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Research Articles: Behavioral/Cognitive
Stable and dynamic coding for working memory in primate prefrontal cortex Eelke Spaaka, Kei Watanabea,b, Shintaro Funahashic and Mark G. Stokesa a
Department of Experimental Psychology, University of Oxford, Oxford, OX1 3UD, United Kingdom.
b
Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Osaka, 565-0871, Japan. c
Kokoro Research Center, Kyoto University, Kyoto, 606-8501, Japan.
DOI: 10.1523/JNEUROSCI.3364-16.2017 Received: 31 October 2016 Revised: 9 March 2017 Accepted: 13 April 2017 Published: 30 May 2017
Author contributions: E.S. and M.S. designed research; E.S., K.W., S.F., and M.S. performed research; E.S. and M.S. analyzed data; E.S., K.W., S.F., and M.S. wrote the paper. Conflict of Interest: The authors declare no competing financial interests. We would like to thank the Biotechnology & Biological Sciences Research Council (to MGS) and the National Institute for Health Research Oxford Biomedical Research Centre Programme based at the Oxford University Hospitals Trust, University of Oxford, and the Japan Society for the Promotion of Science (Grants-in-Aid for Scientific Research, 22220003 and 25240021 to SF, and 16K21686 to KW). Two monkeys used in Experiments 2 and 3 were supplied from the National Bioresource Project (Japanese Monkey), supported by the MEXT, Japan. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health, or the JSPS. We would also like to thank Nicholas Myers for very helpful comments on an earlier draft of this paper. Corresponding author: Eelke Spaak,
