  Driven by analogies with state vectors of quantum informatics and noise-based
logic, we propose a general scheme and elements of neural circuitry for
processing and addressing information in the brain. Specifically, we consider
random (e.g., Poissonian) trains of finite-duration spikes, and, using the
idealized concepts of excitatory and inhibitory synapses, offer a procedure for
generating 2^N-1 orthogonal vectors out of N partially overlapping trains
("neuro-bits"). We then show that these vectors can be used to construct
2^(2^N-1)-1 different superpositions which represent the same number of logic
values when carrying or routing information. In quantum informatics the above
numbers are the same, however, the present logic scheme is more advantageous
because it is deterministic in the sense that the presence of a vector in the
spike train is detected by an appropriate coincidence circuit. For this reason
it does not require time averaging or repeated measurements of the kind used in
standard cross-correlation analysis or in quantum computing.
