Today, it is considered that intelligence includes at least two
skills: the ability to memorize and store knowledge, and the
ability to process knowledge. The person (or machine) without any
knowledge cannot be considered intelligent. The ability of learning
- acquisition of new knowledge, is also one of the aspects of the
intelligence, although we can classify it as an ability to solve
problems. As an "intelligent feature" we can also consider the
ability to communicate with other intelligent beings. For the
concept of intelligence - two questions are essential: the question
of knowledge and the reasoning (making conclusions), and, this
corresponds to the terms of a knowledge base and a reasoning
process. The component of reasoning (inference) also represents a
kind of knowledge - it is knowledge about the process of carrying
out new information from an existing knowledge base. This edition
covers different topics from bio-intelligence science, and
application of bio-intelligence in different domains - the
bio-medical domain, the learning, the medicine etc. Section 1
focuses on biological aspects of the intelligence, describing
biological vs. artificial intelligence, brain as an emergent finite
automaton, biological neural network structure and spike activity
prediction based on multi-neuron spike train data, an experiment in
use of brain computer interfaces for cognitive researches, and
chessboard model of human brain and an application on memory
capacity. Section 2 focuses on topics from neuroscience, describing
patterns discovery in brain signals using decision trees, an
interactive immersive tool for brain education, art, and
neuro-therapy, analyzing brain functions by subject classification
of functional near-infrared spectroscopy data using convolutional
neural networks analysis, modeling neuromorphic persistent firing
networks, and creativity as central to critical reasoning and the
facilitative role of moral education. Section 3 focuses on pattern
recognition in neuro and medical applications, describing Brain-k
for structural image processing: creating electrical models of the
human head, application of machine learning in postural control
kinematics for the diagnosis of Alzheimer's disease, and semi
supervised clustering by iterative partition and regression with
neuroscience applications.Section 4 focuses on neural networks
applications, describing quantum-inspired neural networks with
application, training feedforward neural networks using symbiotic
organisms search algorithm, artificial intelligence for speech
recognition based on neural networks, and deep recurrent neural
network-based auto-encoders for acoustic novelty detection.
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