Feedforward inhibition and synaptic scaling - Two sides of the same coin?

Christian Keck, Cristina Savin, Jörg Lücke

Research output: Contribution to journalArticle

Abstract

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.

Original languageEnglish (US)
Article numbere1002432
JournalPLoS Computational Biology
Volume8
Issue number3
DOIs
StatePublished - Mar 2012

Fingerprint

Feedforward
Scaling
Neurons
learning
Learning
Unsupervised learning
neurons
Neuron
Maximum likelihood
Process control
Plasticity
Subspace
Brain
process control
Adaptive Processes
Probabilistic Modeling
Neuronal Plasticity
Rule Learning
Unsupervised Learning
Process Control

ASJC Scopus subject areas

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

Cite this

Feedforward inhibition and synaptic scaling - Two sides of the same coin? / Keck, Christian; Savin, Cristina; Lücke, Jörg.

In: PLoS Computational Biology, Vol. 8, No. 3, e1002432, 03.2012.

Research output: Contribution to journalArticle

@article{e3d41625f3894a5bb0a4b7f95cb1f014,
title = "Feedforward inhibition and synaptic scaling - Two sides of the same coin?",
abstract = "Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.",
author = "Christian Keck and Cristina Savin and J{\"o}rg L{\"u}cke",
year = "2012",
month = "3",
doi = "10.1371/journal.pcbi.1002432",
language = "English (US)",
volume = "8",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "3",

}

TY - JOUR

T1 - Feedforward inhibition and synaptic scaling - Two sides of the same coin?

AU - Keck, Christian

AU - Savin, Cristina

AU - Lücke, Jörg

PY - 2012/3

Y1 - 2012/3

N2 - Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.

AB - Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.

UR - http://www.scopus.com/inward/record.url?scp=84861110370&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84861110370&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1002432

DO - 10.1371/journal.pcbi.1002432

M3 - Article

VL - 8

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 3

M1 - e1002432

ER -