Working memory cells' behavior may be explained by cross-regional networks with synaptic facilitation

Sergio Verduzco-Flores, Mark Bodner, Bard Ermentrout, Joaquin M. Fuster, Yongdi Zhou

Research output: Contribution to journalArticle

Abstract

Neurons in the cortex exhibit a number of patterns that correlate with working memory. Specifically, averaged across trials of working memory tasks, neurons exhibit different firing rate patterns during the delay of those tasks. These patterns include: 1) persistent fixed-frequency elevated rates above baseline, 2) elevated rates that decay throughout the tasks memory period, 3) rates that accelerate throughout the delay, and 4) patterns of inhibited firing (below baseline) analogous to each of the preceding excitatory patterns. Persistent elevated rate patterns are believed to be the neural correlate of working memory retention and preparation for execution of behavioral/motor responses as required in working memory tasks. Models have proposed that such activity corresponds to stable attractors in cortical neural networks with fixed synaptic weights. However, the variability in patterned behavior and the firing statistics of real neurons across the entire range of those behaviors across and within trials of working memory tasks are typical not reproduced. Here we examine the effect of dynamic synapses and network architectures with multiple cortical areas on the states and dynamics of working memory networks. The analysis indicates that the multiple pattern types exhibited by cells in working memory networks are inherent in networks with dynamic synapses, and that the variability and firing statistics in such networks with distributed architectures agree with that observed in the cortex.

Original languageEnglish (US)
Article numbere6399
JournalPLoS One
Volume4
Issue number8
DOIs
StatePublished - Aug 4 2009

Fingerprint

Short-Term Memory
Data storage equipment
cells
Neurons
neurons
Synapses
synapse
cortex
statistics
Statistics
Network architecture
neural networks
deterioration
Weights and Measures
Neural networks

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Verduzco-Flores, S., Bodner, M., Ermentrout, B., Fuster, J. M., & Zhou, Y. (2009). Working memory cells' behavior may be explained by cross-regional networks with synaptic facilitation. PLoS One, 4(8), [e6399]. https://doi.org/10.1371/journal.pone.0006399

Working memory cells' behavior may be explained by cross-regional networks with synaptic facilitation. / Verduzco-Flores, Sergio; Bodner, Mark; Ermentrout, Bard; Fuster, Joaquin M.; Zhou, Yongdi.

In: PLoS One, Vol. 4, No. 8, e6399, 04.08.2009.

Research output: Contribution to journalArticle

Verduzco-Flores, Sergio ; Bodner, Mark ; Ermentrout, Bard ; Fuster, Joaquin M. ; Zhou, Yongdi. / Working memory cells' behavior may be explained by cross-regional networks with synaptic facilitation. In: PLoS One. 2009 ; Vol. 4, No. 8.
@article{2e299f5ddecd48f69853360fd3799035,
title = "Working memory cells' behavior may be explained by cross-regional networks with synaptic facilitation",
abstract = "Neurons in the cortex exhibit a number of patterns that correlate with working memory. Specifically, averaged across trials of working memory tasks, neurons exhibit different firing rate patterns during the delay of those tasks. These patterns include: 1) persistent fixed-frequency elevated rates above baseline, 2) elevated rates that decay throughout the tasks memory period, 3) rates that accelerate throughout the delay, and 4) patterns of inhibited firing (below baseline) analogous to each of the preceding excitatory patterns. Persistent elevated rate patterns are believed to be the neural correlate of working memory retention and preparation for execution of behavioral/motor responses as required in working memory tasks. Models have proposed that such activity corresponds to stable attractors in cortical neural networks with fixed synaptic weights. However, the variability in patterned behavior and the firing statistics of real neurons across the entire range of those behaviors across and within trials of working memory tasks are typical not reproduced. Here we examine the effect of dynamic synapses and network architectures with multiple cortical areas on the states and dynamics of working memory networks. The analysis indicates that the multiple pattern types exhibited by cells in working memory networks are inherent in networks with dynamic synapses, and that the variability and firing statistics in such networks with distributed architectures agree with that observed in the cortex.",
author = "Sergio Verduzco-Flores and Mark Bodner and Bard Ermentrout and Fuster, {Joaquin M.} and Yongdi Zhou",
year = "2009",
month = "8",
day = "4",
doi = "10.1371/journal.pone.0006399",
language = "English (US)",
volume = "4",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "8",

}

TY - JOUR

T1 - Working memory cells' behavior may be explained by cross-regional networks with synaptic facilitation

AU - Verduzco-Flores, Sergio

AU - Bodner, Mark

AU - Ermentrout, Bard

AU - Fuster, Joaquin M.

AU - Zhou, Yongdi

PY - 2009/8/4

Y1 - 2009/8/4

N2 - Neurons in the cortex exhibit a number of patterns that correlate with working memory. Specifically, averaged across trials of working memory tasks, neurons exhibit different firing rate patterns during the delay of those tasks. These patterns include: 1) persistent fixed-frequency elevated rates above baseline, 2) elevated rates that decay throughout the tasks memory period, 3) rates that accelerate throughout the delay, and 4) patterns of inhibited firing (below baseline) analogous to each of the preceding excitatory patterns. Persistent elevated rate patterns are believed to be the neural correlate of working memory retention and preparation for execution of behavioral/motor responses as required in working memory tasks. Models have proposed that such activity corresponds to stable attractors in cortical neural networks with fixed synaptic weights. However, the variability in patterned behavior and the firing statistics of real neurons across the entire range of those behaviors across and within trials of working memory tasks are typical not reproduced. Here we examine the effect of dynamic synapses and network architectures with multiple cortical areas on the states and dynamics of working memory networks. The analysis indicates that the multiple pattern types exhibited by cells in working memory networks are inherent in networks with dynamic synapses, and that the variability and firing statistics in such networks with distributed architectures agree with that observed in the cortex.

AB - Neurons in the cortex exhibit a number of patterns that correlate with working memory. Specifically, averaged across trials of working memory tasks, neurons exhibit different firing rate patterns during the delay of those tasks. These patterns include: 1) persistent fixed-frequency elevated rates above baseline, 2) elevated rates that decay throughout the tasks memory period, 3) rates that accelerate throughout the delay, and 4) patterns of inhibited firing (below baseline) analogous to each of the preceding excitatory patterns. Persistent elevated rate patterns are believed to be the neural correlate of working memory retention and preparation for execution of behavioral/motor responses as required in working memory tasks. Models have proposed that such activity corresponds to stable attractors in cortical neural networks with fixed synaptic weights. However, the variability in patterned behavior and the firing statistics of real neurons across the entire range of those behaviors across and within trials of working memory tasks are typical not reproduced. Here we examine the effect of dynamic synapses and network architectures with multiple cortical areas on the states and dynamics of working memory networks. The analysis indicates that the multiple pattern types exhibited by cells in working memory networks are inherent in networks with dynamic synapses, and that the variability and firing statistics in such networks with distributed architectures agree with that observed in the cortex.

UR - http://www.scopus.com/inward/record.url?scp=68349154439&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=68349154439&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0006399

DO - 10.1371/journal.pone.0006399

M3 - Article

VL - 4

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 8

M1 - e6399

ER -