From Action to Cognition: Neural Reuse, Network Theory and the Emergence of Higher Cognitive Functions

The aim of this article is to discuss the logic and assumptions behind the concept of neural reuse, to explore its biological advantages and to discuss the implications for the cognition of a brain that reuses existing circuits and resources. We first address the requirements that must be fulfilled for neural reuse to be a biologically plausible mechanism. Neural reuse theories generally take a developmental approach and model the brain as a dynamic system composed of highly flexible neural networks. They often argue against domain-specificity and for a distributed, embodied representation of knowledge, which sets them apart from modular theories of mental processes. We provide an example of reuse by proposing how a phylogenetically more modern mental capacity (mental rotation) may appear through the reuse and recombination of existing resources from an older capacity (motor planning). We conclude by putting arguments into context regarding functional modularity, embodied representation, and the current ontology of mental processes.

Fluoropolymer-based organic memristor with multifunctionality for flexible neural network system

In this study, we propose an effective strategy for achieving the flexible one organic transistor–one organic memristor (1T–1R) synapse using the multifunctional organic memristor. The dynamics of the conductive nanofilament (CF) in a hydrophobic fluoropolymer medium is explored and a hydrophobic fluoropolymer-based organic memristor is developed. The flexible 1T–1R synapse can be fabricated using the solution process because the hydrophobic fluorinated polymer layer is produced on the organic transistor without degradation of the underlying semiconductor. The developed flexible synapse exhibits multilevel conductance with high reliability and stability because of the fluoropolymer film, which acts as a medium for CF growth and an encapsulating layer for the organic transistor. Moreover, the synapse cell shows potential for high-density memory systems and practical neural networks. This effective concept for developing practical flexible neural networks would be a basic platform to realize the smart wearable electronics.

Neural Attribute Normalization: An Application to Product Portfolio Optimization

The importance of the choice set composition on individual preferences has been of wide interest in decision models. We propose a model seeking to elaborate on the role that choice set composition plays in a discrete choice problem through a normalization of the perceived value of each product's attributes. Our model extends the comprehension of context effects beyond the classical three-option cases of decoy, compromise and similarity. We apply a state-of-the-art class of models stemming from recent research on neural normalization to a multi-attribute choice setting. We also investigate the construction of the reference point by comparing different models, from simple cases to flexible neural networks. We highlight the performance of the model with an experimental application to credit card choices,and discuss the implications for product portfolio optimization. We find decisive evidence for attribute-based normalizing behavior. Understanding this normalization phenomenon will allow firms to optimize their portfolio with options whose main purpose is to increase the sales of their other products.

Highly reliable two-factor biometric authentication based on handwritten and voice passwords using flexible neural networks

The paper addresses a problem of highly reliable biometric authentication based on converters of secret biometric images into a long key or password, as well as their testing on relatively small samples (thousands of images). Static images are open, therefore with remote authentication they are of a limited trust. A process of calculating the biometric parameters of voice and handwritten passwords is described, a method for automatically generating a flexible hybrid network consisting of various types of neurons is proposed, and an absolutely stable algorithm for network learning using small samples of “Custom” (7-15 examples) is developed. A method of a trained hybrid "biometrics-code" converter based on knowledge extraction is proposed. Low values of FAR (false acceptance rate) are achieved.

Flexible Neural Network Architecture for Handwritten Signatures Recognition

This article illustrates modeling of flexible neural networks for handwritten signatures preprocessing. An input signature is interpolated to adjust inclination angle, than descriptor vector is composed. This information is preprocessed in proposed flexible neural network architecture, in which some neurons are becoming crucial for recognition and adapt to classification purposes. Experimental research results are compared in benchmark tests with classic approach to discuss efficiency of proposed solution.