well as the number of signals increases, and 5) captures predictive relationships, as is done with Granger causality. We demonstrate the effectiveness of the method through simulation studies and illustrate by analyzing local field potentials recorded from a behaving primate.

When MIT neuroscientist Earl Miller was in graduate school at Princeton, he was inspired by the lectures of George A. Miller, an influential psychologist who helped to spark the young student’s interest in working memory. Now, as the newly named 2019 recipient of the George A. Miller Prize in Cognitive Neuroscience, Earl Miller is set to deliver a lecture honoring his teacher at the annual meeting of the Cognitive Neuroscience Society in San Francisco in March.

Read more here.

Nice review from Miler Lab alumnus Joni Wallis arguing for the importance of single-trial analyses.  Variability across trials may not be noise, it may be cognition.   Joni argues that ensembles, not single neurons, are the fundamental unit in the brain.  One needs to record from many neurons simultaneously to understand cognitive processes.

Wallis, J. D. (2018). Decoding Cognitive Processes from Neural EnsemblesTrends in Cognitive Sciences.

 

On the role of cortex-basal ganglia interactions for category learning: A neuro-computational approach
Francesc Villagrasa, Javier Baladron, Julien Vitay, Henning Schroll, Evan G. Antzoulatos, Earl K. Miller and Fred H. Hamker
Journal of Neuroscience 18 September 2018, 0874-18; DOI: https://doi.org/10.1523/JNEUROSCI.0874-18.2018

Abstract
In addition to the prefrontal cortex (PFC), the basal ganglia (BG) have been increasingly often reported to play a fundamental role in category learning, but the systems-level circuits of how both interact remain to be explored. We developed a novel neuro-computational model of category learning that particularly addresses the BG-PFC interplay. We propose that the BG bias PFC activity by removing the inhibition of cortico-thalamo-cortical loop and thereby provide a teaching signal to guide the acquisition of category representations in the cortico-cortical associations to the PFC. Our model replicates key behavioral and physiological data of macaque monkey learning a prototype distortion task from Antzoulatos and Miller (2011). Our simulations allowed us to gain a deeper insight into the observed drop of category selectivity in striatal neurons seen in the experimental data and in the model. The simulation results and a new analysis of the experimental data, based on the model’s predictions, show that the drop in category selectivity of the striatum emerges as the variability of responses in the striatum rises when confronting the BG with an increasingly larger number of stimuli to be classified. The neuro-computational model therefore provides new testable insights of systems-level brain circuits involved in category learning which may also be generalized to better understand other cortico-basal ganglia-cortical loops

Bouchacourt and Buschman describe a two-layer model of working memory. A sensory layer feeds into an unstructured layer of neurons with random connections (i.e., “mixed-selectivity” type neurons).  It is flexible but interference between representations results in a capacity limit.  Sounds like working memory to me.

Bouchacourt, F., & Buschman, T. J. (2018). A Flexible Model of Working Memory. bioRxiv, 407700.

More 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 »

Congrats to Miller Lab postdoc Andre Bastos for being awarded a prestigious K99 Award from the National Institutes of Health.

 

Wasmuht, D. F., Spaak, E., Buschman, T. J., Miller, E. K., & Stokes, M. G. (2018). Intrinsic neuronal dynamics predict distinct functional roles during working memory. Nature Communications.

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 find that cells with short timescales carry memory information relatively early during memory encoding in lPFC; whereas long-timescale cells play a greater role later during processing, dominating coding in the delay period. We also observe a link between functional connectivity at rest and the intrinsic timescale in FEF and LIP. Our results indicate that individual differences in the temporal processing capacity predict complex neuronal dynamics during WM, ranging from rapid dynamic encoding of stimuli to slower, but stable, maintenance of mnemonic information.

Our dual Perspectives “debate” paper re: Does persistent spiking hold memories “in mind” (i.e., working memory):
Paper and link to opposing paper: Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not

Press release: To understand working memory, scientists must resolve this debate

Our two cents:
Surprised this became a debate.  All we are saying is that if you look at delay activity more closely (on single trials) it’s bursty. Something else (synaptic weight changes) could be helping. Adding synaptic mechanisms saves energy and confers functional advantages.
Lundqvist, M., Rose, J., Herman, P., Brincat, S. L., Buschman, T. J., & Miller, E. K. (2016). Gamma and beta bursts underlie working memory. Neuron, 90(1), 152-164.

And it leaves room for network rhythms that may underlie executive control of working memory.
Bastos, A. M., Loonis, R., Kornblith, S., Lundqvist, M., & 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, 201710323.

Lundqvist, M., Herman, P., Warden, M. R., Brincat, S. L., & Miller, E. K. (2018). Gamma and beta bursts during working memory readout suggest roles in its volitional control. Nature Communications, 9(1), 394.

Yuri Buzsaki pointed out that his lab reported that in PFC working memories are maintained by internally generated cell assembly sequences. The few persistently firing neurons were interneurons.
Fujisawa S, Amarasingham A, Harrison MT, Buzsáki G.
Nat Neurosci. 2008.

