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  • 30
    Jun 2015

    Navigating the circuitry of the brain’s GPS system: Future challenges for neurophysiologists


    Miller Lab
    Neuroscience

    Craig and McBain review the role of oscillations in understanding the functional circuitry of the hippocampus with an eye toward bridging in vitro and in vivo studies.

    Craig, Michael T., and Chris J. McBain. “Navigating the circuitry of the brain’s GPS system: Future challenges for neurophysiologists.” Hippocampus (2015).

  • 30
    Jun 2015

    Inferior-frontal cortex phase synchronizes with the temporal–parietal junction prior to successful change detection


    Miller Lab
    Miller Laboratory

    Micheli et al find that during sustained attention, successful near-threshold visual detection is predicted by increased phase synchrony between the frontal and temporal/parietal cortex.  They suggest that beta coherent states in the prefrontal cortex regulate top-down expectancy and coupling with posterior cortex facilitates the gating of that information.

    Evidence for the role of beta in top-down selection continues to mount.

    Micheli, Cristiano, et al. “Inferior-frontal cortex phase synchronizes with the temporal-parietal junction prior to successful change detection.” NeuroImage (2015).

  • 29
    Jun 2015

    Gamma Activity Coupled to Alpha Phase as a Mechanism for Top-Down Controlled Gating


    Miller Lab
    Neuroscience

    Bonnefond and Jenson used MEG in humans to find coupling between alpha and gamma rhythms during an attention-demanding task.  High alpha power was associated with weak gamma power at the trough of the alpha cycle.  This may provide a mechanism for top-down control of attention.

    Bonnefond, Mathilde, and Ole Jensen. “Gamma Activity Coupled to Alpha Phase as a Mechanism for Top-Down Controlled Gating.” PloS one 10.6 (2015): e0128667.

  • 24
    Jun 2015

    Single-Trial Decoding of Visual Attention from Local Field Potentials in the Primate Lateral Prefrontal Cortex Is Frequency-Dependent


    Miller Lab
    Neuroscience

    Tremblay et al decode the allocation of attention, stimulus location, and saccade from local field potentials in a frequency-dependent matter.  Decoding from LFPs was more stable across time than decoding from spikes.

  • 11
    Jun 2015

    ‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework


    Miller Lab
    Neuroscience

    Working memory has long been thought to depend on sustained firing of cortical neurons.  However, single neurons showing unbroken sustained activity is rare and average population activity is often only strong near the end of a memory delay.  Mark Stokes presents the intriguing hypothesis for activity-silent working memory.  He suggests that working memory depends on patterns of functional connectivity between neurons, not sustained activity.

    ‘Activity-silent’working memory in prefrontal cortex: a dynamic coding framework

    MG Stokes – Trends in Cognitive Sciences, 2015

  • 2
    Jun 2015

    Top-Down Regulation of Laminar Circuit via Inter-Area Signal for Successful Object Memory Recall in Monkey Temporal Cortex


    Miller Lab
    Neuroscience

    Takeda et al show that layer-specific oscillatory synchrony during successful recall of memories.  Specifically, there was laminar specific feedback from area 36 to area TE that supported the recall of a paired associate object.

  • 20
    May 2015

    Context-specific differences in fronto-parieto-occipital effective connectivity during short-term memory maintenance


    Miller Lab
    Neuroscience

    Kundu et al recorded EEG from humans during a short-term memory task.  They found fronto-parietal coherence in different frequencies were associated with different memory functions.  Alpha coherence was associated with maintenance of the information in memory.  By contrast, the top-down filtering of distractions was associated with beta coherence.  This adds to mounting evidence that specific frequency bands are associated with specific types of cortical processing like, for example, beta and top-down control.

  • 3
    Mar 2015

    Mapping of Functionally Characterized Cell Classes onto Canonical Circuit Operations in Primate Prefrontal Cortex


    Miller Lab
    Neuroscience

    Ardid et al use spike shape and firing variability to identify different classes in the primate prefrontal cortex.  They ID four classes of broad spiking neurons and three classes of narrow spiking (inhibitory) neurons.  These cell classes show different strength of synchrony to local field potential oscillations at specific frequencies.  The authors suggest this reflects canonical cortical circuits with different functions.

  • 3
    Mar 2015

    Oscillatory synchrony as a mechanism of attentional processing


    Miller Lab
    Neuroscience

    Georgia Gregoriou and colleagues review the role of oscillations in the focusing of attention. They suggest that different frequencies reflect the biophysical properties of different cell types and that synchrony allows selective routing of information through these cell populations.

  • 23
    Feb 2015

    New paper: Frequency-specific hippocampal-prefrontal interactions during associative learning


    Miller Lab
    In The News, Miller Laboratory, Neuroscience

    Frequency-specific hippocampal-prefrontal interactions during associative learning
    Brincat, S.L. and Miller, E.K. (2015) Nature Neuroscience, advanced online publication

    Abstract:
    Much of our knowledge of the world depends on learning associations (for example, face-name), for which the hippocampus (HPC) and prefrontal cortex (PFC) are critical. HPC-PFC interactions have rarely been studied in monkeys, whose cognitive and mnemonic abilities are akin to those of humans. We found functional differences and frequency-specific interactions between HPC and PFC of monkeys learning object pair associations, an animal model of human explicit memory. PFC spiking activity reflected learning in parallel with behavioral performance, whereas HPC neurons reflected feedback about whether trial-and-error guesses were correct or incorrect. Theta-band HPC-PFC synchrony was stronger after errors, was driven primarily by PFC to HPC directional influences and decreased with learning. In contrast, alpha/beta-band synchrony was stronger after correct trials, was driven more by HPC and increased with learning. Rapid object associative learning may occur in PFC, whereas HPC may guide neocortical plasticity by signaling success or failure via oscillatory synchrony in different frequency bands.

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