Anthony N Burkitt

David B Grayden

Doreen A Thomas

Robert R Kerr

PubMed

Springer - Publisher Connector

POSTER PRESENTATION Open Access
STDP encodes oscillation frequencies in the
connections of recurrent networks of spiking
neurons
Robert R Kerr1,2*, Anthony N Burkitt1,2,4, Doreen A Thomas3, David B Grayden1,2,4
From Twenty First Annual Computational Neuroscience Meeting: CNS*2012
Decatur, GA, USA. 21-26 July 2012
Spike-timing-dependent plasticity (STDP) is a learning
rule that updates synaptic strengths based on the relative
timing of pre- and post-synaptic spikes. Unlike rate-
based Hebbian learning, STDP can potentially encode
fast temporal correlations in neuronal activity, such as
oscillations, in the functional structure of networks of
neurons that have axonal and dendritic propagation
delays. The motivation behind this study was to under-
stand the different ways that spatiotemporal patterns can
be learnt by the recurrent connections in a network of
neurons with STDP present. This understanding is vital
to uncovering the mechanisms by which basic learning
and information processing tasks are performed through-
out the brain. A specific example in which these mechan-
isms may contribute is in explaining how the brain can
perceive the pitch of complex sounds up to 300Hz. This
work employs and builds upon the analytical framework
for learning with STDP used in a previous study [1].
In this study, the changes made by additive STDP to
synaptic strengths in recurrent networks with axonal
* Correspondence: r.kerr@pgrad.unimelb.edu.au
1NeuroEngineering Lab, Dept. Electrical & Electronic Engineering, University
of Melbourne, VIC 3010, Australia
Full list of author information is available at the end of the article
Figure 1 Results of simulations with networks of 10,000 LIF neurons. A: The mean synaptic weight with axonal delay of a network before
learning (dashed: all weights initially set to 0.0025 with an even spread of delays over 1-10ms) and after learning for 20,000s with STDP (blue:
‘medium’ dynamics and 120Hz inputs; red: ‘medium’ dynamics and 240Hz inputs; and green: ‘fast’ dynamics and 240Hz inputs). B: The frequency-
response plot with the average maximum of the oscillatory response of the networks in A. Note: ‘medium/fast’ dynamics correspond to 10ms/
5ms membrane time constant, 0.5ms/0.1ms synaptic rise time, and 1ms/0.5ms synaptic decay time. These are realistic for neurons/synapses
found in the auditory brainstem.
Kerr et al. BMC Neuroscience 2012, 13(Suppl 1):P130
http://www.biomedcentral.com/1471-2202/13/S1/P130
© 2012 Kerr et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
delays receiving oscillatory inputs were investigated analy-
tically with the Poisson neuron model and verified through
simulations with leaky integrate-and-fire (LIF) neurons.
Frequencies between 100-300Hz were considered, which
correspond to the modulation frequencies found in the
auditory brainstem representing the fundamental fre-
quency of different natural sounds. The analysis and simu-
lations found that connections were selectively potentiated
and depressed based on their axonal delay in such a way
that the delays of the strong connections in the network
“resonated” with the input frequency (Figure 1A). The
trained networks were found to respond selectively to the
frequency they were trained with (Figure 1B). Higher fre-
quencies (e.g. 240Hz as shown here) would always be
learnt by the network, but in order to show a selective
response after learning they needed faster neuronal and
synaptic time constants (see details in Figure 1 caption).
Acknowledgements
Funding is acknowledged from the Australian Research Council (ARC
Discovery Project #DP1096699). The Bionics Institute acknowledges the
support it receives from the Victorian Government through its Operational
Infrastructure Support Program.
Author details
1NeuroEngineering Lab, Dept. Electrical & Electronic Engineering, University
of Melbourne, VIC 3010, Australia. 2Centre for Neural Engineering, University
of Melbourne, VIC 3010, Australia. 3Dept. of Mechanical Engineering,
University of Melbourne, VIC 3010, Australia. 4Bionics Institute, 384 Albert St.,
East Melbourne, VIC 3002, Australia.
Published: 16 July 2012
Reference
1. Gilson M, Burkitt AN, Grayden DB, Thomas DA, van Hemmen JL:
Emergence of network structure due to spike-timing-dependent
plasticity in recurrent neuronal networks IV: Structuring synaptic
pathways among recurrent connections. Biological Cybernetics 2009,
101:427-444.
doi:10.1186/1471-2202-13-S1-P130
Cite this article as: Kerr et al.: STDP encodes oscillation frequencies in
the connections of recurrent networks of spiking neurons. BMC
Neuroscience 2012 13(Suppl 1):P130.
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
Kerr et al. BMC Neuroscience 2012, 13(Suppl 1):P130
http://www.biomedcentral.com/1471-2202/13/S1/P130
Page 2 of 2


STDP encodes oscillation frequencies in the connections of recurrent networks of spiking neurons

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks IV: Structuring synaptic pathways among recurrent connections.

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

http://dx.doi.org/10.1186/1471-2202-13-S1-P130

STDP encodes oscillation frequencies in the connections of recurrent networks of spiking neurons

Abstract

Similar works

Full text

Available Versions

Springer - Publisher Connector