Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Gain Control by Concerted Changes in IA and IH Conductances
Neural Computation
Tilt Aftereffects in a Self-Organizing Model of the Primary Visual Cortex
Neural Computation
Learning sensory representations with intrinsic plasticity
Neurocomputing
Synergies Between Intrinsic and Synaptic Plasticity Mechanisms
Neural Computation
Goal-directed feature learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Role of homeostasis in learning sparse representations
Neural Computation
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Current models for learning feature detectors work on two timescales: on a fast timescale, the internal neurons' activations adapt to the current stimulus; on a slow timescale, the weights adapt to the statistics of the set of stimuli. Here we explore the adaptation of a neuron's intrinsic excitability, termed intrinsic plasticity, which occurs on a separate timescale. Here, a neuron maintains homeostasis of an exponentially distributed firing rate in a dynamic environment. We exploit this in the context of a generative model to impose sparse coding. With natural image input, localized edge detectors emerge as models of V1 simple cells. An intermediate timescale for the intrinsic plasticity parameters allows modeling aftereffects. In the tilt aftereffect, after a viewer adapts to a grid of a certain orientation, grids of a nearby orientation will be perceived as tilted away from the adapted orientation. Our results show that adapting the neurons' gain-parameter but not the threshold-parameter accounts for this effect. It occurs because neurons coding for the adapting stimulus attenuate their gain, while others increase it. Despite its simplicity and low maintenance, the intrinsic plasticity model accounts for more experimental details than previous models without this mechanism.