[email protected] Cite as: J. Neurosci ; 10.1523/JNEUROSCI.3364-16.2017 Alerts: Sign up at www.jneurosci.org/cgi/alerts to receive customized email alerts when the fully formatted version of this article is published. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
Accepted manuscripts are peer-reviewed but have not been through the copyediting, formatting, or proofreading process. Copyright © 2017 Spaak et al.
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Stable and dynamic coding for working memory in primate prefrontal cortex
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Acknowledgements
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Subjects and apparatus
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Experiment 1
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ȋ
Ǧ ǡ p δ ͲǤͲͲͳȌǤ ʹǡ ͳ͵
ϰϮϯ ϰϮϰ ϰϮϱ ϰϮϲ ϰϮϳ ϰϮϴ ϰϮϵ ϰϯϬ ϰϯϭ ϰϯϮ ϰϯϯ ϰϯϰ ϰϯϱ ϰϯϲ ϰϯϳ ϰϯϴ ϰϯϵ ϰϰϬ ϰϰϭ ϰϰϮ ϰϰϯ ϰϰϰ ϰϰϱ ϰϰϲ ϰϰϳ ϰϰϴ ϰϰϵ ϰϱϬ
ǡ ȋǦȌ ǡ
ȋp αͲǤͲͲͺȌǤ
ͳȋȌ ʹȋȌ
Ǥ ͳǡ
ǡ
ʹǤ
ǡ Ǧ
ȋǤǡʹͲͳ͵ȌǤǡ ǡ
ǡ
ǡ
Ǥǡ
Ǥ
ǡ ǡ ǡ
Ǥ
Ǧ
ȋǤǡʹͲͳͶȌǤ ͳʹǡ
ȋ ͳǡ
Ǣ δ ͲǤͲͲͳȌǤ ʹǡ
ǡ
Ǧ Ǧǡ
Ǥ ͳǡ
ǡ
Ǥǡ
ǡ
Ǥ Ǯǯ Ǧ
ȋǤǡʹͲͳͲȌǡǦ
Ǧ
ǡ
Ǥ
ϰϱϭ ϰϱϮ ϰϱϯ ϰϱϰ ϰϱϱ ϰϱϲ ϰϱϳ ϰϱϴ ϰϱϵ ϰϲϬ
PFC representation generalizes over time, yet shows clearly dynamic epochs
ͳ
Ǧ
Ǥ
Ǧ Ǧ
ȋ ǡ ʹͲͳͶȌǤ ǡ
Ǥ ǡ
ǡ
Ǥ ͳͶ
ϰϲϭ ϰϲϮ ϰϲϯ ϰϲϰ ϰϲϱ ϰϲϲ ϰϲϳ ϰϲϴ ϰϲϵ ϰϳϬ ϰϳϭ ϰϳϮ ϰϳϯ ϰϳϰ ϰϳϱ ϰϳϲ ϰϳϳ ϰϳϴ ϰϳϵ ϰϴϬ ϰϴϭ ϰϴϮ ϰϴϯ ϰϴϰ ϰϴϱ
ǡ
Ǧ
t1ȋȌ t2Ǥ Ǧ
ǡǦ
ͳǤ
Ǧ
Ǥ
Ǧ
ͳʹȋp δͲǤͲͲͳǢ ʹǡȌǤ ͳǡ
ͲǤͷȂͳǤͲ
ǡ
ǮǦǯ
ȋ Ȍ
͵Ǥͷ
Ǥ
ǡ Ǧ
Ǥ ǡ
Ǥ
Ǧ
Ǧǡǡ
ǡ
ǡ
ͳ ʹ ȋ ʹǡ ȌǤ ǡ
ͳǤ
Ǯǯ
Ǧ ǡ ͳ
ͳǡͺͲͲ
ʹǤ ȋ Ǧ
ʹ
ǡ Ǧ
ǤȌ
Ǧ
ǡ
ȋ ʹǢȌǤ
ϰϴϲ ϰϴϳ ϰϴϴ ϰϴϵ ϰϵϬ ϰϵϭ ϰϵϮ ϰϵϯ ϰϵϰ ϰϵϱ ϰϵϲ ϰϵϳ