Also, the idea that synaptic weight changes help maintain working memories is not altogether new.  Goldman-Rakic suggested such a mechanism.  Her lab found that sparse firing in the PFC produces temporary changes in synaptic weights.  Importantly, if neurons firing too fast, inhibitory mechanisms kick in and you don’t get the weight changes. See:

Wang, Y., Markram, H., Goodman, P.H., Berger, T.K., Ma, J., and Goldman-Rakic, P.S. (2006). Heterogeneity in the pyramidal network of the medial prefrontal cortex. Nat. Neurosci. 9, 534–542.

But, hey, don’t take our word for it:  Look at memory delay activity on single trials and tell us what *you* see.

The Department of Brain and Cognitive Sciences at MIT award Earl Miller the 2017 Award for Excellence in Graduate Teaching.

 

Earl Miller offers advice on how to avoid multitasking in the May 2018 issue of Redbook.

Life and Family – Redbook May 2018

Press release for our new paper:
A heavy working memory load may sink brainwave ‘synch’

The paper:
Pinotsis, D.A., Buschman, T.J. and Miller, E.K. (2018) Working Memory Load Modulates Neuronal Coupling. Cerebral Cortex.  https://doi.org/10.1093/cercor/bhy065  View PDF

Pinotsis, D.A., Buschman, T.J. and Miller, E.K. (2018) Working Memory Load Modulates Neuronal Coupling. Cerebral Cortex, 2018 https://doi.org/10.1093/cercor/bhy065

Abstract: There is a severe limitation in the number of items that can be held in working memory. However, the neurophysiological limits remain unknown. We asked whether the capacity limit might be explained by differences in neuronal coupling. We developed a theoretical model based on Predictive Coding and used it to analyze Cross Spectral Density data from the prefrontal cortex (PFC), frontal eye fields (FEF), and lateral intraparietal area (LIP). Monkeys performed a change detection task. The number of objects that had to be remembered (memory load) was varied (1–3 objects in the same visual hemifield). Changes in memory load changed the connectivity in the PFC–FEF–LIP network. Feedback (top-down) coupling broke down when the number of objects exceeded cognitive capacity. Thus, impaired behavioral performance coincided with a break-down of Prediction signals. This provides new insights into the neuronal underpinnings of cognitive capacity and how coupling in a distributed working memory network is affected by memory load.

An article in Science News about new ideas on the role of brain waves.  It also discuss three new papers from the Miller Lab.

Brain waves may focus attention and keep information flowing  Science New March 13, 2018

Here are the papers that are discussed:

Lundqvist, M., Herman, P. Warden, M.R., Brincat, S.L., and Miller, E.K. (2018) Gamma and beta bursts during working memory read-out suggest roles in its volitional control. Nature Communications. 9, 394   View PDF

Wutz, A., Loonis, R., Roy, J.E., Donoghue, J.A., and Miller, E.K. (2018)  Different levels of category abstraction by different dynamics in different prefrontal areas. Neuron 97: 1-11.  View PDF

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.  View PDF

From Cortech Solutions.

Personal Highlights of the Plenary Sessions
Earl Miller: Rule+Rhythms=Cognition

 

Lundqvist, M., Herman, P. Warden, M.R., Brincat, S.L., and Miller, E.K. (2018) Gamma and beta bursts during working memory read-out suggest roles in its volitional control. Nature Communications. 9, Article number: 394 doi:10.1038/s41467-017-02791-8

Abstract:
Working memory (WM) activity is not as stationary or sustained as previously thought. There are brief bursts of gamma (~50–120 Hz) and beta (~20–35 Hz) oscillations, the former linked to stimulus information in spiking. We examined these dynamics in relation to readout and control mechanisms of WM. Monkeys held sequences of two objects in WM to match to subsequent sequences. Changes in beta and gamma bursting suggested their distinct roles. In anticipation of having to use an object for the match decision, there was an increase in gamma and spiking information about that object and reduced beta bursting. This readout signal was only seen before relevant test objects, and was related to premotor activity. When the objects were no longer needed, beta increased and gamma decreased together with object spiking information. Deviations from these dynamics predicted behavioral errors. Thus, beta could regulate gamma and the information in WM.

Wutz, A., Loonis, R., Roy, J.E., Donoghue, J.A., and Miller, E.K. (2018) Different levels of category abstraction by different dynamics in different prefrontal areas. Neuron  published online Jan 25 2018.

SUMMARY

Categories can be grouped by shared sensory attributes (i.e. cats) or by a more abstract rule (i.e. animals). We explored the neural basis of abstraction by recording from multi-electrode arrays in prefrontal cortex (PFC) while monkeys performed a dot-pattern categorization task. Category abstraction was varied by the degree of exemplar distortion from the prototype pattern. Different dynamics in different PFC regions processed different levels of category abstraction. Bottom-up dynamics (stimulus-locked gamma power and spiking) in ventral PFC processed more low-level abstractions whereas top-down dynamics (beta power and beta spike-LFP coherence) in dorsal PFC processed more high-level abstractions. Our results suggest a two-stage, rhythm-based model for abstracting categories.

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 Sciencesdoi: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 cortexPLoS computational biology12(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 CortexJournal of Neuroscience.  6 October 2017, 1222-17; DOI: https://doi.org/10.1523/JNEUROSCI.1222-17.2017   View PDF