The design of modern convolutional artificial neural networks (ANNs) composed of formal
neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy …

Dealing with uncertainty in applications of machine learning to real-life data critically
depends on the knowledge of intrinsic dimensionality (ID). A number of methods have been …

High-dimensional data and high-dimensional representations of reality are inherent features
of modern Artificial Intelligence systems and applications of machine learning. The well …

Development of spiking neural networks (SNNs) controlling mobile robots is one of the
modern challenges in computational neuroscience and artificial intelligence. Such networks …

A sentence is more than the sum of its words: its meaning depends on how they combine
with one another. The brain mechanisms underlying such semantic composition remain …

The curse of dimensionality causes the well-known and widely discussed problems for
machine learning methods. There is a hypothesis that using the Manhattan distance and …

One of the modern trends in the design of human–machine interfaces (HMI) is to involve the
so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by …

The cell membrane has a layered structure, which separates the intracellular and
extracellular ions for develo** gradient electromagnetic field, and its flexible property …

This work is driven by a practical question: corrections of Artificial Intelligence (AI) errors.
These corrections should be quick and non-iterative. To solve this problem without …

Mammalian brains operate in very special surroundings: to survive they have to react quickly
and effectively to the pool of stimuli patterns previously recognized as danger. Many …