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
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
This study shows the role of alpha and beta oscillations in the prefrontal cortex and frontal eye fields in a classic test of cognitive control: anti-saccades. It also shows how these oscillatory patterns develop with adulthood.
Hwang, Kai, et al. “Frontal preparatory neural oscillations associated with cognitive control: A developmental study comparing young adults and adolescents.” NeuroImage (2016).
Miller Lab Alumnus Andreas Nieder tells you everything you need to know about the brain substrates of the sense of number:
Nieder, Andreas. “The neuronal code for number.” Nature Reviews Neuroscience (2016).
Nice review of past work on the neurobiology of working memory and capacity limits:
Constantinidis, Christos, and Torkel Klingberg. “The neuroscience of working memory capacity and training.” Nature Reviews Neuroscience (2016).
Although there is a caveat: More recent work suggests that the substrate of working memory is *not* sustained spiking activity. That is an artifact of cross-trial averaging. “Delay activity” is more sparse and bursty on single trials. This suggests a different memory substrate.
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
Stokes, Mark G. “‘Activity-silent’working memory in prefrontal cortex: a dynamic coding framework.” Trends in cognitive sciences 19.7 (2015): 394-405.
Stokes and Spaak review our recent work on single-trial analysis of working memory “delay” activity. This showed that the classic profile of sustained activity as the memory substrate is an artifact of averaging across trials. The assumption is that averaging cancels out noise. Instead, it may be covering up important details of the dynamics of neural activity.
Read more here:
The Importance of Single-Trial Analyses in Cognitive Neuroscience
Mark Stokes and Eelke Spaak
Trends in Cognitive Sciences
The original paper:
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 »
A wonderful tribute to a dear friend
Nice paper showing that different task demands in different task stages engage different oscillatory bands in the prefrontal cortex.
Wimmer, Klaus, et al. “Transitions between Multiband Oscillatory Patterns Characterize Memory-Guided Perceptual Decisions in Prefrontal Circuits.”The Journal of Neuroscience 36.2 (2016): 489-505.