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.
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.
As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with “internal” memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)—regions critical for sensory associations—of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (∼11–27 Hz) oscillatory power and synchrony associated with “top-down” or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing.
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