Factors contributing to dynamic coding: changing neuronal selectivity
Ǥ
ȋǤǡʹͲͲͺǢǤǡʹͲͳ͵Ǣ Ǥǡ ʹͲͳȌǡ ǡ ǡ
ȋ
Ǣ ȌǤ
Ǥ
Ǧ
F
ǦǤǡ
Ǧ
ȋ͵ʹͶͻͺͳȌ
ͳͷ
ϰϵϴ ϰϵϵ ϱϬϬ ϱϬϭ ϱϬϮ ϱϬϯ ϱϬϰ ϱϬϱ ϱϬϲ ϱϬϳ ϱϬϴ ϱϬϵ ϱϭϬ ϱϭϭ ϱϭϮ ϱϭϯ ϱϭϰ ϱϭϱ ϱϭϲ ϱϭϳ ϱϭϴ ϱϭϵ ϱϮϬ ϱϮϭ ϱϮϮ ϱϮϯ ϱϮϰ ϱϮϱ ϱϮϲ ϱϮϳ ϱϮϴ
F
ǡ
ǯ
ȋ ȌǤͳ ͵ȋ ͵ȌǤ
ȋ
Ǣ ȋ Ǥǡ ͳͻͻͻȌȌǡ
ǡ
Ǥ
ǯ
ȋ ͵ǡ Ȍǡ
Ǥ
ȋͷͲͲ Ȍǡ
Ǧ
ȋͺȀͳ͵ǣΨȌ
ȋ
Ǧ
ȌǤ
ǡȽαͲǤͲͷǤ ǡ
Ǥ ͵ʹͶ
Ǧ
ǡ ͺ͵ ȋʹΨȌ
Ǧ
Ǥ
ǡ
ȋͳͲȀͳͺͻǣ ͷΨȌǡ
ͳ
ȋǡȋ ǤǡʹͲͳͲȌȌǤ ʹǡ
ȋʹͶȀͻʹǣ ʹΨȌ
ǡ ͵ ͳͲͲ ȋ͵ΨȌ
Ǧ
ȋ ͵
ȌǤ ʹǡ ͳ͵Ȁ͵ȋͳͺΨȌ
ǡ
ȋͲǤȌȋ
ȌǤ
ȋ
Ȍʹ
ͳ
Ǥ ʹ
ȋȌͳȋȌǤǡ
ʹ
ͳǤ
ϱϮϵ ϱϯϬ ϱϯϭ ϱϯϮ ϱϯϯ ϱϯϰ
Factors contributing to dynamic coding: dynamic onset cascade
ǡ
ǯ
ȋ ͵ǡ
Ȍ ǯǤǡǡ
ͳ
ϱϯϱ ϱϯϲ ϱϯϳ ϱϯϴ ϱϯϵ ϱϰϬ ϱϰϭ ϱϰϮ ϱϰϯ ϱϰϰ ϱϰϱ ϱϰϲ ϱϰϳ ϱϰϴ ϱϰϵ ϱϱϬ ϱϱϭ ϱϱϮ ϱϱϯ ϱϱϰ ϱϱϱ ϱϱϲ ϱϱϳ ϱϱϴ ϱϱϵ ϱϲϬ ϱϲϭ ϱϲϮ ϱϲϯ
ȋǤǡʹͲͳȌǤ ǡ
Ǧ
ȋ
ȌǤ
ȋͳǣɏαͲǤǡp αͳǤʹȉͳͲǦͳͺǢʹǣɏαͲǤͲǡpαͳǤʹȉͳͲǦͳȌ
ȋ ͳǣ ɏ α ͲǤͶǡ p α Ǥ ȉ ͳͲǦͷǢ ʹǣ ɏ α ͲǤʹǡ p α ͳǤͷ ȉ ͳͲǦͻȌǡ
ǡ Ǥ
Ǧ
Ǥ
Ǧ Ǥ ǡ
ǡ Ǥ
Ǧ
Ǥ ǡ
Ǥ ǡ
ȋǤǤǡ
ǢȌǤ ͶǤ Ǧ Ǧ
Ǥ
ȋǤǤ
ȌǡǦ
Ǥ ǡ Ǧ
ȋ ͵Ȍ
Ǥ
ϱϲϰ ϱϲϱ ϱϲϲ ϱϲϳ ϱϲϴ ϱϲϵ ϱϳϬ ϱϳϭ
Representational space is stable despite a dynamic population code
ǣ
Ǯǯ
Ǯǯ
Ǧ Ǥ
ǡ ǡ
Ǥǡ
ǡ ͳ
ϱϳϮ ϱϳϯ ϱϳϰ ϱϳϱ ϱϳϲ ϱϳϳ ϱϳϴ ϱϳϵ ϱϴϬ ϱϴϭ ϱϴϮ ϱϴϯ ϱϴϰ ϱϴϱ ϱϴϲ ϱϴϳ ϱϴϴ ϱϴϵ ϱϵϬ ϱϵϭ ϱϵϮ ϱϵϯ ϱϵϰ ϱϵϱ
Ǧ
Ǥ ǡ
ʹǡ
ȋ ͳȌ
Ǥ
ȋǤǤǡ
ǯ
Ȍ
Ǧ
ȋ Ǥǡ ʹͲͲͺǢ ǡ ʹͲͳ͵ȌǤ
ʹǣ
ȋͲǤͲͷȂͲǤ͵ȌȋͳȂͳǤͶȌǤ
ͷǤ ǡ
ǡ ȋ
αͲǤͻͳǡ α Ͷ ȉ ͳͲǦͳͳȌǤ Ǧ
ȋ ǡ ͳͻͻȌȋȌ
ȋ ͷ
ȌǤ
ǡ
ȋ ͷȌǤ ǡ
Ǧ
ǡ
Ǥ
ǡ
Ǧ
ǡ Ǥ ǡ
Ǥ ͷǣ
Ǧ
Ǥ ǡ
ǡ
ǡ
ǡ Ǥ
ϱϵϲ ϱϵϳ ϱϵϴ ϱϵϵ ϲϬϬ ϲϬϭ ϲϬϮ ϲϬϯ ϲϬϰ ϲϬϱ ϲϬϲ ϲϬϳ
Simultaneous performance of a competing task does not abolish dynamic coding and reveals neurons with complex mixed selectivity
ʹ
͵ ȋ ǡ ʹͲͳͶȌǤ ǡ
ȋǤǤǦȌǤ
ǡ Ǥ ǡǦǦ
ǡ Ǥ Ǧ
ȋǤǤǡ
ȌǤǦȋǤǤ Ȍǡ
Ǧ
ȋ ȌǤ
ͳͺ
ϲϬϴ ϲϬϵ ϲϭϬ ϲϭϭ ϲϭϮ ϲϭϯ ϲϭϰ ϲϭϱ ϲϭϲ ϲϭϳ ϲϭϴ ϲϭϵ ϲϮϬ ϲϮϭ ϲϮϮ ϲϮϯ ϲϮϰ ϲϮϱ ϲϮϲ ϲϮϳ ϲϮϴ ϲϮϵ ϲϯϬ ϲϯϭ ϲϯϮ
