Neumann K, Steil JJ. Optimizing Extreme Learning Machines via Ridge Regression and Batch Intrinsic Plasticity. Neurocomputing. 2013;102(Special Issue: Advances in Extreme Learning Machines (ELM 2011):23-30
The parameter space of neural networks has the Riemannian metric structure. The natural Riemannian g...
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, ho...
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, ho...
Neumann K, Steil JJ. Intrinsic Plasticity via Natural Gradient Decent. In: Verleysen M, ed. 20th Eur...
Abstract. This paper introduces the natural gradient for intrinsic plas-ticity, which tunes a neuron...
Neumann K, Strub C, Steil JJ. Intrinsic Plasticity via Natural Gradient Descent with Application to ...
In experimental and theoretical neuroscience, synaptic plasticity has dominated the area of neural p...
In this work, I study the relationship between a local, intrinsic update mechanism and a synaptic, e...
Neurons in various sensory modalities transform the stimuli into series of action potentials The mu...
As a self-adaptive mechanism, intrinsic plasticity (IP) plays an essential role in maintaining homeo...
In many normative theories of synaptic plasticity, weight updates implicitly depend on the chosen pa...
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...
Synaptic plasticity is the primary physiological mechanism underlying learning in the brain. It is d...
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...
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, ho...
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, ho...
Neumann K, Steil JJ. Intrinsic Plasticity via Natural Gradient Decent. In: Verleysen M, ed. 20th Eur...
Abstract. This paper introduces the natural gradient for intrinsic plas-ticity, which tunes a neuron...
Neumann K, Strub C, Steil JJ. Intrinsic Plasticity via Natural Gradient Descent with Application to ...
In experimental and theoretical neuroscience, synaptic plasticity has dominated the area of neural p...
In this work, I study the relationship between a local, intrinsic update mechanism and a synaptic, e...
Neurons in various sensory modalities transform the stimuli into series of action potentials The mu...
As a self-adaptive mechanism, intrinsic plasticity (IP) plays an essential role in maintaining homeo...
In many normative theories of synaptic plasticity, weight updates implicitly depend on the chosen pa...
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...
Synaptic plasticity is the primary physiological mechanism underlying learning in the brain. It is d...
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...
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, ho...
Dynamic neural fields (DNFs) are dynamical systems models that approximate the activity of large, ho...
DOI
Add to ORKG