TY - JOUR
T1 - Inferring decoding strategies for multiple correlated neural populations
AU - Lakshminarasimhan, Kaushik J.
AU - Pouget, Alexandre
AU - DeAngelis, Gregory C.
AU - Angelaki, Dora E.
AU - Pitkow, Xaq
N1 - Funding Information:
This work was supported by NIH R01 DC04260, R21 DC014518, NSF NeuroNex 1707400, the Simons Collaboration for the Global Brain, and the Swiss National Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank Adam Zaidel, Yong Gu, & Aihua Chen for performing the neural recordings, as well as Sheng Liu & Yong Gu for performing the muscimol inactivation experiments.
Publisher Copyright:
© 2018 Lakshminarasimhan et al. http://creativecommons.org/licenses/by/4.0/.
PY - 2018/9
Y1 - 2018/9
N2 - Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations. By expanding current theories of neural coding and incorporating results from inactivation experiments, we demonstrate here that it is possible to infer decoding weights of different brain areas at a coarse scale without precise knowledge of the correlation structure. We apply this technique to neural data collected from two different cortical areas in macaque monkeys trained to perform a heading discrimination task. We identify two opposing decoding schemes, each consistent with data depending on the nature of correlated noise. Our theory makes specific testable predictions to distinguish these scenarios experimentally without requiring measurement of the underlying noise correlations.
AB - Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations. By expanding current theories of neural coding and incorporating results from inactivation experiments, we demonstrate here that it is possible to infer decoding weights of different brain areas at a coarse scale without precise knowledge of the correlation structure. We apply this technique to neural data collected from two different cortical areas in macaque monkeys trained to perform a heading discrimination task. We identify two opposing decoding schemes, each consistent with data depending on the nature of correlated noise. Our theory makes specific testable predictions to distinguish these scenarios experimentally without requiring measurement of the underlying noise correlations.
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U2 - 10.1371/journal.pcbi.1006371
DO - 10.1371/journal.pcbi.1006371
M3 - Article
C2 - 30248091
AN - SCOPUS:85054601527
VL - 14
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 9
M1 - e1006371
ER -