{'fraction_non_alphanumeric': 0.03423566878980892, 'fraction_numerical': 0.007961783439490446, 'mean_word_length': 4.63677130044843, '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}}
Data convergence in a brain model.

Assuming the existence of encoding synapses that record presynaptic axonal 'on-off' pattern as the content of memory, the author has presented a brain model. In this brain model, the synapses of a neuron work like a static random access memory (RAM) that may encode 2 the power of 10,000 'on-off' patterns, the cell body like a central processing unit (CPU) that produces a signal of 1 or 0 in response to different presynaptic axonal 'on-off' patterns, and the axon like a data bus to form synapse with another neuron. Accordingly, the brain is analogous with a computer made of serial static RAMs amid 14 billions of parallel processing CPUs. Such a brain model converges data with each tier of computation, because there are always more input presynaptic 'on-off' patterns than output axonal 'on-off' patterns in a cortical area. The more computations of the data from the primary perceptive cortices, the more likely the data involving the synapses of central cortices, and the more abstract the content of the memory--hence, in the retrieval of memory, parts of the 'on-off' patterns of the original stimulus may lead to the converged, abstract memory. This can be the mechanism of pattern recognition in the brain.