make it or break it time

I’m nearly there, about two more weeks to get there. I worked a lot, but mostly doing simulations. The problems more or less remained the same:

  • inter-layer transmission of signal. Because activation frequency decreases with each layer, in layer 3 I already get to the point where the frequency is too low to activate neurons and synapses are classified as low frequency and removed. Increasing some sort of synaptic strength did not work because that increase is limited, it can’t be something arbitrary. Same layer connections should work as an amplifier, but they suffer from bad timing. There are mechanism that should synchronize the firing events but they too fail more often than not. I still have hopes to improve on this though. Another mechanism could be a persistent reinforcing feedback … Every 3 layers or so the third layer should feedback on the first layer, increasing the frequency for both, it seems far fetched but I’m close to implementing this option too.
  • Inhibition – I’m still looking for the proper level of inhibition. How many synapses should act together to overcome inhibition (meaning in spite of inhibition being present and limiting synaptic potential, the neuron should still fire by adding small but many synaptic signals). For now I believe that this should be the desired mechanism, rather than “all or nothing” mechanism I had so far. The all or nothing is still present for fewer activating synapses. But the number of synapses needed to cooperate is still unclear .. it has to be related to some minimum frequency..
  • Synaptic frequency – a single synapse seems to very limited in the values it can take.. In theory I could have around 100 values, but in practical terms that number is even lower .. around 20 values.. Messing with this number is tricky … There is a hard theoretical limit for the highest and lowest values, but if I increase the value, I also increase the computation time and limit the inter-layer transmission. I believe I can find a reasonable compromise, but right now I feel severely limited by this problem..
  • Synaptic connections – has now become the most pressing issue and the most complicated.. Making and breaking synapses on large matrices is slow an requires me to keep track of every connection. A single neuron can connect to a lower layer same layer and upper layer + to a Inhibitory layer .. at the same time. So far I used a simplification, once removed a synapse would not form again. But that was putting serious pressure on the mechanism for timing the connections, meaning a connection should be the best connection possible from the first time. That is not working right. I tried to wait long enough for all connections to form in the first layer and only after that bind to the second layer.. There are too many cases to consider. So now I let connections form and break forever. But keeping track of all of them is difficult. I still wonder how two neurons close by, stop forming an infinite amount of connections among them. They form many, to be sure but not a fixed or an infinite number.. So when does the process stop.. why ? If I don’t keep track of every synapse, two neurons would keep forming synapses among themselves..
  • Training patterns – I thought they could be everything.. I’m not so sure anymore. Signals received from the retina are heterogeneous because there are 3 types of pigments for the cone cells and 2 types of bipolar cells.. This results in a complex pattern even when you deal with a very uniform input pattern (say watching a white piece of paper ). Having a heterogeneous signal works while a homogeneous signal leads to “bad timing” .. close by neurons receiving exactly the same signal cannot form connections among themselves. I have a work around for this but I don’t like it because what I see as input is not the real input (the white sheet of paper, say), I need to imagine the input… that makes understanding very difficult.
  • Small input going into a bigger second layer.. I’ve procrastinated on this. I can create layers of different dimensions but neurons don’t bind with some sort of step. I need to have neurons that don’t get direct connections from the lower layer, or even if they still bind I need to have more neurons in the second layer to act as signal amplifier ..

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