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On the influence of inhibitory STDP on balanced state random networks

BMC Neuroscience201314 (Suppl 1) :P200

https://doi.org/10.1186/1471-2202-14-S1-P200

  • Published:

Keywords

  • 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 [1].

We study random balanced state networks of leaky integrate-and-fire neurons in the asynchronous irregular (AI) regime [2] 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.

When introducing excitatory STDP alone, parameters involving the maximal weight have to be fine-tuned in order to keep the network activity stably in the AI regime [3]. For almost all parameter values the network activity becomes unstable, leaving the AI regime and settling in a pathological, highly synchronized state with saturated firing rates of most cells, see Figure 1A. We also observed that even without STDP, few strong excitatory connections can substantially destabilize network dynamics yielding pathological states. Interestingly, this destabilization does not happen when in addition to excitatory STDP we also introduce STDP for inhibitory synapses projecting onto excitatory cells. The latter setup results in a network that stably rests in the AI regime, see Figure 1A. Both STDP rules yield near-Gaussian distributions of synaptic weights, see Figure 1B. Inhibitory STDP even manages to stabilize a network that was brought to a pathological state by excitatory STDP, see Figure 1A. This clearly shows that inhibitory STDP has a stabilizing effect on network dynamics and we expect that especially in combination with synaptic scaling and in the context of clustered networks [4] other non-trivial dynamical effects will become visible.
Figure 1
Figure 1

A. Raster plot of 30 randomly sampled cells showing network activity. Red line: activation of excitatory STDP, green line: activation of inhibitory STDP. B. Weight distributions of plastic synapses converging onto 100 randomly sampled excitatory neurons. Red: excitatory synapses, green inhibitory synapses.

Declarations

Acknowledgements

The second author acknowledges support by the Federal Ministry of Education and Research (BMBF) Germany under grant number 01GQ1005B.

Authors’ Affiliations

(1)
Max-Planck-Institute for Mathematics in the Sciences, Leipzig, 04103, Germany
(2)
Bernstein Center for Computational Neuroscience Göttingen, 37077, Germany

References

  1. 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
  2. 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
  3. 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
  4. 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

Copyright

© Effenberger et al; licensee BioMed Central Ltd. 2013

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.

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