Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

John D. Murray, Alberto Bernacchia, Nicholas A. Roy, Christos Constantinidis, Ranulfo Romo, Xiao-Jing Wang

Research output: Contribution to journalArticle

Abstract

Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain's WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.

Original languageEnglish (US)
Pages (from-to)394-399
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume114
Issue number2
DOIs
StatePublished - Jan 10 2017

Fingerprint

Population Dynamics
Prefrontal Cortex
Short-Term Memory
Population
Neurons
Maintenance
Cognition
Primates
Haplorhini
Cues
Linear Models
Brain

Keywords

  • Population coding
  • Prefrontal cortex
  • Working memory

ASJC Scopus subject areas

  • General

Cite this

Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex. / Murray, John D.; Bernacchia, Alberto; Roy, Nicholas A.; Constantinidis, Christos; Romo, Ranulfo; Wang, Xiao-Jing.

In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 114, No. 2, 10.01.2017, p. 394-399.

Research output: Contribution to journalArticle

Murray, John D. ; Bernacchia, Alberto ; Roy, Nicholas A. ; Constantinidis, Christos ; Romo, Ranulfo ; Wang, Xiao-Jing. / Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex. In: Proceedings of the National Academy of Sciences of the United States of America. 2017 ; Vol. 114, No. 2. pp. 394-399.
@article{3da38a0788824c5f9d67daeaf21049e4,
title = "Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex",
abstract = "Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain's WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.",
keywords = "Population coding, Prefrontal cortex, Working memory",
author = "Murray, {John D.} and Alberto Bernacchia and Roy, {Nicholas A.} and Christos Constantinidis and Ranulfo Romo and Xiao-Jing Wang",
year = "2017",
month = "1",
day = "10",
doi = "10.1073/pnas.1619449114",
language = "English (US)",
volume = "114",
pages = "394--399",
journal = "Proceedings of the National Academy of Sciences of the United States of America",
issn = "0027-8424",
number = "2",

}

TY - JOUR

T1 - Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

AU - Murray, John D.

AU - Bernacchia, Alberto

AU - Roy, Nicholas A.

AU - Constantinidis, Christos

AU - Romo, Ranulfo

AU - Wang, Xiao-Jing

PY - 2017/1/10

Y1 - 2017/1/10

N2 - Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain's WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.

AB - Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain's WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.

KW - Population coding

KW - Prefrontal cortex

KW - Working memory

UR - http://www.scopus.com/inward/record.url?scp=85009401685&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85009401685&partnerID=8YFLogxK

U2 - 10.1073/pnas.1619449114

DO - 10.1073/pnas.1619449114

M3 - Article

C2 - 28028221

AN - SCOPUS:85009401685

VL - 114

SP - 394

EP - 399

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

SN - 0027-8424

IS - 2

ER -