Error Weighting in Artificial Neural Networks Learning Interpreted as a Metaplasticity Model

Andina, Diego; Jevtić, Aleksandar; Marcano, Alexis; Barrón-Adame, J. M.

doi:10.1007/978-3-540-73053-8_24

Cited by 10 publications

(5 citation statements)

References 2 publications

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“…In this chapter, the introduced modeling environment for reified temporal-causal networks was applied to model a second-order adaptive Mental Network showing plasticity and metaplasticity as known from the empirical neuroscientific literature. Although some specific computational models for metaplasticity have been put forward with interesting perspectives for artificial neural networks, for example in Marcano-Cedeno et al (2011), Andina et al (2007, 2009, Fombellida et al (2017), the modeling environment proposed here provides a more general architecture. Applications may extend well beyond the neuro-inspired area (as will be shown in Chap.…”

Section: Discussionmentioning

confidence: 99%

Modeling Higher-Order Network Adaptation by Multilevel Network Reification

Treur

2019

Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models

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Section: Discussionmentioning

confidence: 99%

Modeling Higher-Order Network Adaptation by Multilevel Network Reification

Treur

2019

Network-Oriented Modeling for Adaptive Networks: Designing Higher-Order Adaptive Biological, Mental and Social Network Models

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“…In this paper, the introduced modeling environment for reified temporal-causal networks was also applied to model plasticity and metaplasticity known from the empirical neuroscientific literature (see Section 9). Although some specific computational models for metaplasticity have been put forward with interesting perspectives for artificial neural networks, for example, in Marcano-Cedeno et al 2011, Andina et al (2007), Andina et al (2009), and Fombellida et al (2017), the modeling environment proposed here provides a more general architecture. Application may extend well beyond the neuro-inspired area (as already shown in Sections 5-7).…”

Section: On Simulating Large-scale Reified Networkmentioning

confidence: 99%

Modeling higher order adaptivity of a network by multilevel network reification

Treur

2020

Net Sci

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In network models for real-world domains, often network adaptation has to be addressed by incorporating certain network adaptation principles. In some cases, also higher order adaptation occurs: the adaptation principles themselves also change over time. To model such multilevel adaptation processes, it is useful to have some generic architecture. Such an architecture should describe and distinguish the dynamics within the network (base level), but also the dynamics of the network itself by certain adaptation principles (first-order adaptation level), and also the adaptation of these adaptation principles (second-order adaptation level), and may be still more levels of higher order adaptation. This paper introduces a multilevel network architecture for this, based on the notion network reification. Reification of a network occurs when a base network is extended by adding explicit states representing the characteristics of the structure of the base network. It will be shown how this construction can be used to explicitly represent network adaptation principles within a network. When the reified network is itself also reified, also second-order adaptation principles can be explicitly represented. The multilevel network reification construction introduced here is illustrated for an adaptive adaptation principle from social science for bonding based on homophily and one for metaplasticity in Cognitive Neuroscience.

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“…Neurons are interconnected with connection links which have weights that are multiplied by the signal transmitted in the network [10]. The output of each network is determined by use of an activating function as the Sigmoid or Gaussian [11].…”

Section: A Multi Layer Perceptron Neural Network (Mlp)mentioning

confidence: 99%

Using artificial immune algorithm for fast convergence of multi layer perceptron in breast cancer diagnosis application

Daoudi¹,

Djemal²,

Benyettou³

2015

2015 International Conference on Image Processing Theory, Tools and Applications (IPTA)

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International audienceIn this paper, a Multi Layer Perceptron (MLP) based Artificial Immune System (AIS) is presented for breast cancer classification. The proposed algorithm integrates clonal selection principle of AIS in MLP learning to reduce its computational costs and accelerate its convergence to a Mean Squared Error Threshold (MSEth) set by the user. Applied on the Wisconsin Diagnosis Breast Cancer database (WDBC), the results show that combining Artificial Immune Systems and Neural Networks is effective. Indeed, a significant reduction of computation time has been obtained with a slight improvement of classification accuracy

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Error Weighting in Artificial Neural Networks Learning Interpreted as a Metaplasticity Model

Cited by 10 publications

References 2 publications

Modeling Higher-Order Network Adaptation by Multilevel Network Reification

Modeling Higher-Order Network Adaptation by Multilevel Network Reification

Modeling higher order adaptivity of a network by multilevel network reification

Using artificial immune algorithm for fast convergence of multi layer perceptron in breast cancer diagnosis application

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