The CoAxies

Each year the lab gets together to discuss what it things are the best papers we've discussed over the past 12 months. We break our topics down into four categories and, as a group, select the best paper in that group. We don't wear tuxes or evening gowns (at least not yet), but we do have fun.

The winners only get our gratitude at their great contribution to science. (We're not awash in NIH money afterall!).


Computational Cognitive Neuroscience (winner): Palminteri, S., Wyart, V., & Koechlin, E. (2017). The importance of falsification in computational cognitive modeling. Trends in cognitive sciences, 21(6), 425-433.

While model parsimony is well-appreciated, the complementary role of model falsification has been relatively neglected. Palminteri and colleagues demonstrate that the omission of generative performance as a criterion for model selection can result in unwarranted conclusions about the ability of a model to capture a behavioral effect, and they suggest a rigorous and practical framework for a model selection process which evaluates both the generative and predictive capacity of candidate models. This proposal has the potential to vastly increase the rigor of cognitive models as computational hypotheses, allowing us to take one step closer to a mechanistic understanding of cognition. (summary by K. Bond)

Decision-Making (winner): Klaus, A., Martins, G. J., Paixao, V. B., Zhou, P., Paninski, L., & Costa, R. M. (2017). The spatiotemporal organization of the striatum encodes action space. Neuron, 95(5), 1171-1180.

The findings of Klaus et al. provide exciting empirical evidence of an emerging perspective on how direct and indirect pathway SPNs encode movement behavior. We loved how the beautifully presented data from Rui Costa’s group show a striking link between patterns of activity in locally-biased SPN ensembles and specific movement behaviors in rodents (see Figure 3). In particular, the similarity between activity patterns of spatially proximal ensembles encoded more similar behaviors, while more dissimilar behaviors were encoded by more distal groups of SPNs. Though current prevailing theories of SPN function focus on either go/no-go signaling or movement speed or vigor, these recent results lend strong support to the view that SPN ensemble activity reflects features of movement representations. (summary by K. Jarbo)

Learning (winner): Krakauer, J. W., Ghazanfar, A. A., Gomez-Marin, A., MacIver, M. A., & Poeppel, D. (2017). Neuroscience needs behavior: correcting a reductionist bias. Neuron, 93(3), 480-490.

While this paper clearly snuck in from the Computational Neuroscience category (poor quality control on the judges here), here Krakauer and colleagues present a compelling case against a strictly reductionist approach to neuroscience; arguing instead, that neuroscientists will be best served in their quest to understand the brain by prioritizing cognitive and behavioral research. This paper is, in large part, a re-hashing of old, but foundational ideas (i.e., Marr's three levels of analysis), which the authors invoke to highlight the dangers of pure reductionism and the potential breakthroughs we stand to miss when we ignore emergent phenomena. It's an important message and a great read. (summary by K. Dunovan)

Network Neuroscience (winner): Bassett, D. S., & Mattar, M. G. (2017). A network neuroscience of human learning: potential to inform quantitative theories of brain and behavior. Trends in cognitive sciences, 21(4), 250-264.

In this review, Bassett & Mattar make a compelling case for how network topology theory can inform mechanistic models of behavior. Unlike many other papers in this vein, which largely rely on qualitiative comparisons of network theory and abstract relations to behavior, this article makes several specific suggestions on how to appropriately utlize graphs as tool for uniting neuroimaging observations with quantiative cognitive models. (summary by T. Verstynen)

Runners up:

Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258.

Hunt, L. T., & Hayden, B. Y. (2017). A distributed, hierarchical and recurrent framework for reward-based choice. Nature Reviews Neuroscience, 18(3), 172.

Dezza, I. C., Angela, J. Y., Cleeremans, A., & Alexander, W. (2017). Learning the value of information and reward over time when solving exploration-exploitation problems. Scientific reports, 7(1), 16919.

Hwang, K., Bertolero, M. A., Liu, W. B., & D'Esposito, M. (2017). The human thalamus is an integrative hub for functional brain networks. Journal of Neuroscience, 37(23), 5594-5607.

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