Anthony N Burkitt

David B Grayden

Doreen A Thomas

GQ Bi

H Markram

H Meffin

J Leo van Hemmen

Matthieu Gilson

R Gütig

R Kempter

S Song

W Gerstner

English

PubMed

Springer - Publisher Connector

POSTER PRESENTATION Open Access
The representation of input correlation structure
from multiple pools in the synaptic weights
by STDP
Matthieu Gilson1,2,3, Anthony N Burkitt1,2*, David B Grayden1,2, Doreen A Thomas1,3, J Leo van Hemmen4
From Nineteenth Annual Computational Neuroscience Meeting: CNS*2010
San Antonio, TX, USA. 24-30 July 2010
Spike-timing-dependent plasticity (STDP) has been
shown to generate a synaptic weight structure by the
strengthening (potentiation) or weakening (depression)
of synapses depending on the timing of the pre- and
post-synaptic spikes at the synapse at the scale of milli-
seconds [1,2]. Mathematical studies of STDP have
shown how it generates competition between individual
weights that can lead to effective neuronal specialization
relying on spike-time correlations [3,4]. In addition, the
change in the synaptic weight induced by STDP is
affected by its current value, which modulates the
resulting competition [5]. It was shown for the case of a
single neuron stimulated by two identical input pools
that the asymptotic weight distribution can be either
unimodal or bimodal, depending on the strength of the
weight dependence for STDP [6]; the transition (bifurca-
tion) between these two classes of STDP also depends
upon the input correlation level. Stronger input correla-
tions and almost-additive STDP lead to effective weight
specialization, namely an asymptotic bimodal distribu-
tion. For the case of several pools with identical proper-
ties, the weight dependence can also lead to the
selection of a subset of the pools [7].
In this paper it is shown under what conditions a neu-
ron stimulated by several pools that have within-pool
(but no between-pool) correlations with distinct
strengths encodes this input structure in its resulting
weight structure. The analysis is carried out using the
Poisson neuron model [3] with pair-wise weight-depen-
dent STDP [6]. The learning dynamics induced by
STDP leads to both stabilization of the input weights
and competition between the weights for a broad range
of learning parameters. While weak weight dependence
is synonymous with strong competition between indivi-
dual weights, there is a qualitative difference between
the weight dynamics generated by additive and almost-
additive STDP. For additive STDP, only the bounds of
the weights are stable, whereas intermediate values can
be stable even for weakly weight-dependent STDP. In
the latter case, a stable multimodal asymptotic distribu-
tion can emerge, where the weights are sorted in
increasing order with respect to the correlation strength
of the corresponding pool they come from. When the
strength of weight dependence is increased, the spread-
ing of the asymptotic distribution diminishes, eventually
becoming unimodal.
The results demonstrate how weakly weight-depen-
dent STDP can generate a multimodal stable asymptotic
distribution of the synaptic weights, providing a further
step towards a better understanding of how STDP can
represent the input (spike-time correlation) structure in
the resulting weight structure.
Acknowledgements
This work was funded partly by NICTA, The Bionic Ear Institute, the
Australian Research Council (ARC Discovery Projects #DP0771815 and
#DP1096699) and BCCN-Munich.
Author details
1Department of Electrical and Electronic Engineering, University of
Melbourne, Melbourne, VIC 3010, Australia. 2The Bionic Ear Institute, East
Melbourne, VIC 3002, Australia. 3NICTA, Victoria Research Lab, University of
Melbourne, Melbourne, VIC 3010, Australia. 4Physik Department (T35) and
BCCN-Munich, Universität München, 85747 Garching bei München, Germany.
Published: 20 July 2010
References
1. Gerstner W, Kempter R, van Hemmen JL, Wagner H: A neuronal learning
rule for sub-millisecond temporal coding. Nature 1996, 383(6595):76-78.
* Correspondence: aburkitt@unimelb.edu.au
1Department of Electrical and Electronic Engineering, University of
Melbourne, Melbourne, VIC 3010, Australia
Gilson et al. BMC Neuroscience 2010, 11(Suppl 1):P191
http://www.biomedcentral.com/1471-2202/11/S1/P191
© 2010 Burkitt et al; licensee BioMed Central Ltd.
2. Markram H, Lübke J, Frotscher M, Roth A, Sakmann B: Physiology and
anatomy of synaptic connections between thick tufted pyramidal
neurones in the developing rat neocortex. J Physiol 1997, 500:409-440.
3. Kempter R, Gerstner W, van Hemmen JL: Hebbian learning and spiking
neurons. Phys Rev E 1999, 59(4):4498-4514.
4. Song S, Miller KD, Abbott LF: Competitive Hebbian learning through
spike-timing-dependent synaptic plasticity. Nature Neurosci 2000,
3:919-926.
5. Bi GQ, Poo MM: Synaptic modifications in cultured hippocampal
neurons: Dependence on spike timing, synaptic strength, and
postsynaptic cell type. J Neurosci 1998, 18:10464-10472.
6. Gütig R, Aharonov R, Rotter S, Sompolinsky H: Learning input correlations
through nonlinear temporally asymmetric Hebbian plasticity. J Neurosci
2003, 23(9):3697-3714.
7. Meffin H, Besson J, Burkitt AN, Grayden DB: Learning the structure of
correlated synaptic subgroups using stable and competitive spike-
timing-dependent plasticity. Phys Rev E 2006, 73:041911.
doi:10.1186/1471-2202-11-S1-P191
Cite this article as: Gilson et al.: The representation of input correlation
structure from multiple pools in the synaptic weights by STDP. BMC
Neuroscience 2010 11(Suppl 1):P191.
Submit your next manuscript to BioMed Central
and take full advantage of: 
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at 
www.biomedcentral.com/submit
Gilson et al. BMC Neuroscience 2010, 11(Suppl 1):P191
http://www.biomedcentral.com/1471-2202/11/S1/P191
Page 2 of 2


The representation of input correlation structure from multiple pools in the synaptic weights by STDP

Grayden David B

Burkitt Anthony N

Gilson Matthieu

Thomas Doreen A

van Hemmen J Leo

Directory of Open Access Journals

BMC Neuroscience

Crossref

aburkitt@unimelb.edu.au 1Department of Electrical and Electronic Engineering, University of Melbourne,

Grayden DB: Learning the structure of correlated synaptic subgroups using stable and competitive spiketiming-dependent plasticity. Phys Rev E

H: A neuronal learning rule for sub-millisecond temporal coding. Nature

Hebbian learning and spiking neurons. Phys Rev E

LF: Competitive Hebbian learning through spike-timing-dependent synaptic plasticity.

Poo MM: Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type.

Sompolinsky H: Learning input correlations through nonlinear temporally asymmetric Hebbian plasticity.

Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color ﬁgure charges • Immediate publication on acceptance

file:///data/core-remote/dit/data/Springer-OA/pdf/cf7/aHR0cDovL2xpbmsuc3ByaW5nZXIuY29tLzEwLjExODYvMTQ3MS0yMjAyLTExLVMxLVAxOTEucGRm.pdf

The representation of input correlation structure from multiple pools in the synaptic weights by STDP

Abstract

Similar works

Full text

Available Versions

Springer - Publisher Connector

Directory of Open Access Journals

Crossref