ǡ
ȋ
Ǧ Ȍ
͵ȋ ȌǤ ͳʹ
Ǥǡ
Ǧ
ǢǤǤ
ȋ Ǥǡ ͳͻͻͺǢ Ǥǡ ʹͲͲǢ ǤǡʹͲͳ͵ȌǤǡ
Ǥ ͵
Ǥ
ǡ
Ǥ
ǯ
Ǧ ͵ͷ ȋ
Ǧ
ȌǤ
Ǥ
ȋ
Ȍ
Ǥ
ȋȀȀȀȌ
ȋαͲȌǤ
ȋȌǤ ǡ
ǡ
Ȁ
ȋ
ȌǤ
ȋ ǡ
Ȍǡ
Ǥ
ǡͳʹǤǡ
ȋǦȌ
Ǥ
ϲϯϯ ϲϯϰ
ϲϯϱ ϲϯϲ ϲϯϳ ϲϯϴ ϲϯϵ ϲϰϬ ϲϰϭ ϲϰϮ ϲϰϯ ϲϰϰ
Discussion
Ǥ
ǡ
ǡ
ǡ
ǦǤ ǡ
Ǧ
Ǧ
Ǧ Ǥ
ǣ
ǡ
Ǥ
ͳͻ
ϲϰϱ ϲϰϲ
ǡ
Ǥ
ϲϰϳ ϲϰϴ ϲϰϵ ϲϱϬ ϲϱϭ ϲϱϮ ϲϱϯ ϲϱϰ ϲϱϱ ϲϱϲ ϲϱϳ ϲϱϴ ϲϱϵ ϲϲϬ ϲϲϭ ϲϲϮ ϲϲϯ ϲϲϰ ϲϲϱ ϲϲϲ ϲϲϳ ϲϲϴ ϲϲϵ ϲϳϬ ϲϳϭ ϲϳϮ ϲϳϯ ϲϳϰ ϲϳϱ ϲϳϲ ϲϳϳ ϲϳϴ ϲϳϵ ϲϴϬ ϲϴϭ ϲϴϮ
Dynamic coding in working memory
ǡ
ǡ
Ǥ
ȋǤǡͳͻͻͻǢǤǡʹͲͲ͵ǢǡʹͲͲͷǢ ǤǡʹͲͲͺǢǤǡʹͲͳͲǢǤǡʹͲͳʹǢǤǡʹͲͳ͵ǢǤǡʹͲͳͷǢ Ǥǡ ʹͲͳȌǡ
ȋǡʹͲͲͻǢǡʹͲͳͲȌǤ
ǡ
Ǧ
Ǥ ǡ
ȋǤǡʹͲͲͺǢǤǡʹͲͳ͵ȌȀ
ȋ Ǥǡ ʹͲͲͺǢ Ǥǡ ʹͲͳͲȌǤ ǡ
Ǧ ȋ Ǥǡ ʹͲͲͺȌ
ȋ Ǥǡ ʹͲͳ͵Ȍ
ȋ
ǤȋǤǡʹͲͲͺȌȌǤ Ǥ
Ǥ
ȋ ǯǡʹͲͲ͵Ǣ ǡʹͲͳͷȌǤ ǡ Ǧ ǡ
Ǥ ǡ
Ǥǡ ͳ
Ǥ
ǡ
ͳ
Ǥ
ǡ
Ǥ
Ǥ
Ǧ
ǡ
ȋǡʹͲͳͷȌǡǦ ȋ
Ǯǯǡ ʹͲ
ϲϴϯ ϲϴϰ
ǤǤ
ȌǤ
Ǥ
ǡ Ǧ
Ǥ
ϲϴϱ ϲϴϲ ϲϴϳ ϲϴϴ ϲϴϵ ϲϵϬ ϲϵϭ ϲϵϮ ϲϵϯ ϲϵϰ ϲϵϱ ϲϵϲ ϲϵϳ ϲϵϴ ϲϵϵ ϳϬϬ ϳϬϭ ϳϬϮ ϳϬϯ
Stable representational geometry
Ǧ
Ǧ
Ǥ
Ǥ ȋȌ ȋ ǡ ʹͲͳ͵Ȍ
Ǯ
ǯǤ
ǡ
ȋ
ǤǡʹͲͳͶȌǤ Ǧ
ȋǤǤǡ
Ȍ
Ǥ
Ȃ ǡ
Ǥ ǡ
Ǥ
ϳϬϰ ϳϬϱ ϳϬϲ ϳϬϳ ϳϬϴ ϳϬϵ ϳϭϬ ϳϭϭ ϳϭϮ ϳϭϯ ϳϭϰ ϳϭϱ ϳϭϲ ϳϭϳ ϳϭϴ ϳϭϵ
Active versus silent (‘hidden’) maintenance of WM
ȋǤǡʹͲͲͺǢ ǡʹͲͳͶȌ
Ǧ
ȋ
ǡ ȋ Ǥǡ ʹͲͲͲȌȌǡ
Ǥ
Ǧ
Ǧ
Ǥ
ȋ© Ǥǡ ʹͲͲͻǢ Ǥǡ ʹͲͳͷǢ ǤǡʹͲͳȌǡǦ
Ǧ
Ǧ
ȋǡʹͲͳͷȌǤ
Ǧ
ȋ Ǥǡ ͳͻͺͻǢ Ǧ
ǡ ͳͻͻͷȌǡ
ȋǤǡʹͲͲͲǢǡʹͲͲͳȌǤǡ
Ǧ
ǤǦ
ʹͳ
ϳϮϬ ϳϮϭ ϳϮϮ ϳϮϯ ϳϮϰ ϳϮϱ ϳϮϲ ϳϮϳ ϳϮϴ ϳϮϵ ϳϯϬ ϳϯϭ ϳϯϮ ϳϯϯ
ȋǦ
Ǥǡ ʹͲͳͳǢ ǡ ʹͲͳʹȌǡ
Ǥ
ͳǡ
Ǧ
ȋ ǡ ʹͲͳͶȌǤ ǡ
ǡ
ǡ Ǯ
ǯ
Ǥ ǡ ȋ
Ǧ Ȍ
ȋ ʹǢ ͳȌǤ ǡ Ǧ
Ǣ ǡ
ǦǤ
ϳϯϰ ϳϯϱ ϳϯϲ ϳϯϳ ϳϯϴ ϳϯϵ ϳϰϬ ϳϰϭ ϳϰϮ ϳϰϯ ϳϰϰ ϳϰϱ ϳϰϲ ϳϰϳ ϳϰϴ ϳϰϵ ϳϱϬ ϳϱϭ ϳϱϮ ϳϱϯ ϳϱϰ ϳϱϱ ϳϱϲ ϳϱϳ
Dynamic selectivity, dynamic subpopulations, and the neural null space
Ǧ
Ǥ ǡ
ǡ
ȋ Ǥǡ ʹͲͳȌǡ
Ǯ
ǯ ȋ Ǥǡ ʹͲͳʹȌǤ ǡ
ǡ
Ǥ
ǡ
Ǧ Ǧȋ
ǤȋǤǡʹͲͳ͵ȌȌǤ
ǡ ǡ ǡ
ǡ
Ǯ ǯ
ȋ Ȍ Ǥ
ȋǤǤǡ Ǧ
Ȍ
ȋ
Ǧ
Ǣ ʹȌǡ
Ǥ ǡ
Ǥ
ǡ
ǡ ǮǯǤ
ǡ
Ǧ
ʹʹ
ϳϱϴ ϳϱϵ ϳϲϬ ϳϲϭ ϳϲϮ ϳϲϯ ϳϲϰ ϳϲϱ ϳϲϲ ϳϲϳ ϳϲϴ ϳϲϵ ϳϳϬ ϳϳϭ ϳϳϮ ϳϳϯ ϳϳϰ ϳϳϱ ϳϳϲ ϳϳϳ ϳϳϴ ϳϳϵ ϳϴϬ ϳϴϭ
