Abstract: Identifying shared events across spiking-neural-network data streams with significant stochastic content. The data streams are first subject to cross correlation. If two data streams are completely uncorrelated, the rate of occurrence, of cross-stream spike pairs, is an approximately uniform “r_ind” across all Time Between Events (TBE's). Any shared events create a gradient, where r_ind increases to a rate “r_shr,” for any TBE's?a Time Of Discernment (TOD). A search for the actual TOD (TOD_a) can be accomplished with a conjectured TOD (TOD_c). TOD_c is tested against an exponential decay with its rate set to a conjectured r_ind (r_ind_c). When r_ind_c=actual r_ind, equal ranges (or regions) of values, of exponential decay, represent equal probabilities. Values of TOD_c and r_ind_c are generated (at respective learning rates), until a combination is found where probabilistically equal regions receive statistically equal numbers of cross-stream events. It is then known TOD_a?TOD_c.

Abstract: A multi-stream cross correlator for spiking neural networks, where each stream contains significant stochastic content. At least one event occurs, with a fixed temporal relationship across at least two streams. Each stream is treated as a Frame Of Reference (FOR), and subject to an adjustable delay based on comparison to the Other streams. For each spike of the FOR, a timing analysis, relative to the last and current FOR spikes, is completed by comparing Post and Pre accumulators. Also, a new timing analysis is begun, with the current FOR spike, by restarting the production of Post and Pre weighting functions, the values of which are accumulated, upon the occurrence of each Other spike, until a next FOR spike. A one-spike delay unit can be used, if time-neutral conflict resolution is used. The average spike rate of the FOR can be determined and used for the Post and Pre weighting functions.

Abstract: Identifying shared events across spiking-neural-network data streams with significant stochastic content. The data streams are first subject to cross correlation. If two data streams are completely uncorrelated, the rate of occurrence, of cross-stream spike pairs, is an approximately uniform “r_ind” across all Time Between Events (TBE's). Any shared events create a gradient, where r_ind increases to a rate “r_shr,” for any TBE's?a Time Of Discernment (TOD). A search for the actual TOD (TOD_a) can be accomplished with a conjectured TOD (TOD_c). TOD_c is tested against an exponential decay with its rate set to a conjectured r_ind (r_ind_c). When r_ind_c=actual r_ind, equal ranges (or regions) of values, of exponential decay, represent equal probabilities. Values of TOD_c and r_ind_c are generated (at respective learning rates), until a combination is found where probabilistically equal regions receive statistically equal numbers of cross-stream events. It is then known TOD_a?TOD_c.