Abstract
This paper presents a novel lattice based biomimetic neural network trained by means of a similarity measure derived from a lattice positive valuation. For a wide class of pattern recognition problems, the proposed artificial neural network, implemented as a dendritic hetero-associative memory delivers high percentages of successful classification. The memory is a feedforward dendritic network whose arithmetical operations are based on lattice algebra and can be applied to real multivalued inputs. In this approach, the realization of recognition tasks, shows the inherent capability of prototype-class pattern associations in a fast and straightforward manner without need of any iterative scheme subject to issues about convergence. Using an artificially designed data set we show how the proposed trained neural net classifies a test input pattern. Application to a few typical real-world data sets illustrate the overall network classification performance using different training and testing sample subsets generated randomly.
Highlights
The lattice neural network discussed in this paper is a biomimetic neural network.The term biomimetic refers to man-made systems of processes that imitate nature
The interested reader may peruse the works of some researchers [3,4,5,6,7,8,16,20,21], that have proposed possible biophysical mechanisms for dendritic computation of logical functions such as ‘AND’, ‘NOT’, ‘OR’, and ‘XOR’. It is in light of these observations that we modeled biomimetic artificial neural networks based on dendritic computing
Lattice Biomimetic Neural Networks In artificial neural networks (ANNs) endowed with dendrites whose computation is based on lattice algebra, a set
Summary
The lattice neural network discussed in this paper is a biomimetic neural network. The term biomimetic refers to man-made systems of processes that imitate nature. In order to model an artificial neural network that can represent more faithfully a biological brain network, it is not possible to ignore dendrites and their spines, which cover the membrane of a neuron in more than 50% This is true by considering that several brain researchers have proposed that dendrites (not the neuron) are the basic computing devices of the brain. The interested reader may peruse the works of some researchers [3,4,5,6,7,8,16,20,21], that have proposed possible biophysical mechanisms for dendritic computation of logical functions such as ‘AND’, ‘NOT’, ‘OR’, and ‘XOR’ It is in light of these observations that we modeled biomimetic artificial neural networks based on dendritic computing.
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