A recurrent network model captures the dynamics of frontal and parietal cortex activity during a categorization task. It reveals cortical motifs that underlie computations for categorical decision-making.
Chaisangmongkon, W., Swaminathan, S. K., Freedman, D. J., & Wang, X. J. (2017). Computing by Robust Transience: How the Fronto-Parietal Network Performs Sequential, Category-Based Decisions. Neuron, 93(6), 1504-1517.
Neurons in LIP contribute to two distinct stages of processing during a search task. The working memory for the sought-after feature and then the focusing of attention on target location.
Levichkina, E., Saalmann, Y. B., & Vidyasagar, T. R. (2017). Coding of spatial attention priorities and object features in the macaque lateral intraparietal cortex. Physiological Reports, 5(5), e13136.
A review on issues to consider when studying the brain by perturbing (and measuring) it. Very thoughtful.
Navigating the Neural Space in Search of the Neural Code
Mehrdad Jazayer and Arash Afraz
An excellent and comprehensive review of the brain basis of visual attention. Worthy of yours.
Moore, T., & Zirnsak, M. (2017). Neural Mechanisms of Selective Visual Attention. Annual Review of Psychology, 68, 47-72.
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
The anterior cingulate and FEF coordinate through theta and beta phase synchronization between spikes in one and local field potential in the other.
Babapoor-Farrokhran, S., Vinck, M., Womelsdorf, T., & Everling, S. (2017). Theta and beta synchrony coordinate frontal eye fields and anterior cingulate cortex during sensorimotor mapping. Nature Communications, 8, 13967.
The brain monitors simultaneous sensory input by “time division multiplexing”, a rhythmic juggling of the two streams of information.
Evidence for time division multiplexing of multiple simultaneous items in a sensory coding bottleneck
Valeria C Caruso, Jeffrey T Mohl, Chris Glynn, JungAh Lee, Shawn M Willett, Azeem Zaman, Rolando Estrada, Surya Tokdar, Jennifer M Groh
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
New Miller Lab paper:
Jia, N., Brincat, S.L., Salazar-Gomez, A., Panko, M., Guenther, F. and Miller, E.K. (2017) Decoding of intended saccade direction in an oculomotor brain-computer interface. Journal of Neural Engineering, 2017. https://doi.org/10.1088/1741-2552/aa5a3e
Objective. To date, invasive brain-computer interface (BCI) research has largely focused on replacing lost limb functions using signals from of hand/arm areas of motor cortex. However, the oculomotor system may be better suited to BCI applications involving rapid serial selection from spatial targets, such as choosing from a set of possible words displayed on a computer screen in an augmentative and alternative communication (AAC) application. Here we aimed to demonstrate the feasibility of a BCI utilizing the oculomotor system. Approach. We developed a chronic intracortical BCI in monkeys to decode intended saccadic eye movement direction using activity from multiple frontal cortical areas. Main results. Intended saccade direction could be decoded in real time with high accuracy, particularly at contralateral locations. Accurate decoding was evident even at the beginning of the BCI session; no extensive BCI experience was necessary. High-frequency (80-500 Hz) local field potential magnitude provided the best performance, even over spiking activity, thus simplifying future BCI applications. Most of the information came from the frontal and supplementary eye fields, with relatively little contribution from dorsolateral prefrontal cortex. Significance. Our results support the feasibility of high-accuracy intracortical oculomotor BCIs that require little or no practice to operate and may be ideally suited for point and click computer operation as used in most current AAC systems.
The title says it all. A comprehensive review on how salience is determined and used by the brain.
Veale, R., Hafed, Z. M., & Yoshida, M. (2017). How is visual salience computed in the brain? Insights from behaviour, neurobiology and modelling. Phil. Trans. R. Soc. B, 372(1714), 20160113.
This review of the neural basis of working memory argues that working memory is a property of many brain areas working in concert. Prefrontal vs sensory cortical areas differ in their degrees of abstraction and how they are tied to action. They argue that the persistent activity that seems to underlie working memory is a general product of cortical networks.
Christophel, T. B., Klink, P. C., Spitzer, B., Roelfsema, P. R., & Haynes, J. D. (2017). The Distributed Nature of Working Memory. Trends in Cognitive Sciences.
I would add that persistent activity may not be so persistent:
Lunqvist, M., Rose, J., Herman, P, Brincat, S.L, Buschman, T.J., and Miller, E.K. (2016) Gamma and beta bursts underlie working memory. Neuron, published online March 17, 2016. View PDF »
Stokes, M., & Spaak, E. (2016). The Importance of Single-Trial Analyses in Cognitive Neuroscience. Trends in cognitive sciences.
Stokes, M. G. (2015). ‘Activity-silent’working memory in prefrontal cortex: a dynamic coding framework. Trends in cognitive sciences, 19(7), 394-405.
Stokes, M., Buschman, T.J., and Miller, E.K. (in press) Dynamic coding for flexible cognitive control. Wiley Handbook of Cognitive Control.
Randoph Helfrich and Robert Knight review evidence that the infrastructure of cognitive control is rhythmic. The general idea is that the prefrontal cortex controls large-scale oscillatory dynamics in the cortex and subcortex. But there is much more. Do yourself a favor: Read it.
Helfrich, R. F., & Knight, R. T. (2016). Oscillatory Dynamics of Prefrontal Cognitive Control. Trends in Cognitive Sciences.
Alavash et al show how changes in network dynamics in the beta (16-28 Hz) band. Faster perceptual decisions occurred when beta-coupling became more local than global. The also found different network states in different cortical areas were associated with faster decisions. This paper lends support for recent suggestions that cortical communication is regulated via beta synchrony.
Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision making
Mohsen Alavash, Christoph Daube, Malte Woestmann, Alex Brandmeyer, Jonas Obleser
Buschman, T.J., Denovellis, E.L., Diogo, C., Bullock, D. and Miller, E.K. (2012) Synchronous oscillatory neural ensembles for rules in the prefrontal cortex. Neuron. 76: 838-846. View PDF »
Buschman T.J., Miller E.K. (2014) Goal-direction and top-down control. Philos Trans R Soc Lond B Biol Sci. 2014 Nov 5;369(1655). View PDF »
Antzoulatos, E. G., & Miller, E. K. (2016). Synchronous beta rhythms of frontoparietal networks support only behaviorally relevant representations. eLife, 5, e17822.
Categorization has been associated with distributed networks of the primate brain, including the prefrontal cortex (PFC) and posterior parietal cortex (PPC). Although category-selective spiking in PFC and PPC has been established, the frequency-dependent dynamic interactions of frontoparietal networks are largely unexplored. We trained monkeys to perform a delayed-match-to-spatial-category task while recording spikes and local field potentials from the PFC and PPC with multiple electrodes. We found category-selective beta- and delta-band synchrony between and within the areas. However, in addition to the categories, delta synchrony and spiking activity also reflected irrelevant stimulus dimensions. By contrast, beta synchrony only conveyed information about the task-relevant categories. Further, category-selective PFC neurons were synchronized with PPC beta oscillations, while neurons that carried irrelevant information were not. These results suggest that long-range beta-band synchrony could act as a filter that only supports neural representations of the variables relevant to the task at hand.
Why multitasking is BAD for your brain: Neuroscientist warns it wrecks productivity and causes mistakes
- Earl Miller has advised that people should avoid multitasking altogether
- Switching between tasks take more mental energy to get back on track
- They advise removing distractions to overcome the brain’s thirst for new information and to block out time to focus on individual tasks
Here’s Why You Shouldn’t Multitask, According to a MIT Neuroscientist – Fortune, December 7, 2016
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
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.
Now out from behind the paywall:
Miller Lab alum Andreas Nieder and crew show how dopamine receptors in the prefrontal cortex regulate access to working memory and its protection from interference.
Jacob, Simon N., Maximilian Stalter, and Andreas Nieder. “Cell-type-specific modulation of targets and distractors by dopamine D1 receptors in primate prefrontal cortex.” Nature Communications (2016): 13218.
Earl Miller wins 2016 Goldman-Rakic Prize for Outstanding Achievement in Cognitive Neuroscience.
Watch a video here:
The Goldman-Rakic Prize for Outstanding Achievement in Cognitive Neuroscience
The Goldman-Rakic Prize was created by Constance and Stephen Lieber in memory of Dr. Patricia Goldman-Rakic, a neuroscientist renowned for discoveries about the brain’s frontal lobe, who died in an automobile accident in 2003.
Earl K. Miller, Ph.D., Picower Professor of Neuroscience, Massachusetts Institute of Technology
Building on Pat Goldman-Rakic’s groundbreaking studies, Dr. Miller’s work in primates has broken new ground in the understanding of cognition. Using innovative experimental and theoretical approaches to study the neural basis of high-level cognitive functions, his laboratory has provided insights into how categories, concepts, and rules are learned, how attention is focused, and how the brain coordinates thought and action. The laboratory has innovated techniques for studying the activity of many neurons in multiple brain areas simultaneously, providing insight into how different brain structures interact and collaborate. This work has established a foundation upon which to construct more detailed, mechanistic accounts of how executive control is implemented in the brain and its dysfunction in diseases such as autism, schizophrenia and attention deficit disorder, and has led to new approaches relevant to severe mental illnesses in children and adults.
Watch Award video:
Castejon and Nunez propose a theoretical framework in which cortical oscillations produce computation by quantizing information into “discrete results”. Interesting stuff.
Castejon, Carlos, and Angel Nuñez. “Cortical Neural Computation by Discrete Results Hypothesis.”
Earl K. Miller’s Commencement Address at Kent State 5-14-16
Kent State Professional Achievement Award:
Digital Lives – The Science Behind Multitasking:
Pinotsis, D.A., Loonis, R., Bastos, A. Miller, E.K, and Friston, K.J. “Bayesian Modelling of Induced Responses and Neuronal Rhythms” Brain Topogr (2016). doi:10.1007/s10548-016-0526-y
Neural rhythms or oscillations are ubiquitous in neuroimaging data. These spectral responses have been linked to several cognitive processes; including working memory, attention, perceptual binding and neuronal coordination. In this paper, we show how Bayesian methods can be used to finesse the ill-posed problem of reconstructing—and explaining—oscillatory responses. We offer an overview of recent developments in this field, focusing on (i) the use of MEG data and Empirical Bayes to build hierarchical models for group analyses—and the identification of important sources of inter-subject variability and (ii) the construction of novel dynamic causal models of intralaminar recordings to explain layer-specific activity. We hope to show that electrophysiological measurements contain much more spatial information than is often thought: on the one hand, the dynamic causal modelling of non-invasive (low spatial resolution) electrophysiology can afford sub-millimetre (hyper-acute) resolution that is limited only by the (spatial) complexity of the underlying (dynamic causal) forward model. On the other hand, invasive microelectrode recordings (that penetrate different cortical layers) can reveal laminar-specific responses and elucidate hierarchical message passing and information processing within and between cortical regions at a macroscopic scale. In short, the careful and biophysically grounded modelling of sparse data enables one to characterise the neuronal architectures generating oscillations in a remarkable detail.