Add to ORKGAbstract. This paper introduces the natural gradient for intrinsic plas-ticity, which tunes a neuron’s activation function such that its output dis-tribution becomes exponentially distributed. The information-geometric properties of the intrinsic plasticity potential are analyzed and the im-proved learning dynamics when using the natural gradient are evaluated for a variety of input distributions. The applied measure for evaluation is the relative geodesic length of the respective path in parameter space.
<p>(A) Input patterns that are particularly challenging for learning since the weak pattern (bottom)...
We analyse natural gradient learning in a two-layer feed-forward neural network using a statistical ...
Synaptic plasticity is the primary physiological mechanism underlying learning in the brain. It is d...
Neumann K, Steil JJ. Intrinsic Plasticity via Natural Gradient Decent. In: Verleysen M, ed. 20th Eur...
Neumann K, Steil JJ. Optimizing Extreme Learning Machines via Ridge Regression and Batch Intrinsic P...
Neumann K, Strub C, Steil JJ. Intrinsic Plasticity via Natural Gradient Descent with Application to ...
Neurons in various sensory modalities transform the stimuli into series of action potentials The mu...
Experience constantly shapes neural circuits through a variety of plasticity mechanisms. While the f...
Experience constantly shapes neural circuits through a variety of plasticity mechanisms. While the f...
Intrinsic plasticity (IP) refers to a neuron’s ability to regulate its firing activity by adapting i...
The parameter space of neural networks has the Riemannian metric structure. The natural Riemannian g...
In many normative theories of synaptic plasticity, weight updates implicitly depend on the chosen pa...
Continuous adaptation allows survival in an ever-changing world. Adjustments in the synaptic couplin...
In experimental and theoretical neuroscience, synaptic plasticity has dominated the area of neural p...
This paper explores the computational consequences of simultaneous in-trinsic and synaptic plasticit...
<p>(A) Input patterns that are particularly challenging for learning since the weak pattern (bottom)...
We analyse natural gradient learning in a two-layer feed-forward neural network using a statistical ...
Synaptic plasticity is the primary physiological mechanism underlying learning in the brain. It is d...
Neumann K, Steil JJ. Intrinsic Plasticity via Natural Gradient Decent. In: Verleysen M, ed. 20th Eur...
Neumann K, Steil JJ. Optimizing Extreme Learning Machines via Ridge Regression and Batch Intrinsic P...
Neumann K, Strub C, Steil JJ. Intrinsic Plasticity via Natural Gradient Descent with Application to ...
Neurons in various sensory modalities transform the stimuli into series of action potentials The mu...
Experience constantly shapes neural circuits through a variety of plasticity mechanisms. While the f...
Experience constantly shapes neural circuits through a variety of plasticity mechanisms. While the f...
Intrinsic plasticity (IP) refers to a neuron’s ability to regulate its firing activity by adapting i...
The parameter space of neural networks has the Riemannian metric structure. The natural Riemannian g...
In many normative theories of synaptic plasticity, weight updates implicitly depend on the chosen pa...
Continuous adaptation allows survival in an ever-changing world. Adjustments in the synaptic couplin...
In experimental and theoretical neuroscience, synaptic plasticity has dominated the area of neural p...
This paper explores the computational consequences of simultaneous in-trinsic and synaptic plasticit...
<p>(A) Input patterns that are particularly challenging for learning since the weak pattern (bottom)...
We analyse natural gradient learning in a two-layer feed-forward neural network using a statistical ...
Synaptic plasticity is the primary physiological mechanism underlying learning in the brain. It is d...