ABSTRACT This paper describes a new model for an artificial neural network processing unit or neuron. It is slightly different to a traditional feedforward network by the fact that it favours a mechanism of trying to match the wave-like ‘shape’ of the input with the shape of the output against specific value error corrections. The expectation is then that a best fit shape can be transposed into the desired output values more easily. This allows for notions of reinforcement through resonance and also the construction of synapses.https://www.edusoft.ro/brain/index.php/brain/article/view/418/47
<p>A. Network architecture. The network is composed of two interacting modalities. Each modality rec...
This thesis investigates the functionality of the units used in connectionist Artificial Intelligenc...
Full text of this chapter is not available in the UHRA.In this chapter, we describe neuroConstruct, ...
Currently used neural networks employ mostly simple neuron models that greatly differ from the "real...
(a) Simplified model of a biological neuron: The main body of the neuron receives input from many ot...
ABSTRACT This paper describes some biologically-inspired processes that could be used to build the ...
In this paper, we lay out a novel model of neuroplasticity in the form of a horizontal-vertical inte...
This paper presents a new approach to mental functions modeling with the use of artificial neural ne...
In this manuscript it is exposed a method to approximate functions using artificial neural networks ...
Artificial neuron network (ANN) is non linear mapping structures based on the function of human inte...
A biological neurone receives inputs from many sources, combines and presents them as a non-linear o...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
We outline the main models and developments in the broad field of artificial neural networks (ANN). ...
Models of neural nets are developed from a biological point of view. Small networks are analyzed usi...
Computational neuroscience is an interdisciplinary field that incorporates an analysis of brain func...
<p>A. Network architecture. The network is composed of two interacting modalities. Each modality rec...
This thesis investigates the functionality of the units used in connectionist Artificial Intelligenc...
Full text of this chapter is not available in the UHRA.In this chapter, we describe neuroConstruct, ...
Currently used neural networks employ mostly simple neuron models that greatly differ from the "real...
(a) Simplified model of a biological neuron: The main body of the neuron receives input from many ot...
ABSTRACT This paper describes some biologically-inspired processes that could be used to build the ...
In this paper, we lay out a novel model of neuroplasticity in the form of a horizontal-vertical inte...
This paper presents a new approach to mental functions modeling with the use of artificial neural ne...
In this manuscript it is exposed a method to approximate functions using artificial neural networks ...
Artificial neuron network (ANN) is non linear mapping structures based on the function of human inte...
A biological neurone receives inputs from many sources, combines and presents them as a non-linear o...
Neural network is a web of million numbers of inter-connected neurons which executes parallel proces...
We outline the main models and developments in the broad field of artificial neural networks (ANN). ...
Models of neural nets are developed from a biological point of view. Small networks are analyzed usi...
Computational neuroscience is an interdisciplinary field that incorporates an analysis of brain func...
<p>A. Network architecture. The network is composed of two interacting modalities. Each modality rec...
This thesis investigates the functionality of the units used in connectionist Artificial Intelligenc...
Full text of this chapter is not available in the UHRA.In this chapter, we describe neuroConstruct, ...