Hebbian plasticity combined with Homeostasis shows STDP-like behavior
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1
Georg-August University Göttingen, III. Physical Institute – Biophysics, Germany
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2
Max-Planck Institute for Dynamics and Self-Organization, MPI, Germany
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3
Bernstein Center for Computational Neuroscience, BCCN-G, Germany
Functional properties of neural networks are dependent on the network connectivity. Connections between neurons are formed by synapses and are modified by different biological mechanisms. In this study, we considered the following three mechanisms: Hebbian, Homeostatic, and Spike-Timing-Dependent (STDP) plasticity. In Hebbian plasticity (Dayan and Abbott, 2001) weight change depends on the activity rate of pre- and post-synaptic neuron and, thus, is a local learning rule, whereas in Homeostatic plasticity (Turrigiano and Nelson, 2004) weight change depends only on the rate of the post-synaptic neuron. This mechanism adjusts all synapses of the incoming inputs of the neuron and, therefore, is a global plasticity process. STDP, like Hebbian, is a local process, however, differently from Hebbian plasticity (which can only produce long-term potentiation; LTP) it does not depend on the rates of pre- and post-synaptic neuron, but on the timing of the neuronal activity, and can either lead to LTP or LTD (long-term depression, Bi and Poo, 1998). However, STDP can be reformulated in a rate description (Izhikevich and Desai, 2003).
In this study we are interested in the behavior of these three mechanisms with respect to the change of the dynamics when they interact with each other. To test this we analyzed single and two neuron systems analytically and numerically by looking at their fixed points in synapse development. We found out that these fixed points change their position and stability state (from stable to unstable or vice versa) dependent on the plasticity parameters. We show that Hebbian together with Homeostatic plasticity shows qualitatively the same behavior as the rate description of STDP. This means that STDP, which has both LTP and LTD, can be a mixture of Hebbian and Homeostatic plasticity.
In general, we show that dynamically different plasticity mechanisms change their behavior dramatically when they interact with each other.
Keywords:
computational neuroscience
Conference:
Bernstein Conference on Computational Neuroscience, Berlin, Germany, 27 Sep - 1 Oct, 2010.
Presentation Type:
Presentation
Topic:
Bernstein Conference on Computational Neuroscience
Citation:
Tetzlaff
C,
Kolodziejski
C and
Wörgötter
F
(2010). Hebbian plasticity combined with Homeostasis shows STDP-like behavior.
Front. Comput. Neurosci.
Conference Abstract:
Bernstein Conference on Computational Neuroscience.
doi: 10.3389/conf.fncom.2010.51.00034
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Received:
20 Sep 2010;
Published Online:
23 Sep 2010.
*
Correspondence:
Dr. Christian Tetzlaff, Georg-August University Göttingen, III. Physical Institute – Biophysics, Göttingen, Germany, christian.tetzlaff@med.uni-goettingen.de