{'fraction_non_alphanumeric': 0.015081206496519721, 'fraction_numerical': 0.0, 'mean_word_length': 5.488721804511278, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
On the conscious and subconscious components of knowledge representation in neural networks

Principles of learning and knowledge representation are studied in complex neural networks with a large number of parameters. Our neural networks incorporate both deep and shallow knowledge representations. In the case of a stationary environment, the neural nets can develop a deep understanding of the problem and working in a properly chosen, narrow subspace of the system variables. In this functional mode, a small number of parameters dominate the operation of the network on the surface (conscious components), while the overwhelming part of the network operates at the subconscious level. The components realizing the conscious operational task are quite stable, nevertheless, they might change in time if the external environment of the analyzed system varies.