Ǥ
Ǯ
ǯǡ
Ǧ
ǡ
ȋǤǡʹͲͳȌǤ ǡ
Ǯ
ǯ
ȋ Ǥǡ ʹͲͳͶȌǤ ǡ
Ǥ
Ǧ
Ǥ Ǯǯ
ǡ
Ǯǯ
ǡ
ǯ
Ǥ
Ǧ
ȋǤǡ ʹͲͳͶȌǤ
ǡ
ǡ
ǡ
ȋǤǤ
Ǯ
ǯ
Ǣ ȋ Ǥǡ ͳͻͻͻȌȌ
ȋǤǤǡ
all
ȌǤ
ȋ
ǡʹͲͳʹȌǤ ǡ
ȋ
ǤǡʹͲͳͲǢǤǡʹͲͳͶȌǡǤ
ϳϴϮ
ʹ͵
ϳϴϯ
Figure legends
ϳϴϰ
ϳϵϰ
Figure 1. Overview of experimental paradigm and population-level pattern analyses. (a)
Ǧ
ȋ ȌǤ
ȋ ͳȌ ȋ ʹȌ Ǥ (b)
Ǥ
ͳʹǢ
ʹǤ (c)
ǡ
ǣ
ȋ ȌǤ (d) ȋȌ
ȋȌ
ǡ ͳ ʹǤ
Ǥ
ϳϵϱ
ϳϴϱ ϳϴϲ ϳϴϳ ϳϴϴ ϳϴϵ ϳϵϬ ϳϵϭ ϳϵϮ ϳϵϯ
ϳϵϲ ϳϵϳ ϳϵϴ ϳϵϵ ϴϬϬ ϴϬϭ ϴϬϮ ϴϬϯ
Figure 2. Cross-temporal discriminability analysis shows periods of dynamic and stable coding.(a)
Ǧ
ǢǤǤ
Ǥ
Ǣ
Ǧ
ǢǤǤǡ
Ǥ(b) Ǯ
ǯ
Ǥ
ǡ
ǦǤ
ϴϬϰ
ϴϬϱ ϴϬϲ ϴϬϳ ϴϬϴ ϴϬϵ ϴϭϬ ϴϭϭ ϴϭϮ ϴϭϯ ϴϭϰ ϴϭϱ
Figure 3. Single-neuron selectivity analysis reveals neurons with time-varying selectivity. (a) ǡ
ȋǣ ͳǢ ǣ ʹȌǤ
Ǧ Ǥ
Ǥ
Ǥ (b,c) Ǧ
Ǧ
Ǥ
Ǥ
ǯ
Ǥ
Ǧ
Ǥ (a) ͳǤ(b) ʹ ȋǣ Ǧ
ǡ ǣ ǦȌǤ
ϴϭϲ
ϴϭϳ
ϴϭϴ
ʹͶ
ϴϮϱ
Figure 4. Relative contributions of changing selectivity and onset variability to dynamic coding.Ǧ
ȋ ʹȌ ǦǡǤ
Ǧ
Ǥ ǡ
Ǥ
Ǥ
Ǥ
ϴϮϲ
ϴϭϵ ϴϮϬ ϴϮϭ ϴϮϮ ϴϮϯ ϴϮϰ
ϴϮϳ ϴϮϴ ϴϮϵ ϴϯϬ ϴϯϭ ϴϯϮ ϴϯϯ ϴϯϰ
Figure 5. Representational similarity analysis reveals a stable representational geometry. (a)
ȋʹȌǤ
ȋȌ ȋȌǤ (b) Ǧ
ȋȌǡ
ȋ ȌǤ (c) Ǧ
ȋȌ
ȋȌǤ
Ǥ(d)
ȋ
Ǧ
ȌǤ
ϴϯϱ ϴϯϲ ϴϯϳ ϴϯϴ ϴϯϵ ϴϰϬ ϴϰϭ ϴϰϮ ϴϰϯ ϴϰϰ ϴϰϱ ϴϰϲ ϴϰϳ ϴϰϴ ϴϰϵ ϴϱϬ ϴϱϭ ϴϱϮ ϴϱϯ
Figure 6. Dynamic coding is preserved during a more complex dual-task scenario. (a)
͵Ǥȋ
Ȍȋǡ
Ǧ
Ǣ ȌǤ (b) Ǧ
͵Ǥ
Ǧ
Ǥ (c) Ǧ ȋ
Ǧ
Ȍ ͵ͷ ȋ͵ Ȍ ȋͷ Ȍ
Ǥ
Ǧ
Ǥ
Ǥ
Ǥ(d)
ǡ
Ǥ
ǡ
ǡǤ
ǡ Ǥ
ȋͷΨȌ
Ǥǡ Ǧ ǡ
Ǥ
Ǧ
Ǥ
ϴϱϰ
ʹͷ
ϴϱϱ
References ǡ ǡȋͳͻͻͺȌ
Ǥʹͳǣͳ͵ͻͻȂͳͶͲǤ
ϵϬϵ ϵϭϬ ϵϭϮ
ϴϲϮ
ǡ ǡ Ǧǡ ȋʹͲͳͷȌ
ǡ ǡ Ǥ
͵ͷǣ͵ͳͶȂ͵ͳͺͻǤ
ϴϲϯ ϴϲϰ
ǡȋʹͲͳͶȌ ǤʹͷǣʹͲȂʹͶǤ
ϵϭϲ ϵϭϳ
ϴϲϱ
ǡ ǡ ȋʹͲͳͲȌ
Ǥ
͵ͲǣͻͶʹͶȂͻͶ͵ͲǤ
ϵϭϴ
ǡ ȋͳͻͻȌ
ǣ
ǤǤ
ϵϮϭ
ǡ ǡ ǡ ȋʹͲͲ͵Ȍ
Ǥͳ͵ǣͳͳͻȂͳʹͲǤ
