Ott and Nieder show that stimulating dopamine D2 receptors enhancing working memory related activity in the prefrontal cortex.
Ott, Torben, and Andreas Nieder. “Dopamine D2 Receptors Enhance Population Dynamics in Primate Prefrontal Working Memory Circuits.”Cerebral Cortex (2016).
A very nice experiment from Matt Chafee et al (as usual). They show that neurons in the prefrontal cortex don’t have fixed properties. Instead, they show “mixed selectivity” that changes with behavioral context and is biased toward stimuli that inhibit prepotent responses. Sounds like cognitive control to me.
Blackman, Rachael K., et al. “Monkey prefrontal neurons reflect logical operations for cognitive control in a variant of the AX continuous performance task (AX-CPT).” The Journal of Neuroscience 36.14 (2016): 4067-4079.
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.
Ester et al addressed the dichotomy of source vs site in visual attention. The frontoparietal cortex has long been thought to be the “source” of top-down attention signals that enhance activity at “sites” in posterior (sensory) cortex that represent visual stimuli. They used fMRI, a roving searchlight analysis, and an inverted encoding model to show that stimulus representations are all over the cortex and enhanced by attention. This calls the dichotomy between source and site into question.
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.
Nice review and test of a hypothesis about the role of transient beta oscillations in cortical processing.
Sherman et al (2016) Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice
For much of the history of modern neuroscience, it has been a assumed that the neuron is the functional unit of the brain. But now there is increasing evidence that ensembles of neurons, not individuals, are the functional units. One line of evidence is that many neurons in higher cortical areas have “mixed selectivity” , responses to diverse combinations of variables; they don’t signal one “message”. Thus, their activity only makes sense when simultaneously considering the activity of other neurons. In fact, we (Rigotti et al., 2013; Fusi et al., 2016) have shown that mixed selectivity gives the brain the computational horsepower needed for complex behavior.
In this paper, Dehaqani et al show that simultaneously recorded prefrontal cortex neurons have high-dimensional, mixed-selectivity, representations and convey more information as a population than even individuals. This was especially true for parts of visual space that were weakly encoded by single neurons. Less-informative neurons were recruited into ensemble to fully encode visual space.
Prefrontal neurons expand their representation of space by increase in dimensionality and decrease in noise correlation. Mohammad-Reza Dehaqani, Abdol-Hossein Vahabie, Mohammadbagher Parsa, Behrad Noudoost, Alireza Soltani
doi: http://dx.doi.org/10.1101/065581
Further reading:
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 »
Yuste, Rafael. “From the neuron doctrine to neural networks.” Nature Reviews Neuroscience 16.8 (2015): 487-497.
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).
LIP has been the area for studying motion direction discrimination as model of decision-making. In this paper, Katz et al show that deactivation of LIP has little effect on that model task. Deactivating an upstream area, MT, where decision signals are weaker, however, caused a big deficit.
Dissociated functional significance of decision-related activity in the primate dorsal stream. Leor N. Katz, Jacob L. Yates, Jonathan W. Pillow & Alexander C. Huk Nature.
Sure, this is a cautionary tale of correlates does not equal causation. But it is important not to over-interpret the results of lesions/deactivations. They identify *bottlenecks* in neural processing, not contributions. Just because there is no effect of deactivation doesn’t mean that a given area doesn’t contribute. MT could be providing the raw materials that a number of downstream areas, including LIP, use for decision-making. This doesn’t mean that LIP doesn’t contribute to decisions, it just means that it is not the only area that contributes.
This is in line with recent work showing that neural processing is more distributed than previously thought. For example, see:
Siegel, M., Buschman, T.J., and Miller, E.K. (2015) Cortical information flow during flexible sensorimotor decisions. Science. 19 June 2015: 1352-1355. View PDF »
An excellent, comprehensive review of the neurobiology of decision-making by David Freedman and John Asaad.
Neuronal Mechanisms of Visual Categorization: An Abstract View on Decision Making
David J. Freedman and John A. Assad, Annual Review of Neuroscience, 2016
DOI: 10.1146/annurev-neuro-071714-033919