Intrinsic plasticity

Improving reservoirs using intrinsic plasticity

The benefits of using Intrinsic Plasticity (IP), an unsupervised, local, biologically inspired adaptation rule that tunes the probability density of a neuron’s output towards an exponential distribution – thereby realizing an information maximization – have already been demonstrated. In this work, we extend the ideas of this adaptation method to a more commonly used nonlinearity and a Gaussian ...

Posttranscriptional Regulation of Intrinsic Plasticity

Intrinsic Plasticity in Random Recurrent Networks

ii iii Hiermit erkläre ich, dass ich die vorliegende Arbeit selbstständig verfasst und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe.

Learning sensory representations with intrinsic plasticity

Intrinsic plasticity (IP) refers to a neuron’s ability to regulate its firing activity by adapting its intrinsic excitability. Previously, we showed that model neurons combining a model of IP based on information theory with Hebbian synaptic plasticity can adapt their weight vector to discover heavy-tailed directions in the input space. In this paper we show how a network of such units can solv...

Intrinsic Plasticity via Natural Gradient Descent

This paper introduces the natural gradient for intrinsic plasticity, which tunes a neuron’s activation function such that its output distribution becomes exponentially distributed. The information-geometric properties of the intrinsic plasticity potential are analyzed and the improved learning dynamics when using the natural gradient are evaluated for a variety of input distributions. The appli...