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  • 27
    Oct 2016

    Cortical Neural Computation by Discrete Results Hypothesis


    Miller Lab
    Neuroscience

    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.”

  • 13
    Oct 2016

    New paper! Bayesian Modelling of Induced Responses and Neuronal Rhythms


    Miller Lab
    Neuroscience

    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.

  • 11
    Oct 2016

    Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice


    Miller Lab
    Neuroscience

    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.

  • 2
    Aug 2016

    Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice


    Miller Lab
    Neuroscience

    Nice review and test of a hypothesis about the role of transient beta oscillations in cortical processing.

    Sherman et al (2016) Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice

  • 1
    Jun 2016

    Frontal preparatory neural oscillations associated with cognitive control


    Miller Lab
    Neuroscience

    This study shows the role of alpha and beta oscillations in the prefrontal cortex and frontal eye fields in a classic test of cognitive control: anti-saccades.  It also shows how these oscillatory patterns develop with adulthood.

    Hwang, Kai, et al. “Frontal preparatory neural oscillations associated with cognitive control: A developmental study comparing young adults and adolescents.” NeuroImage (2016).

  • 30
    May 2016

    The Importance of Single-Trial Analyses in Cognitive Neuroscience


    Miller Lab
    Miller Laboratory, Neuroscience

    Stokes and Spaak review our recent work on single-trial analysis of working memory “delay” activity.   This showed that the classic profile of sustained activity as the memory substrate is an artifact of averaging across trials.  The assumption is that averaging cancels out noise.  Instead, it may be covering up important details of the dynamics of neural activity.

    Read more here:
    The Importance of Single-Trial Analyses in Cognitive Neuroscience
    Mark Stokes and Eelke Spaak
    Trends in Cognitive Sciences
    DOI: http://dx.doi.org/10.1016/j.tics.2016.05.008

    The original paper:
    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 »

  • 27
    May 2016

    Transitions between Multiband Oscillatory Patterns Characterize Memory-Guided Perceptual Decisions in Prefrontal Circuits


    Miller Lab
    Neuroscience

    Nice paper showing that different task demands in different task stages engage different oscillatory bands in the prefrontal cortex.

    Wimmer, Klaus, et al. “Transitions between Multiband Oscillatory Patterns Characterize Memory-Guided Perceptual Decisions in Prefrontal Circuits.”The Journal of Neuroscience 36.2 (2016): 489-505.

  • 17
    Mar 2016

    New paper: Gamma and Beta Bursts Underlie Working Memory


    Miller Lab
    Miller Laboratory, Neuroscience

    Sustained activity has long been thought to be the neural substrate of working memory.  But the evidence is based on averaging neural activity across trials.  A closer examination reveals that something more complex is happening and supports a very different model of working memory.

    Gamma and Beta Bursts Underlie Working Memory
    Mikael Lundqvist, Jonas Rose, Pawel Herman, Scott L. Brincat, Timothy J. Buschman, Earl K. Miller
    Neuron, published online March 17, 2016

    DOI: http://dx.doi.org/10.1016/j.neuron.2016.02.028
    Summary
    Working memory is thought to result from sustained neuron spiking. However, computational models suggest complex dynamics with discrete oscillatory bursts. We analyzed local field potential (LFP) and spiking from the prefrontal cortex (PFC) of monkeys performing a working memory task. There were brief bursts of narrow-band gamma oscillations (45–100 Hz), varied in time and frequency, accompanying encoding and re-activation of sensory information. They appeared at a minority of recording sites associated with spiking reflecting the to-be-remembered items. Beta oscillations (20–35 Hz) also occurred in brief, variable bursts but reflected a default state interrupted by encoding and decoding. Only activity of neurons reflecting encoding/decoding correlated with changes in gamma burst rate. Thus, gamma bursts could gate access to, and prevent sensory interference with, working memory. This supports the hypothesis that working memory is manifested by discrete oscillatory dynamics and spiking, not sustained activity.

  • 15
    Feb 2016

    The Primary Visual Cortex Is Differentially Modulated by Stimulus-Driven and Top-Down Attention


    Miller Lab
    Neuroscience

    Increases in beta power associated with top-down attention.  Beta seemed unite visual cortex.  There was a more homogeneous pattern of beta correlation across the cortex during top-down vs bottom-up attention.

    Bekisz, M., Bogdan, W., Ghazaryan, A., Waleszczyk, W. J., Kublik, E., & Wróbel, A. (2016). The Primary Visual Cortex Is Differentially Modulated by Stimulus-Driven and Top-Down Attention. PloS one, 11(1), e0145379.

  • 10
    Feb 2016

    Mikael and Earl celebrate acceptance of a new paper


    Miller Lab
    Miller Laboratory, Neuroscience

    Lunqvist, M., Rose, J., Herman, P, Brincat, S.L, Buschman, T.J., and Miller, E.K. (in press) Gamma and beta bursts underlie memory.  Neuron

    We know how to party!

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