- Poster presentation
- Open Access
On the influence of inhibitory STDP on balanced state random networks
© Effenberger et al; licensee BioMed Central Ltd. 2013
- Published: 8 July 2013
- Network Dynamic
- Network Activity
- Balance State
- Synaptic Weight
- Inhibitory Synapse
The distribution of synaptic efficacies in neural networks takes fundamental influence on their dynamics and the modification of synaptic strengths forms the foundation of learning and memory. A prominent plasticity rule that has been observed in vitro is spike-timing-dependent plasticity (STDP). While first studied in glutamatergic synapses, recently also STDP of GABAergic synapses came into the focus of experimental and theoretical research .
We study random balanced state networks of leaky integrate-and-fire neurons in the asynchronous irregular (AI) regime  that is believed to be a good theoretical fit to the activity of cortical networks in vivo. We consider driven networks that receive Poisson input as well as networks in a self-sustained state of activity. In order to assess the influence of excitatory and inhibitory STDP on the network dynamics, we introduce these two plasticity rules independently, observing network dynamics and weight distributions after a transient phase. Note that both additive and multiplicative STDP rules yield the same network dynamics as described below.
The second author acknowledges support by the Federal Ministry of Education and Research (BMBF) Germany under grant number 01GQ1005B.
- Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W: Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks. Science. 2011, 334: 1569-73. 10.1126/science.1211095. (New York, NY)View ArticlePubMedGoogle Scholar
- Brunel N: Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of computational neuroscience. 2000, 8: 183-208. 10.1023/A:1008925309027.View ArticlePubMedGoogle Scholar
- Morrison A, Aertsen A, Diesmann M: Spike-timing-dependent plasticity in balanced random networks. Neural computation. 2007, 19: 1437-67. 10.1162/neco.2007.19.6.1437.View ArticlePubMedGoogle Scholar
- Litwin-Kumar A, Doiron B: Slow dynamics and high variability in balanced cortical networks with clustered connections. Nature Neuroscience. 2012, 15: 1498-1505. 10.1038/nn.3220.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to 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.