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 ra ndom access memory (RAM) that may encode 2 the power of 10 000 'on-off' pat terns, the cell body like a central processing unit (CPU) that produces a s ignal of 1 or 0 in response to different presynaptic axonal 'on-off' patter ns, and the axon like a data bus to form synapse with another neuron. Accor dingly, the brain is analogous with a computer made of serial static RAMs a mid 14 billions of parallel processing CPUs. Such a brain model converges d ata with each tier of computation, because there are always more input pres ynaptic 'on-off' patterns than output axonal 'on-off' patterns in a cortica l area. The more computations of the data from the primary perceptive corti ces, the more likely the data involving the synapses of central cortices, a nd the more abstract the content of the memory-hence, in the retrieval of m emory, parts of the 'on-off' patterns of the original stimulus may lead to the converged, abstract memory. This can be the mechanism of pattern recogn ition in the brain. (C) 1999 Harcourt Publishers Ltd.