Noise in Biological Systems
Self Organization of Neuronal Information Processing
in Recurrent Networks
Gordon Pipa
Max Planck Institute for Brain Research, Frankfurt, Germany
& Frankfurt Institute for Advanced Studies (FIAS)

Neuronal activity is the key element of neuronal information processing. Recent evidence shows that the network sub serving this activity changes constantly and substantially on different temporal scales ranging from milliseconds to days and years. Moreover, single neurons seem to exhibit a high level of noise. Both of these two facts are a major challenge for information processing and have been widely ignored so far. As a solution we propose a combination of online processing of time-varying inputs introduced as the Echo state networks (Jäger 2004) and liquid state machines (Markram 2002), with self organized optimization of the dynamical state and of the network structure. Self organized optimization is achieved by the combination of two types of neuronal plasticity. The first type called spike timing dependent plasticity is used for sequence learning and for structure formation in the network. The second type called intrinsic plasticity achieves homeostasis of the overall network activity and acts as an intrinsic noise source. Based on extensive simulation studies we demonstrate that the combination of both types first optimizes the information processing, second leads to self organized criticality of the network dynamics, and third that the intrinsic noise introduced by intrinsic plasticity increases the robustness of information processing in a high noise regime.
This work was supported by the Hertie and VW foundation.

1. Lazar, G. Pipa, J. Triesch. Fading memory and time series prediction in recurrent networks with different forms of plasticity, Neural Networks 20 312–322, (2007)

2. Song, S., Miller, K. D., & Abbott, L. F.. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience, 3(9), 919–926, (2000).

3. Maass, W., Natschl¨ager, T., & Markram, H.. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 14, 2531–2560, (2002)

4. Jäger, H., & Haas, H.. Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication. Science, 304, 78–80, (2004)

5. g-pipa.de/Publication_list/publication_list.html

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