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 »

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

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 »

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.

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).

 

 

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).

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.

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. LatimerJacob L. YatesMiriam L. R. MeisterAlexander C. Hukand Jonathan W. Pillow
Science 10 July 2015: 349 (6244), 184187. [DOI:10.1126/science.aaa4056]

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).

Siegel, M., Buschman, T.J., and Miller, E.K. (2015) Cortical information flow during flexible sensorimotor decisions.  Science19 June 2015: 1352-1355.

During flexible behavior, multiple brain regions encode sensory inputs, the current task, and choices.  It remains unclear how these signals evolve. We simultaneously recorded neuronal activity from six cortical regions (MT, V4, IT, LIP, PFC and FEF) of monkeys reporting the color or motion of stimuli. Following a transient bottom-up sweep, there was a top-down flow of sustained task information from frontoparietal to visual cortex.  Sensory information flowed from visual to parietal and prefrontal cortex. Choice signals developed simultaneously in frontoparietal regions and travelled to FEF and sensory cortex. This suggests that flexible sensorimotor choices emerge in a frontoparietal network from the integration of opposite flows of sensory and task information.

From the MIT News Office:
Uncovering a dynamic cortex
Neuroscientists show that multiple cortical regions are needed to process information.

Miller Lab alumnus, Andreas Nieder, continues his epic investigations into the neural basis of number sense.  Here, Viswanathan and Nieder show that training to make numerosity judgments sharpens neural selectivity in frontal cortex but not in parietal cortex.  It seems that the number representations in parietal cortex are innate whereas in the frontal cortex, they are learned.

Miller Lab alumnus David Freedman and colleagues present a model that shows how categorical neural activity can develop through learning.   As a result of top-down influences from decision neurons, categorical representations develop in neurons that show choice-correlated activity fluctuations.  They test the model via recordings from parietal cortex.

Choice-correlated activity fluctuations underlie learning of neuronal category representation
Tatiana A. Engel, Warasinee Chaisangmongkon, David J. Freedman & Xiao-Jing Wang

Braunlich et al compared stimulus identity vs categorization tasks using fMRI in humans.  They applied a Constrained Principal Components Analysis.  They found evidence for two distinct frontoparietal networks.  One that rapidly analyzes the stimuli and a second one that more slowly categorizes them.

Dotson et al report both 0 and 180 deg phase synchrony between the prefrontal and parietal cortices during a working memory task, suggestion both formation and segregation of different functional networks by neural synchrony.

Quentin et al examined the relationship between white matter connectivity between the frontal and parietal cortices and the improvement of visual perception by beta oscillatory synchrony between them.  They used diffusion imaging to examine the white matter connectivity and used transcranial magnetic stimulation (TMS) over the right frontal eye fields (FEF) to induce beta oscillations.  Individuals that showed greater perceptual improvement with the beta TMS also had stronger white matter connectivity.

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

Matt Chafee and colleagues used multiple-electrode recording in the prefrontal and parietal cortices to examine the temporal dynamics of their neural activity during a categorization task.   They decoded category signals from patterns of simultaneously recorded in small bins and asked whether the resulting  information  time series in one area could predict the other.  This showed that  “executive” top-down signals flow from the prefrontal to parietal cortex.