ϵϮϯ ϵϮϰ
ϴϱϲ ϴϱϳ ϴϱϴ ϴϱϵ ϴϲϬ ϴϲϭ
ϴϲϲ ϴϲϳ ϴϲϴ ϴϲϵ ϴϳϬ ϴϳϭ ϴϳϮ ϴϳϯ ϴϳϰ ϴϳϱ ϴϳϲ ϴϳϳ ϴϳϴ ϴϳϵ ϴϴϬ ϴϴϭ ϴϴϮ ϴϴϯ ϴϴϰ ϴϴϱ ϴϴϲ ϴϴϳ ϴϴϴ ϴϴϵ ϴϵϬ ϴϵϭ ϴϵϮ ϴϵϯ ϴϵϰ ϴϵϱ ϴϵϲ ϴϵϳ ϴϵϴ ϴϵϵ ϵϬϬ ϵϬϭ ϵϬϮ ϵϬϯ ϵϬϰ ϵϬϱ ϵϬϲ ϵϬϳ ϵϬϴ
ǡ ǡǡ ȋͳͻͺͷȌ Ǥ Ǥ
Ǥ ͷͶǣͳͶȂ͵ͶǤ ǡ ȋʹͲͲͻȌ Ǧ
ǣ
Ǥ
ͳͲǣͳͳ͵ȂͳʹͷǤ ȋʹͲͳͲȌ ǣ ǡ ǡǤͺǣ͵ʹȂ͵ͺͷǤ
ǡ ǡ ǡ ǡ ȋʹͲͳͲȌ
ǣ
ǫͺǣ͵ͺȂͶͲͲǤ
ǡ ǡ ȋʹͲͳͶȌ
Ǥ
ͳǣͶͷͷȂͶʹǤ ǡ ǡ Ǧ
ǡ Ǧ ȋʹͲͲͲȌ
Ǥ ͳͲǣͻͳͲȂ ͻʹ͵Ǥ
ϵϭϭ ϵϭϯ ϵϭϰ ϵϭϱ
ϵϭϵ ϵϮϬ
ȋʹͲͳͷȌ
Ǧ
Ǥ
ͻǣʹǤ ǡ
ǡ Ǧ
ȋͳͻͺͻȌ
ǯ
Ǥ ͳǣ͵͵ͳȂ͵ͶͻǤ ǡ ȋͳͻͳȌ
ǦǤ
ͳ͵ǣͷʹȂͷͶǤ
ϵϮϵ
Ǧ
ȋͳͻͺȌ
Ǥ ǣ ǡǡ
ȋ ǡ ȌǤ ǡ ǡ ǣ
Ǥ ǣ ǣȀȀǤǤ
ȀͳͲǤͳͲͲʹȀ
Ǥ
ͲͳͲͷͲͻ ȏ
ǡʹͲͳȐǤ
ϵϯϬ ϵϯϭ
Ǧ
ȋͳͻͻͷȌ ǤͳͶǣͶȂͶͺͷǤ
ϵϯϮ
ǡ ǡ ȋʹͲͳʹȌ
Ǧ
Ǧ
ǤͶͺͶǣʹȂͺǤ
ϵϮϮ
ϵϮϱ ϵϮϲ ϵϮϳ ϵϮϴ
ϵϯϯ ϵϯϰ ϵϯϱ ϵϯϲ ϵϯϳ ϵϯϴ
ǡ ǡ ȋʹͲͳͶȌ
Ǥ
͵ǣͶ͵ͷȂͶͷǤ
ϵϰϮ
ǡ ǡ Ǧ ǡ
ǡ ȋʹͲͲͲȌ Ǧ
Ǥ ͺ͵ǣ͵Ͳ͵ͳȂ͵ͲͶͳǤ
ϵϰϯ ϵϰϰ
ȋʹͲͲȌ ǣ ʹ
Ǥ
ͻǣͻͲȂͻͷǤ
ϵϰϱ
ǡ ǡ ǡ ȋʹͲͲͳȌ
ǣ
Ǥ ǣǣȀȀǤ
ǤȀǤ
ϵϯϵ ϵϰϬ ϵϰϭ
ϵϰϲ ϵϰϳ
ǡǦ ȋʹͲͲͶȌ
Ǥ
ͳͲǣͷͷ͵ȂͷͷǤ ǡ
ǡ ȋʹͲͳͲȌ
Ǧ
Ǥ
͵ͲǣͳͳͶͲȂͳͳͷ͵Ǥ
ϵϰϴ
ǡǯȋʹͲͲ͵Ȍ
Ǥ
ǣͶͳͷȂͶʹ͵Ǥ
ϵϱϲ
ǡ ȋʹͲͳʹȌ
ǤʹʹǣʹͲͻͷȂʹͳͲ͵Ǥ
ǡ
ǡ ǡ ȋʹͲͳȌ
Ǥ ͳʹǣͳͲͲͶͻǤ
ϵϰϵ ϵϱϬ ϵϱϭ ϵϱϮ ϵϱϯ ϵϱϰ ϵϱϱ ϵϱϳ ϵϱϴ ϵϱϵ ϵϲϬ ϵϲϭ ϵϲϮ
ǡǡǡǡǡ ǡ ȋʹͲͳͲȌ Ǧ
Ǥ
͵ͲǣͻͳȂͻʹͻǤ ǡ
ǡ ǡ ȋʹͲͳͶȌ
ǣ Ǥ
ͳǣͶͶͲȂͶͶͺǤ ǦǡȋʹͲͳͶȌ
ǣ Ǥ
ͳͺǣʹͲ͵Ȃ ʹͳͲǤ ǡ ȋʹͲͳ͵Ȍ ǣ
ǡ
ǡ Ǥ
ͳǣͶͲͳȂͶͳʹǤ
ʹ
ϵϲϯ ϵϲϰ ϵϲϱ ϵϲϲ ϵϲϳ ϵϲϴ ϵϲϵ ϵϳϬ ϵϳϭ ϵϳϮ ϵϳϯ ϵϳϰ ϵϳϱ ϵϳϲ ϵϳϳ ϵϳϴ ϵϳϵ ϵϴϬ ϵϴϭ ϵϴϮ ϵϴϯ ϵϴϰ ϵϴϱ ϵϴϲ ϵϴϳ ϵϴϴ ϵϴϵ ϵϵϬ ϵϵϭ ϵϵϮ ϵϵϯ ϵϵϰ ϵϵϱ ϵϵϲ ϵϵϳ ϵϵϴ ϵϵϵ ϭϬϬϬ ϭϬϬϭ ϭϬϬϮ ϭϬϬϯ ϭϬϬϰ ϭϬϬϱ ϭϬϬϲ ϭϬϬϳ ϭϬϬϴ ϭϬϬϵ ϭϬϭϬ ϭϬϭϭ ϭϬϭϮ ϭϬϭϯ ϭϬϭϰ ϭϬϭϱ ϭϬϭϲ ϭϬϭϳ ϭϬϭϴ ϭϬϭϵ
ǡ ǡ ǡ ǡ ǡ ǡ ǡ ȋʹͲͲͺȌ
Ǥ ͲǣͳͳʹȂͳͳͶͳǤ
ϭϬϮϬ
ǡȋͳͻͳȌ
Ǥ
͵Ͷǣ͵͵Ȃ͵ͶǤ
ϭϬϮϱ
Ǧ
ǡ ǡ ǡ ȋʹͲͳͳȌ
Ǧ
Ǥ
ʹͶǣͳȂͻǤ
ϭϬϮϴ
ǡ ǡ ǡ
ǡ
ǡ ȋʹͲͳȌ ǤͲǣ ǣȀȀǤ
Ǥ
Ȁ
ȀͲͺͻʹ͵ͳͲͲͳͶͷ ͺȀ
ȏ
ͳͺǡʹͲͳȐǤ
ϭϬϯϮ
ǡǡȋʹͲͲȌ
ǯ
Ǥ
ʹǣʹͶͷȂʹͷǤ
ϭϬϯϳ
ǡȋʹͲͲȌ
Ǧ ǦǤ
ͳͶǣͳȂͳͻͲǤ ǡ ȋʹͲͲͷȌ
Ǥ ͶͺǣͳȂ͵Ǥ
