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Computational neuroscience (theory of neuroscience) theory and applications of artificial neural networks

All lectures: All lectures on the discipline: computational neuroscience (theory of neuroscience) theory and applications of artificial neural networks are collected here. These fascinating lectures provide us with the opportunity to explore the deep principles and phenomena underlying our knowledge and our desire to understand the world.


1. Computational Neuroscience (Computational Neuroscience) Introduction
2. Information from higher mathematics.
3. Biological neuron and its cybernetic model.
4. PERSEPTRON Rosenblatt.
5. 5. Properties of learning processes in neural networks.
6. 6. Multi-layer PERSEPTRON.
7. 7. Other hierarchical architectures.
8. 8. Hopfield model
9. 9. Generalizations and applications of the Hopfield model.
10. 10. NEOKOGNITRON Fukushima.
11. 11. Theory of adaptive resonance.
12. 12. Features of modern architecture neural networks
13. 1. Computer simulation of neural networks.
14. Genetic search. Fuzzy logic systems. Cellular automata and neural networks.
15. Neural Network Exam Tests for Data Scientist
16. ANALYSIS OF THE ALGORITHM FOR REVERSE DISTRIBUTION OF NEURAL NETWORK ERROR
17. Types of neural network architectures
18. Neurocomputer Interface (Neural Interface)
19. Introduction to neural network architectures. Classification of neural networks, principle of operation