Dendrite morphological neurons (DMNs) are artificial neural models for pattern classification that present two main characteristics: a geometric shape represents a dendrite, and the dendritic processing is based on morphological operations represented by the minimum and maximum functions (Ritter & Urcid, 2003).
We provide an alternative way of computation in an artificial neuron based on lattice algebra and dendritic computation. The neurons of the proposed model bear ...
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Abstract. Computation in a neuron of a traditional neural network is accomplished by summing the products of neural values and connection.
We introduce a DMN learning approach based on a deterministic hierarchical clustering method, which builds a linkage tree for each class of patterns.
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Classification of neurons by dendritic branching pattern. A categorisation based on Golgi impregnation of spinal and cranial somatic and visceral afferent and ...
Topics · Computation · Dendritic Computations · Pattern Classification · Lattice Algebra · Activation Function · Connection Weights · Neural Network ...
We present a two layer dendritic hetero-associative memory that gives high percentages of correct classification for typical pattern recognition problems. The ...
In this paper, a neuron model with dendrite morphology, called the logic dendritic neuron model (LDNM), is proposed for classification.
We present a pattern classification model which uses a sparse connection matrix and exploits the mechanism of nonlinear dendritic processing to achieve high ...
The dendrites of a neuron are cellular extensions with many branches. This overall shape and structure are referred to metaphorically as a dendritic tree.