ϭϬϮϭ ϭϬϮϮ ϭϬϮϯ ϭϬϮϰ ϭϬϮϲ ϭϬϮϳ ϭϬϮϵ ϭϬϯϬ ϭϬϯϭ ϭϬϯϯ ϭϬϯϰ ϭϬϯϱ ϭϬϯϲ ϭϬϯϴ ϭϬϯϵ ϭϬϰϬ ϭϬϰϭ ϭϬϰϮ ϭϬϰϯ ϭϬϰϰ ϭϬϰϱ ϭϬϰϲ ϭϬϰϳ ϭϬϰϴ
ǡ ǡ ǡǡ ȋʹͲͲͺȌ
Ǥ ͳͲͲǣͳͶͲȂͳͶͳͻǤ
ϭϬϰϵ
ǡ Ǧǡ ȋʹͲͳʹȌ
Ǥ
ͳͲͻǣͶͷͳȂͶͷǤ
ϭϬϱϯ
ǡ ǡ ȋʹͲͳȌ
Ǥ ͻ͵ǣ͵ʹ͵Ȃ ͵͵ͲǤ
ϭϬϱϳ
ǡǡȋʹͲͲͺȌ
Ǥ
͵ͳͻǣͳͷͶ͵ȂͳͷͶǤ
ϭϬϲϬ
ǡ
ǡ ǡ ǡ ǡ Ǧ ȋʹͲͳȌ
Ǥ
ǣʹͲͳͳͻͶͶͻǤ
ϭϬϲϮ
© ǡ ¡ ǡ ǡ ȋʹͲͲͻȌ Ǥ ǣͳͲͲͲʹͲǤ
ϭϬϲϳ
ǡ ǡ ǡ
ǡ ǡ ǡ ǡ ǡ ǡ ǡ ǡ ǡ ǡ
ǡǡ
2 ȋʹͲͳͳȌ
Ǧǣ
Ǥ
ͳʹǣʹͺʹͷΫʹͺ͵ͲǤ
ϭϬϳϭ
ϭϬϱϬ ϭϬϱϭ ϭϬϱϮ ϭϬϱϰ ϭϬϱϱ ϭϬϱϲ ϭϬϱϴ ϭϬϱϵ ϭϬϲϭ ϭϬϲϯ ϭϬϲϰ ϭϬϲϱ ϭϬϲϲ ϭϬϲϴ ϭϬϲϵ ϭϬϳϬ ϭϬϳϮ ϭϬϳϯ ϭϬϳϰ ϭϬϳϱ
ǡ ǡ ȋͳͻͻͻȌ
Ǥ
ͳͻǣͷͶͻ͵ȂͷͷͲͷǤ ǡȋʹͲͳʹȌ
Ǥ
͵ʹǣͳʹͻͻͲȂͳʹͻͻͺǤ ǡ ǡ ǡ Ǧ ǡ ǡ ǡ ȋʹͲͳ͵Ȍ
Ǥ ͶͻǣͷͺͷȂͷͻͲǤ ǡ ȋʹͲͳȌ
Ǥ
ǣͳͺͳǤ ǡ Ǧǡ ȋʹͲͳȌ
Ȃ
Ǥ ǣͳȂͳͷǤ ǡ ǡ ǡ ȋͳͻͻͻȌ
Ǥ͵ͻͻǣͶͲȂ Ͷ͵Ǥ ǡ
ǡ ǡ ǡ ǡǡȋʹͲͳȌ
Ǥ
͵ͷͶǣͳͳ͵Ȃͳͳ͵ͻǤ ǡǡ ǡǡ ǡ ȋʹͲͲȌ
Ǥ
ͳͶǣͳͲͺʹȂͳͳͲͺǤ ǡǡǦ ǡ ǡ
ȋʹͲͲͺȌ
Ǥ
ͳͲͷǣͳͳͻͻȂͳͳͻͶǤ ǡ ǡ ǯ ȋʹͲͳͶȌ
Ǥ
ͳͺǣͺʹȂ ͺͻǤ ȋʹͲͳͷȌ Dz
Ǧdz
ǣ
Ǥ
ͳͻǣ͵ͻͶȂͶͲͷǤ ǡ ǡ ǡ ǡ ǡ
ȋʹͲͳ͵Ȍ
Ǥͺǣ͵ͶȂ͵ͷǤ Ǧ ǡ ǡ ǡ
ǡ
ȋʹͲͲͺȌǦ
ǤͶǣͳͲͲͲͲ͵Ǥ ǡ ȋʹͲͲͶȌ
Ǥ ͳͶǣͳ͵ʹͺȂ ͳ͵͵ͻǤ ǡ
ȋʹͲͲͶȌ Ǧ
Ǧ Ǧ
Ǥ
ͳͻǣͶͶȂͶͷǤ
ʹ
ϭϬϳϲ ϭϬϳϳ ϭϬϳϴ ϭϬϳϵ ϭϬϴϬ ϭϬϴϭ ϭϬϴϮ ϭϬϴϯ ϭϬϴϰ ϭϬϴϱ ϭϬϴϲ ϭϬϴϳ ϭϬϴϴ ϭϬϴϵ ϭϬϵϬ ϭϬϵϭ
ǡ ǡ ȋʹͲͳͳȌ ǣ
Ǥ
ͳ͵ǣʹʹȂ͵ͲǤ
ϭϬϵϮ
ǡ ǡ Ǧ ǡ ȋʹͲͳȌ
ǤͺͻǣͷͶȂʹǤ
ϭϬϵϱ ϭϬϵϲ
Ǧ ȋʹͲͲͳȌ
Ǥ
ʹͶǣͶͷͷȂͶ͵Ǥ
ϭϬϵϵ
ǡ ȋʹͲͲȌ Ǧ
Ǧ
Ǥ ͳǣͺͺȂͳͲͲǤ
ϭϭϬϮ
ϭϭϬϱ
ǡ ǡ ȋʹͲͲȌ
Ǧǡ Ǧǡ Ǧ
Ǧ
Ǥ ͳͻǣͳʹͲ͵ȂͳʹʹʹǤ ǡ ǡǡ ȋʹͲͳͷȌ
Ǥ
ǣͳʹ͵Ǥ
ǡ ȋʹͲͳͶȌ
Ǧ
ϭϭϬϲ
ȋʹͲͳ͵Ȍ
ǡͷǤǤ
ϭϭϬϴ
ϭϬϵϯ ϭϬϵϰ
ϭϬϵϳ ϭϬϵϴ ϭϭϬϬ ϭϭϬϭ ϭϭϬϯ ϭϭϬϰ
Ǥ
ͳǣͲͳȂͳͳǤ ǡ ȋʹͲͳͷȌǦ
Ǥ
ʹͶǣͳȂ ͳʹǤ
ϭϭϬϳ
ʹͺ
a
Cue (0.5 s)
Delay (3 s / variable)
b
Response
c
Both experiments Experiment 2 only 4
0.8
Experiment 1 3
2
1
0
Experiment 2
0.6 Discriminability
Mean firing rate (versus baseline; sp/s)
d
Fixation (1 s)
0.4
0.2
0.0 Cue
0
Cue
Saccade
1
2
3
0
4
Time (s)
Saccade
1
−1
0
Experiment 1 4
Experiment 2 Significant generalization
Saccade
Saccade
3
0
2
−1 −1
0 0
1
2
3
0 0.63 Discriminability
