Seems by adding kinetics to synapses I added also time to the algorithm. But time has always been an elusive variable. Time is the rate of change for some events. So time is not really correlated with the outside arbitrary unit of time and will depend on the computer power. It is very possible to correlate this internal time to the outside time but for now it will serve no purpose. However time is now embedded in multiple processes. What can be learned is now indirectly linked to time. The time component will determine what is correlated and what is “important”. Time also seems to determine how many patterns can a synapse learn without internal changes. Slower kinetics would allow for more patterns being learned.. In a way would increase precision or selectivity. Increase precision requires more processing cycles.
On the update side.
I implemented the new kinetics at synapse level but I need some sort of kinetics at neuronal level. Without it I cannot decide when a neuron was active. Inhibitory neurons still work on the old simpler mechanism so inhibition is instantaneous and inhibits 100%. I may have to change that in the future.