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  • 20
    Dec 2017

    Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex


    Miller Lab
    Neuroscience

    Cavanagh et al test the sustained vs dynamic activity models of working memory.  They find that sustained activity does not maintain the memory of a cue location/response past a distractor.  Instead of sustained activity, they conclude that a dynamic population-level process underlies working memory.

    Cavanagh, S. E., Towers, J. P., Wallis, J. D., Hunt, L. T., & Kennerley, S. W. (2017). Reconciling persistent and dynamic hypotheses of working memory coding in prefrontal cortex. bioRxiv, 231506.

  • 14
    Dec 2017

    New results: Intrinsic neuronal dynamics predict distinct functional roles during working memory


    Miller Lab
    Miller Laboratory, Neuroscience

    Intrinsic neuronal dynamics predict distinct functional roles during working memory
    Dante Francisco Wasmuht, Eelke Spaak, Timothy J. Buschman, Earl K. Miller, Mark G. Stokes
    doi: https://doi.org/10.1101/233171

    Abstract
    Working memory (WM) is characterized by the ability to maintain stable representations over time; however, neural activity associated with WM maintenance can be highly dynamic. We explore whether complex population coding dynamics during WM relate to the intrinsic temporal properties of single neurons in lateral prefrontal cortex (lPFC), the frontal eye fields (FEF) and lateral intraparietal cortex (LIP) of two monkeys (Macaca mulatta). We found that cells with short timescales carried memory information relatively early during memory encoding in lPFC; whereas long timescale cells played a greater role later during processing, dominating coding in the delay period. We also observed a link between functional connectivity at rest and intrinsic timescale in FEF and LIP. Our results indicate that individual differences in the temporal processing capacity predicts complex neuronal dynamics during WM; ranging from rapid dynamic encoding of stimuli to slower, but stable, maintenance of mnemonic information.

  • 5
    Dec 2017

    Functional connectivity between Anterior Cingulate cortex and Orbitofrontal cortex during value-based decision making


    Miller Lab
    Neuroscience

    Low-frequency synchrony between the anterior cingulate and orbitofrontal cortex is diminished when errors are made.

    Fatahi, Z., Haghparast, A., Khani, A., & Kermani, M. (2017). Functional connectivity between Anterior Cingulate cortex and Orbitofrontal cortex during value-based decision making. Neurobiology of Learning and Memory.

  • 5
    Dec 2017

    Contributions of primate prefrontal cortex and medial temporal lobe to temporal-order memory


    Miller Lab
    Neuroscience

    An interesting contrast between the prefrontal cortex (PFC) and medial temporal lobe (MTL) in encoding temporal order.  PFC neurons showed stronger “mixed selectivity” type encoding. They responded to a combination of an item and the order in which in appeared, only responding to specific items at specific times.  By contrast, MTL neurons were mainly item-selective.  They typically responded to an item, regardless of its order, but their firing rate was modulated by order.

    Naya, Y., Chen, H., Yang, C., & Suzuki, W. A. (2017). Contributions of primate prefrontal cortex and medial temporal lobe to temporal-order memory. Proceedings of the National Academy of Sciences, 201712711.

    Further reading on mixed selectivity:
    Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. (2013) The importance of mixed selectivity in complex cognitive tasks. Nature, 497, 585-590, doi:10.1038/nature12160. View PDF »

    Fusi, S., Miller, E.K., and Rigotti, M. (2016) Why neurons mix: High dimensionality for higher cognition.  Current Opinion in Neurobiology. 37:66-74  doi:10.1016/j.conb.2016.01.010. View PDF »

  • 4
    Dec 2017

    Thalamic functions in distributed cognitive control


    Miller Lab
    Neuroscience

    A review by Mike Halassa and Sabine Kastner about our emerging understanding of the role of the thalamus in cognitive control.

    Halassa, M. M., & Kastner, S. (2017). Thalamic functions in distributed cognitive control. Nature Neuroscience, 20(12), 1669.

  • 21
    Nov 2017

    Prefrontal Executive Functions Predict and Preadapt


    Miller Lab
    Neuroscience

    The Dean of Prefrontal Cortex, Joaquin Fuster, breaks down prefrontal function along three lines: Executive attention, working memory, and decision-making.

    Fuster, J. M. (2017). Prefrontal Executive Functions Predict and Preadapt. In Executive Functions in Health and Disease(pp. 3-19).

  • 15
    Nov 2017

    When brain rhythms aren’t ‘rhythmic’: implication for their mechanisms and meaning


    Miller Lab
    Neuroscience

    A thoughtful review and discussion of the issues involved in analyzing brain rhythms.

    Jones, S. R. (2016). When brain rhythms aren’t ‘rhythmic’: implication for their mechanisms and meaning. Current opinion in neurobiology, 40, 72-80.

  • 15
    Nov 2017

    Neurons in the crow nidopallium caudolaterale encode varying durations of visual working memory periods


    Miller Lab
    Neuroscience

    Working memory in crows.  Many of the same neural properties as primates.

    Hartmann, K., Veit, L., & Nieder, A. (2017). Neurons in the crow nidopallium caudolaterale encode varying durations of visual working memory periods. Experimental Brain Research, 1-12.

     

  • 8
    Nov 2017

    The Brain’s Router: A Cortical Network Model of Serial Processing in the Primate Brain


    Miller Lab
    Neuroscience

    A model in which local parallel processors assemble to produce goal-directed behavior.   A performance bottleneck comes from the routing stage, which learns to map inputs onto motor representations.  This is very much like mixed-selectivity models of cortex.

    Zylberberg, A., Slezak, D. F., Roelfsema, P. R., Dehaene, S., & Sigman, M. (2010). The brain’s router: a cortical network model of serial processing in the primate brain. PLoS computational biology, 6(4), e1000765.

    For more about mixed selectivity see:
    Fusi, S., Miller, E.K., and Rigotti, M. (2016) Why neurons mix: High dimensionality for higher cognition.  Current Opinion in Neurobiology. 37:66-74  doi:10.1016/j.conb.2016.01.010. View PDF »

    Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. (2013) The importance of mixed selectivity in complex cognitive tasks. Nature, 497, 585-590, doi:10.1038/nature12160. View PDF »

  • 8
    Nov 2017

    Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex


    Miller Lab
    Miller Laboratory, Neuroscience

    Enel et al use reservoir computing to understand how mixed selectivity dynamic in the prefrontal cortex support complex, flexible behavior.  Reservoir computing (like mixed selectivity) involves inputs fed to a dynamical system that learns only at the output stage.  They argue that this approach is good framework for understanding how cortical dynamics produce higher cognitive functions.

    Enel, P., Procyk, E., Quilodran, R., & Dominey, P. F. (2016). Reservoir computing properties of neural dynamics in prefrontal cortex. PLoS computational biology, 12(6), e1004967.

    For more about mixed selectivity see:
    Fusi, S., Miller, E.K., and Rigotti, M. (2016) Why neurons mix: High dimensionality for higher cognition.  Current Opinion in Neurobiology. 37:66-74  doi:10.1016/j.conb.2016.01.010. View PDF »

    Rigotti, M., Barak, O., Warden, M.R., Wang, X., Daw, N.D., Miller, E.K., & Fusi, S. (2013) The importance of mixed selectivity in complex cognitive tasks. Nature, 497, 585-590, doi:10.1038/nature12160. View PDF »

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