Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons

Ian H. Stevenson, Brian M. London, Emily R. Oby, Nicholas A. Sachs, Jacob Reimer, Bernhard Englitz, Stephen David, Shihab A. Shamma, Timothy J. Blanche, Kenji Mizuseki, Amin Zandvakili, Nicholas G. Hatsopoulos, Lee E. Miller, Konrad P. Kording

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

How interactions between neurons relate to tuned neural responses is a longstanding question in systems neuroscience. Here we use statistical modeling and simultaneous multi-electrode recordings to explore the relationship between these interactions and tuning curves in six different brain areas. We find that, in most cases, functional interactions between neurons provide an explanation of spiking that complements and, in some cases, surpasses the influence of canonical tuning curves. Modeling functional interactions improves both encoding and decoding accuracy by accounting for noise correlations and features of the external world that tuning curves fail to capture. In cortex, modeling coupling alone allows spikes to be predicted more accurately than tuning curve models based on external variables. These results suggest that statistical models of functional interactions between even relatively small numbers of neurons may provide a useful framework for examining neural coding.

Original languageEnglish (US)
Article numbere1002775
JournalPLoS Computational Biology
Volume8
Issue number11
DOIs
StatePublished - Nov 2012

Fingerprint

Neurons
connectivity
Neuron
Tuning
Connectivity
neurons
Curve
Interaction
Population
modeling
Statistical Models
Neurosciences
neurophysiology
Noise
brain
Electrodes
electrode
statistical models
Neuroscience
electrodes

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Ecology
  • Molecular Biology
  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Computational Theory and Mathematics

Cite this

Stevenson, I. H., London, B. M., Oby, E. R., Sachs, N. A., Reimer, J., Englitz, B., ... Kording, K. P. (2012). Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons. PLoS Computational Biology, 8(11), [e1002775]. https://doi.org/10.1371/journal.pcbi.1002775

Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons. / Stevenson, Ian H.; London, Brian M.; Oby, Emily R.; Sachs, Nicholas A.; Reimer, Jacob; Englitz, Bernhard; David, Stephen; Shamma, Shihab A.; Blanche, Timothy J.; Mizuseki, Kenji; Zandvakili, Amin; Hatsopoulos, Nicholas G.; Miller, Lee E.; Kording, Konrad P.

In: PLoS Computational Biology, Vol. 8, No. 11, e1002775, 11.2012.

Research output: Contribution to journalArticle

Stevenson, IH, London, BM, Oby, ER, Sachs, NA, Reimer, J, Englitz, B, David, S, Shamma, SA, Blanche, TJ, Mizuseki, K, Zandvakili, A, Hatsopoulos, NG, Miller, LE & Kording, KP 2012, 'Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons', PLoS Computational Biology, vol. 8, no. 11, e1002775. https://doi.org/10.1371/journal.pcbi.1002775
Stevenson, Ian H. ; London, Brian M. ; Oby, Emily R. ; Sachs, Nicholas A. ; Reimer, Jacob ; Englitz, Bernhard ; David, Stephen ; Shamma, Shihab A. ; Blanche, Timothy J. ; Mizuseki, Kenji ; Zandvakili, Amin ; Hatsopoulos, Nicholas G. ; Miller, Lee E. ; Kording, Konrad P. / Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons. In: PLoS Computational Biology. 2012 ; Vol. 8, No. 11.
@article{db6b9833965442d5af21291097ef2e26,
title = "Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons",
abstract = "How interactions between neurons relate to tuned neural responses is a longstanding question in systems neuroscience. Here we use statistical modeling and simultaneous multi-electrode recordings to explore the relationship between these interactions and tuning curves in six different brain areas. We find that, in most cases, functional interactions between neurons provide an explanation of spiking that complements and, in some cases, surpasses the influence of canonical tuning curves. Modeling functional interactions improves both encoding and decoding accuracy by accounting for noise correlations and features of the external world that tuning curves fail to capture. In cortex, modeling coupling alone allows spikes to be predicted more accurately than tuning curve models based on external variables. These results suggest that statistical models of functional interactions between even relatively small numbers of neurons may provide a useful framework for examining neural coding.",
author = "Stevenson, {Ian H.} and London, {Brian M.} and Oby, {Emily R.} and Sachs, {Nicholas A.} and Jacob Reimer and Bernhard Englitz and Stephen David and Shamma, {Shihab A.} and Blanche, {Timothy J.} and Kenji Mizuseki and Amin Zandvakili and Hatsopoulos, {Nicholas G.} and Miller, {Lee E.} and Kording, {Konrad P.}",
year = "2012",
month = "11",
doi = "10.1371/journal.pcbi.1002775",
language = "English (US)",
volume = "8",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "11",

}

TY - JOUR

T1 - Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons

AU - Stevenson, Ian H.

AU - London, Brian M.

AU - Oby, Emily R.

AU - Sachs, Nicholas A.

AU - Reimer, Jacob

AU - Englitz, Bernhard

AU - David, Stephen

AU - Shamma, Shihab A.

AU - Blanche, Timothy J.

AU - Mizuseki, Kenji

AU - Zandvakili, Amin

AU - Hatsopoulos, Nicholas G.

AU - Miller, Lee E.

AU - Kording, Konrad P.

PY - 2012/11

Y1 - 2012/11

N2 - How interactions between neurons relate to tuned neural responses is a longstanding question in systems neuroscience. Here we use statistical modeling and simultaneous multi-electrode recordings to explore the relationship between these interactions and tuning curves in six different brain areas. We find that, in most cases, functional interactions between neurons provide an explanation of spiking that complements and, in some cases, surpasses the influence of canonical tuning curves. Modeling functional interactions improves both encoding and decoding accuracy by accounting for noise correlations and features of the external world that tuning curves fail to capture. In cortex, modeling coupling alone allows spikes to be predicted more accurately than tuning curve models based on external variables. These results suggest that statistical models of functional interactions between even relatively small numbers of neurons may provide a useful framework for examining neural coding.

AB - How interactions between neurons relate to tuned neural responses is a longstanding question in systems neuroscience. Here we use statistical modeling and simultaneous multi-electrode recordings to explore the relationship between these interactions and tuning curves in six different brain areas. We find that, in most cases, functional interactions between neurons provide an explanation of spiking that complements and, in some cases, surpasses the influence of canonical tuning curves. Modeling functional interactions improves both encoding and decoding accuracy by accounting for noise correlations and features of the external world that tuning curves fail to capture. In cortex, modeling coupling alone allows spikes to be predicted more accurately than tuning curve models based on external variables. These results suggest that statistical models of functional interactions between even relatively small numbers of neurons may provide a useful framework for examining neural coding.

UR - http://www.scopus.com/inward/record.url?scp=84870666419&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84870666419&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1002775

DO - 10.1371/journal.pcbi.1002775

M3 - Article

VL - 8

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 11

M1 - e1002775

ER -