> Current models based on biological principals are starting at a level of complexity which is emergent from a more foundational level which is ignored in our currently best performing models.
Not really. It's too complex, we understand too little, and there's very little reason for it, since intelligence is not a property which is encoded in a neuron, but an emergent property, emerging from the interactions between them and the development of the network.
It's a bad idea for the same reason that if you want to simulate the aerodynamic properties of an airplane, you don't start with string theory.
> Individual units within biological networks can individually adjust their behavior, based on external inputs both from other cells in the network and external to whole network (environmental) inputs.
You don't need to start from the cellular level to achieve this though.
Not really. It's too complex, we understand too little, and there's very little reason for it, since intelligence is not a property which is encoded in a neuron, but an emergent property, emerging from the interactions between them and the development of the network.
It's a bad idea for the same reason that if you want to simulate the aerodynamic properties of an airplane, you don't start with string theory.
> Individual units within biological networks can individually adjust their behavior, based on external inputs both from other cells in the network and external to whole network (environmental) inputs.
You don't need to start from the cellular level to achieve this though.