Inferring decoding strategies for multiple correlated neural populations.
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2018-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC6188888?pdf=render |
| _version_ | 1818313620260388864 |
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| author | Kaushik J Lakshminarasimhan Alexandre Pouget Gregory C DeAngelis Dora E Angelaki Xaq Pitkow |
| author_facet | Kaushik J Lakshminarasimhan Alexandre Pouget Gregory C DeAngelis Dora E Angelaki Xaq Pitkow |
| author_sort | Kaushik J Lakshminarasimhan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T08:36:38Z |
| format | Article |
| id | doaj.art-efa4e7c671e0432cb6af403715d42e9c |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-13T08:36:38Z |
| publishDate | 2018-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-efa4e7c671e0432cb6af403715d42e9c2022-12-21T23:53:37ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582018-09-01149e100637110.1371/journal.pcbi.1006371Inferring decoding strategies for multiple correlated neural populations.Kaushik J LakshminarasimhanAlexandre PougetGregory C DeAngelisDora E AngelakiXaq PitkowStudies 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.http://europepmc.org/articles/PMC6188888?pdf=render |
| spellingShingle | Kaushik J Lakshminarasimhan Alexandre Pouget Gregory C DeAngelis Dora E Angelaki Xaq Pitkow Inferring decoding strategies for multiple correlated neural populations. PLoS Computational Biology |
| title | Inferring decoding strategies for multiple correlated neural populations. |
| title_full | Inferring decoding strategies for multiple correlated neural populations. |
| title_fullStr | Inferring decoding strategies for multiple correlated neural populations. |
| title_full_unstemmed | Inferring decoding strategies for multiple correlated neural populations. |
| title_short | Inferring decoding strategies for multiple correlated neural populations. |
| title_sort | inferring decoding strategies for multiple correlated neural populations |
| url | http://europepmc.org/articles/PMC6188888?pdf=render |
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