Neural networks in models and in reality

I have recently read a modern book on neural systems in biology and found a lot of misconceptions between current models and real systems.

At first, real neurons use both inhibitory (negative, -) and motor (positive, +) actions, that corresponds to both negative and positive neuronal weights (between -1 and 1). But it seems like in lots of models neurons use only motor actions in range (0;1).

Also it looks like real neural systems are predefined by design using genes. For example, all sensory data (audio, visual and somatosensory) use the same pathways – at first to talamus, then to primary cortex areas (like V1 for vision) using the same pattern between standard 6 neuronal layers in cortex. This talamus-cortex path pattern always send (+) data to layer #4, it sends to processing layers 1-3. Layers 1-3 sends (+) data to layer #5 that resends it (+) to layer #6, which in turn is able to send both (+,-) data to layer #4 and back to talamus (regulating feedback).

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