Running actual images where the background is a thing put all my theories on ice.. I first realized that when I have many synapses for a neuron and few for another, it modifies the activation phase …d’ooohh… and I had to bring back my old nemesis, frequency. Frequency means now that a neuron can receive multiple pre-synaptic inputs before it fires once. I discarded this idea not only because it complicates matter, but I though this should not be the default mechanism, because would be a waste of energy.. Besides with thousands of synapses is not very likely for a neuron to wait for a secondary activation to do a time summation for activation. Anyway, I thought by bringing frequency back, the very bright neuron would win the inhibition battle.. Not likely, multiple synapses would still win. The consequence is that the background is selected instead of the foreground.. because it’s big 🙂 . How are CNNs dealing with this issue ? I’ll have to look into it. I have thought long and hard… I see no way around this, because in a way is the expected behavior given my algorithms… But, as with many other problems, I have a general solution.. I’m going to ignore it… I’ll chose a very low intensity background, I’ll avoid using both ON and OFF bipolar cells from the receptive fields and go with this. Sure I can’t have real images now, but maybe along the way I’ll find a solution or a solution would present itself..
TECHNOLOGIES