A Preliminary Neural Model for Movement Direction Recognition Based on Biologically Plausible Plasticity Rules

Kinto, Eduardo Akira; Hernandez, Emilio M.; Marcano, Alexis; Peláez, Javier Ropero

doi:10.1007/978-3-540-73055-2_65

Cited by 24 publications

(7 citation statements)

References 7 publications

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“…The concept of biological metaplasticity was defined in 1996 by Abraham [26] and now is widely applied in the fields of biology, finances, neuroscience, physiology, neurology and others [26,27]. Recently, Andina [21], Ropero-Peláez [22] and Marcano-Cerdeño [28] have introduced and modeled the biological property metaplasticity in the field of ANNs, obtaining excellent results.…”

Section: Artificial Metaplasticity Neural Network Modelmentioning

confidence: 99%

Tackling business intelligence with bioinspired deep learning

Fombellida

Martín‐Rubio

Torres-Alegre

et al. 2018

Neural Comput & Applic

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To tackle the complex problem of providing business intelligence solutions based on business data, bioinspired deep learning has to be considered. This paper focuses on the application of artificial metaplasticity learning in business intelligence systems as an alternative paradigm of achieving a deeper information extraction and learning from arbitrary size data sets. As a case study, artificial metaplasticity multilayer perceptron applied to the automation of credit approval decision based on collected client data is analyzed, showing its potential and improvements over the state-of-the-art techniques. This paper successfully introduces the relevant novelty that the artificial neural network itself estimates the pdf of the input data to be used in the metaplasticity learning, so it is much closer to the biologic reality than previous implementations of artificial metaplasticity.

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Section: Artificial Metaplasticity Neural Network Modelmentioning

confidence: 99%

Tackling business intelligence with bioinspired deep learning

Fombellida

Martín‐Rubio

Torres-Alegre

et al. 2018

Neural Comput & Applic

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“…The concept of biological metaplasticity was defined in 1996 by W.C. Abraham [20] and is now widely applied in the fields of biology, neuroscience, physiology, neurology and others [20,21 ]. In neuroscience and other fields "metaplasticity" indicates a higher level of plasticity, expressed as a change or transformation in the way synaptic efficacy is modified.…”

Section: Artificial Metaplasticitymentioning

confidence: 99%

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Marcano-Cedeño

Chausa

García

et al. 2013

Artificial Intelligence in Medicine

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Objective: The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials:The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC®. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC® and the outcome of the patient after a 3-5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results: The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions: The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

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“…The concept of biological metaplasticity was defined in 1996 by Abraham [9] and now widely applied in the fields of biology, neuroscience, physiology, neurology and others [9,10].…”

Section: Artificial Metaplasticity Neural Networkmentioning

confidence: 99%

“…Recently, Ropero-Peláez [10], Andina [11] and Marcano-Cedeño [12] have introduced and modeled the biological property metaplasticity in the field of artificial neural networks (ANN), obtaining excellent results.…”

Section: Artificial Metaplasticity Neural Networkmentioning

confidence: 99%

A Prediction Model to Diabetes Using Artificial Metaplasticity

Marcano-Cedeño

Torres

Andina

2011

Lecture Notes in Computer Science

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Abstract. Diabetes is the most common disease nowadays in all populations and in all age groups. Different techniques of artificial intelligence has been applied to diabetes problem. This research proposed the artificial metaplasticity on multilayer perceptron (AMMLP) as prediction model for prediction of diabetes. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with other algorithms, recently proposed by other researchers,that were applied to the same database. The best result obtained so far with the AMMLP algorithm is 89.93%.

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A Preliminary Neural Model for Movement Direction Recognition Based on Biologically Plausible Plasticity Rules

Cited by 24 publications

References 7 publications

Tackling business intelligence with bioinspired deep learning

Tackling business intelligence with bioinspired deep learning

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

A Prediction Model to Diabetes Using Artificial Metaplasticity

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