Cue
Saccade
0
4
1
Significant dynamics
Saccade
Time (s)
Cue
Cue
Cue
0
4
0
1
1
Saccade Saccade
Independent split B
Saccade
3
0
2
−1 −1
0
−1
0
1 Cue
1
0
0 Cue
0
Cue
a
Cue
Saccade
1
2
3
4
0
1
Time (s)
Independent split A
Dynamicism index
b 1
0
Cue
0
Cue
Saccade
1
2
3
4 Time (s)
0
Saccade
1
a
0.2–0.3 s
1.0–4.0 s
20
b
10
Firing rate (sp/s)
30 Cue
Saccade
Saccade
Neu rons sorted by peak selectiv ity
Exper iment 1
40
Cue
320
0.9–1.5 s
Example ce ll Exper iment 2
Example ce ll Exper iment 1
0.5–0.8 s
0 0
c
2
3
Cue
0
Saccade
1
2
3
Cue
Saccade
Neu rons sorted by peak selectiv ity
105
Exper iment 2
1
0 0
1
−1
0
Time (s)
0
1
−1 Time (s)
90
P referred orientation 1 8 0 (deg)
+90 0
270
0
P referred orie ntation difference f rom peak ±1 8 0 selectivity (d eg)
0 -90
Off- versus on-diagonal discriminability
0.00
−0.05
−0.10
Fully static code Switching selectivity No switching selectivity
−0.15
Experiment 1 Experiment 2 −0.20 0.0
0.1
0.2
0.3 Time separation (s)
0.4
0.5
0.6
Saccade Saccade
a
b
Condition
44
1–1.4 s
26
−1 −1
0
1
0
0
Cue
0
1
Time (s), Independent split A
c
d
0.9
Geometry correlation Cue
Distance (sp/s)
0.05–0.3 s
Time (s), Independent split B
50
94
Distance (sp/s)
Condition
0
DS
M
DS
M
a
Attention task Dual task period Working memory task
Analysis window
c Discriminability 0.63
tio
I
Att
Sorted cells
Cue
n
0
tio
Cue
ac
en
nt er
1
0
Experiment 3 (dual task)
WM
n
0
Time (s), Independent split B
d
Cue
70
Cumulative proportion of cells changing selectivity (%)
b
Cue
50
Experiment 2
25
Experiment 1 0 0
1
Time (s), Independent split A
1
Time (s) 0 0
1
Time (s)