parietal cortex – The Miller Lab

14

Aug 2018

Structuring of Abstract Working Memory Content by Fronto-parietal Synchrony in Primate Cortex

Miller Lab

Neuroscience

Super-cool paper by Andreas Nieder and crew. Frontal-parietal beta synchrony encodes the most recent numerical input. Theta synchrony distinguishes between different numerosities held in working memory. The spiking of mixed-selectivity neurons multiplexed both task-relevant and irrelevant stimuli but they were separated in different phases of theta oscillations. Powerful support that neural oscillations functionally organize spiking activty.

Jacob, S. N., Hähnke, D., & Nieder, A. (2018). Structuring of Abstract Working Memory Content by Fronto-parietal Synchrony in Primate Cortex. Neuron, 99(3), 588-597.
27

Mar 2018

An Integrative Framework for Sensory, Motor, and Cognitive Functions of the Posterior Parietal Cortex

Miller Lab

Neuroscience

Freedman and Ibos give us a new general framework to think about the functions of the parietal cortex.

Freedman, D. J., & Ibos, G. (2018). An Integrative Framework for Sensory, Motor, and Cognitive Functions of the Posterior Parietal Cortex. Neuron, 97(6), 1219-1234.
25

Jul 2017

Partially Mixed Selectivity in Human Posterior Parietal Association Cortex

Miller Lab

Neuroscience

More evidence for mixed selectivity. Mixed selectivity is “a neural encoding scheme in which different task variables and behavioral choices are combined indiscriminately in a non-linear fashion within the same population of neurons. This scheme generates a high-dimensional non-linear representational code that allows for a simple linear readout of multiple variables from the same network of neurons” (Fusi et al., 2016). It adds computational horsepower to the brain. In this case, the evidence is from human parietal cortex.

Zhang, C. Y., Aflalo, T., Revechkis, B., Rosario, E. R., Ouellette, D., Pouratian, N., & Andersen, R. A. (2017). Partially Mixed Selectivity in Human Posterior Parietal Association Cortex. Neuron.

For further reading:
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 »
1

Mar 2017

Working memory and decision making in a fronto-parietal circuit model

Miller Lab

Neuroscience

A model of the collaboration and distribution of function between the prefrontal cortex and parietal cortex.

Working memory and decision making in a fronto-parietal circuit model
John D Murray, Jorge H Jaramillo, Xiao-Jing Wang
doi: https://doi.org/10.1101/104802
11

Oct 2016

History-dependent variability in population dynamics during evidence accumulation in cortex

Miller Lab

Neuroscience

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 »
23

Aug 2016

A Putative Multiple-Demand System in the Macaque Brain

Miller Lab

Neuroscience

The multidemand network is a set of frontoparietal areas in humans that are recruited for a wide range of cognitive-demanding tasks. Mitchell et al use FMRI connectivity analysis to identify a putative homolog in monkeys.

Mitchell, Daniel J., et al. “A Putative Multiple-Demand System in the Macaque Brain.” The Journal of Neuroscience 36.33 (2016): 8574-8585.
15

Aug 2016

Interaction between Spatial and Feature Attention in Posterior Parietal Cortex

Miller Lab

Neuroscience

Ibos and Freedman show that spatial and feature-based attention independently modulate activity in area LIP and that they added together. This suggests a common function of gating task-relevant features, whether they are spatial or non-spatial.

Ibos, Guilhem, and David J. Freedman. “Interaction between Spatial and Feature Attention in Posterior Parietal Cortex.” Neuron (2016).
11

Jul 2016

Coding of Visual, Auditory, Rule, and Response Information in the Brain: 10 Years of Multivoxel Pattern Analysis

Miller Lab

Neuroscience

Woolgar et al provide a meta-analysis of experiments using multivoxel pattern analysis in FMRI. They show that cortical areas traditionally though to be visual, auditory or motor, primarily (though not exclusively) code visual, auditory, and motor information. However, the frontoparietal cortex is hypothesized to a multiple-demand network and it shows domain generality, coding multisensory and rule information.

Woolgar, Alexandra, Jade Jackson, and John Duncan. “Coding of visual, auditory, rule, and response information in the brain: 10 years of multivoxel pattern analysis.” Journal of cognitive neuroscience (2016).
13

Jul 2015

Remapping, Spatial Stability, and Temporal Continuity: From the Pre-Saccadic to Postsaccadic Representation of Visual Space in LIP

Miller Lab

Neuroscience

Mirpour and Bisley provide new insights into how saccadic remapping produces perceptual stability during eye movements.

Mirpour, Koorosh, and James W. Bisley. “Remapping, Spatial Stability, and Temporal Continuity: From the Pre-Saccadic to Postsaccadic Representation of Visual Space in LIP.” Cerebral Cortex (2015): bhv153.
10

Jul 2015

Single-trial spike trains in parietal cortex reveal discrete steps during decision-making

Miller Lab

Neuroscience

Decision-making due to a gradual ramp of neural firing rates? Nope. There are discrete state changes that are more informative that spike counts.

Single-trial spike trains in parietal cortex reveal discrete steps during decision-making
Kenneth W. Latimer, Jacob L. Yates, Miriam L. R. Meister, Alexander C. Huk, and Jonathan W. Pillow
Science 10 July 2015: 349 (6244), 184–187. [DOI:10.1126/science.aaa4056]