The Miller Lab

  • Rule Activity Related to Spatial and Numerical Magnitudes

    Miller Lab

    Eiselt and Nieder trained monkeys to make greater/less than judgments to line lengths and dot numerosities.  They compared neural activity in the prefrontal cortex (PFC), anterior cingulate (AC), and premotor cortex (PMC).  The greatest proportion of greater/less than rule neurons were found in the PFC.  Further, only the PFC had neurons that were “generalists”; they signaled the greater/less than rules for both judgments.  Neurons in other areas were specialized for one judgment or the other.

    This is consistent with our work showing that a large proportion of PFC neurons are multifunction, mixed selectivity neurons.  They may be key in providing the computational power for complex, flexible behavior.  For further reading see:

    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

    Cromer, J.A., Roy, J.E., and Miller, E.K. (2010) Representation of multiple, independent categories in the primate prefrontal cortex. Neuron, 66: 796-807. View PDF »

  • Load Dependence of β and γ Oscillations Predicts Individual Capacity of Visual Attention

    Miller Lab

    Rouhinen et al provide evidence for the role of neural oscillations in the limitations of cognitive capacity.  Subjects tracked multiple objects.  Strength of oscillations were different preceding detected vs undetected objects.  Suppression of low-frequency oscillations (<20 Hz) and strengthening of high-frequency oscillations (>20 Hz) in the frontoparietal cortex was correlated with attentional load.   Load-dependent strengthening of 20-90 Hz oscillations was predictive of individual capacity.  This supports hypotheses that oscillations play major role in attention and are responsible for the limited bandwidth of cognition.

    Further reading on attention, capacity, and oscillations:

    • Buschman,T.J. and Miller, E.K. (2010) Shifting the Spotlight of Attention: Evidence for Discrete Computations in Cognition. Frontiers in Human Neuroscience. 4(194): 1-9. View PDF »
    • Miller, E.K. and Buschman, T.J. (2013) Cortical circuits for the control of attention.  Current Opinion in Neurobiology.  23:216–222  View PDF »
    • Buschman, T.J. and Miller, E.K. (2009) Serial, covert, shifts of attention during visual search are reflected by the frontal eye fields and correlated with population oscillations. Neuron, 63: 386-396. View PDF »
    • Buschman, T.J. and Miller, E.K. (2007) Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science. 315: 1860-1862  The Scientist’s “Hot Paper” for October 2009. View PDF »
    • Siegel, M., Warden, M.R., and Miller, E.K. (2009) Phase-dependent neuronal coding of objects in short-term memory. Proceedings of the National Academy of Sciences, 106: 21341-21346. View PDF »

  • Trisecting representational states in short-term memory

    Miller Lab

    Nee and Jonides argue that short-term memory (STM) is not monolithic, but instead involves multiple processes with different characteristics.  There are frontal selection mechanisms (normally associated with attention), medial temporal binding mechanisms (associated with long-term memory) and synaptic plasticity.  As a result, STM involves a single representation that can be focused on, a set of active representations that focused can be switched to, and passive long-term memory representations with residual traces that can be easily activated. The authors show how this model can explain many discrepancies across studies.

  • Wall Street Journal: The Brain’s Crowdsourcing Software

    Miller Lab
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    Our work with Stefano Fusi’s Lab makes  The Wall Street Journal.

  • Single-Neuron Mechanisms Underlying Cost-Benefit Analysis in Frontal Cortex

    Miller Lab
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    Miller Lab alumnus Jon Wallis and crew studied two different types of cost-benefit decisions (delay vs effort).  They found that different neurons in the dorsolateral prefrontal cortex, orbitofrontal, and anterior cingulate encoded the different types of decisions.  Thus, rather than have neurons encode decisions on an abstract level, frontal cortex neurons encode stimuli based on their exact consequences.

  • Miller and Cohen (2001) makes list of most highly cited papers in neuroscience

    Miller Lab

    Blogger John Borghi lists the most highly cited papers in neuroscience and has kind words for Miller and Cohen (2001).  Thanks, John!

    • Miller, E.K. and Cohen, J.D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24:167-202.
      Designated a Current Classic by Thomson Scientific as among the most cited papers in Neuroscience and Behavior. View PDF »

  • Disruption of Dorsolateral Prefrontal Cortex Decreases Model-Based in Favor of Model-free

    Miller Lab

    Behavior can be fast and automatic, but inflexible (model-free) vs slower, more deliberate, and flexible (model-based) Ray Dolan and crew show that disruption of the prefrontal cortex by transcranial magnetic stimulation (TMS) pushes humans towards more inflexible, model-free, behavior.

  • Broad domain generality in focal regions of frontal and parietal cortex

    Miller Lab

    More evidence for domain-general processing in higher-level cortex.  Federenko et al tested human subjects with seven tasks with different cognitive demands.  FMRI revealed overlapping activation zones in the frontal and parietal cortex.  This is consistent with neurophysiological studies showing that many neurons in these areas are multifunctional.  Rigotti et al recently demonstrated that these multifunctional “mixed selectivity” neurons provide the computational power needed for high-level cognition.

    For further reading:

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

    Miller, E.K. and Fusi, S. (2013) Limber neurons for a nimble mind. Neuron. 78:211-213. View PDF

  • Evidence for differential top-down and bottom-up suppression in posterior parietal cortex

    Miller Lab

    Mirpour and Bisley recorded neural responses and local field potentials from the lateral intraparietal cortex (LIP) during visual search.  Previously fixated non-target stimuli elicited greater lower frequency (alpha and beta) oscillations.  This suggested that reduced neural responses (and attention) to previously seen stimuli results from oscillatory-based top-down influences from the frontal cortex.

  • Dynamic Construction of a Coherent Attentional State in a Prefrontal Cell Population

    Miller Lab

    John Duncan and colleagues examined dynamic allocation of attention in the prefrontal cortex.  A behaviorally relevant target and non-target were simultaneously presented in both visual hemifields.  At first, activity in each hemifield was dominated by the stimulus in the contralateral field but then all activity became dominated by the target alone.  The speed and degree of attentional reallocation depend on relative attentional weights; more experience with a target led to faster and greater allocation to the target.  Because neurons rapidly shifted their representation from an irrelevant to relevant stimulus in the opposite hemifield, these results are consistent with adaptive coding models of neural representation.
    Kadohisa et al (2013) Dynamic Construction of a Coherent Attentional State in a Prefrontal Cell Population

    Further reading on adaptive coding:
    Miller, E.K. and Cohen, J.D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24:167-202.  View PDF »

    Duncan, J. and Miller, E.K. (2013) Adaptive neural coding in frontal and parietal cortex. In: Stuss, D.T. and Knight, R.T. (Eds). Principles of Frontal Lobe Function: Second Edition.

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