Abstract. We calculate bounds on the VC dimension and pseudo dimension for networks of spiking neurons. The connections between network nodes are parameterized by transmission delays and synaptic weights. We provide bounds in terms of network depth and number of connections that are almost linear. For networks with few layers this yields better bounds than previously established results for networks of unrestricted depth. 1. Introduction and D
We find the VC dimension of a leaky integrate-and-fire neuron model. The VC dimension quantifies the...
AbstractMost of the work on the Vapnik-Chervonenkis dimension of neural networks has been focused on...
A general relationship is developed between the VC-dimension and the statistical lower epsilon-capac...
Techniques from differential topology are used to give polynomial bounds for the VC-dimension of sig...
Spiking neurons are models for the computational units in biological neural systems where informatio...
Spiking neurons are models for the computational units in biological neural systems where informatio...
AbstractSpiking neurons are models for the computational units in biological neural systems where in...
2 Abstract We investigate the computational power of a formal model for networks of spiking neurons....
Most of the work on the Vapnik-Chervonenkis dimension of neural networks has been focused on feedfor...
AbstractThis paper shows that neural networks which use continuous activation functions have VC dime...
A product unit is a formal neuron that multiplies its input values instead of summing them. Further...
Most of the work on the Vapnik-Chervonenkis dimension of neural networks has been focused on feedfor...
. We consider the VC-dimension of a set of the neural networks of depth s with w adjustable paramet...
This paper shows that neural networks which use continuous activation functions have VC dimension at...
We find the VC dimension of a leaky integrate-and-fire neuron model. The VC dimension quantifies the...
We find the VC dimension of a leaky integrate-and-fire neuron model. The VC dimension quantifies the...
AbstractMost of the work on the Vapnik-Chervonenkis dimension of neural networks has been focused on...
A general relationship is developed between the VC-dimension and the statistical lower epsilon-capac...
Techniques from differential topology are used to give polynomial bounds for the VC-dimension of sig...
Spiking neurons are models for the computational units in biological neural systems where informatio...
Spiking neurons are models for the computational units in biological neural systems where informatio...
AbstractSpiking neurons are models for the computational units in biological neural systems where in...
2 Abstract We investigate the computational power of a formal model for networks of spiking neurons....
Most of the work on the Vapnik-Chervonenkis dimension of neural networks has been focused on feedfor...
AbstractThis paper shows that neural networks which use continuous activation functions have VC dime...
A product unit is a formal neuron that multiplies its input values instead of summing them. Further...
Most of the work on the Vapnik-Chervonenkis dimension of neural networks has been focused on feedfor...
. We consider the VC-dimension of a set of the neural networks of depth s with w adjustable paramet...
This paper shows that neural networks which use continuous activation functions have VC dimension at...
We find the VC dimension of a leaky integrate-and-fire neuron model. The VC dimension quantifies the...
We find the VC dimension of a leaky integrate-and-fire neuron model. The VC dimension quantifies the...
AbstractMost of the work on the Vapnik-Chervonenkis dimension of neural networks has been focused on...
A general relationship is developed between the VC-dimension and the statistical lower epsilon-capac...