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

Abstract:
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:
http://discovermagazine.com/2016/oct/your-attention-please

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.
https://bbrfoundation.org/annual-prizes#Goldman

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.

MIT press release:
http://news.mit.edu/2016/earl-miller-receives-goldman-rakic-prize-in-cognitive-neuroscience-1101

BBRF press release:
https://bbrfoundation.org/news-releases/brain-behavior-research-foundation-honors-nine-scientists-for-outstanding-achievemen-0

Watch Award video:
https://www.youtube.com/watch?v=_HxD5ORVQqo&t=4s

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

Abstract:
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.

Morocos and Harvey reveal new depths in ongoing activity in parietal cortex in mice.  Information about cues, behavioral choices, etc were not represented by single neurons in a winner-take fashion (the traditional view).  Rather, different information is added to on-going patterns of activity that reflect the history of recent events. This could only be revealed via analysis of activity on single trials.

Morcos, Ari S., and Christopher D. Harvey. “History-dependent variability in population dynamics during evidence accumulation in cortex.” Nature Neuroscience (2016).

For another example of how single-trial analysis reveals much more than across-trial averaging, see:

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 »

Cavanagh et al show that characterizing the temporal receptive field of integration of individual PFC neurons from their resting activity (via autocorrelation) helps predict their coding for value.  In short, taking into account the temporal dynamics of neuron spiking yields more information about their role in representing value than spike rates alone.

Cavanagh, Sean E., et al. “Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice.” eLife 5 (2016): e18937.