This means there is no magic mystical difference between a real fly’s brain and a virtualized one.
That’s not true. You might simulate fly intelligence, but you aren’t simulating a fly. Flys are an entity driven by their phenomenological experience of the world (as all us nervous systems are). It would be a rather strange thing to say that a fly is only the pattern of behavior you recognize as a fly’s behavior.
Note that flies also have this capacity to self-evolve over generations. There is no single fly that you can point to and say, “that’s the right one. Copy
It.” So, your “fly simulations” are always at best a behavioral approximation. Given enough time, say a decade or century, this ought be obvious by the fact that your simulation no longer accurately resembles the most modern flies.
Projecting further, there is no difference, other than scale, between our brain and the fly.
That’s just not true. Consider “What The Frog Eye Tells The Frog Brain.” Put briefly, the eye encodes and transmits semantic information — as opposed to the more common belief that it transmits raw visual information. That said, there a trillions of differences between how that might work in a human versus in a frog, let alone a fly.
I think you’ve discovered “neurons work similarly across species,” which is like saying “thing does same thing when used in other location.” This doesn’t tell you how neurons work to drive that behavior, it doesn’t tell you what it’s like to experience your neurons firing that way, it doesn’t tell you why the neurons were developed that way topologically over time… At best, it helps you develop a timebound understanding of fly automata to neural architecture. That’s without any understanding of phenomenology to neural architecture, and likely without being able to decompose the neural system into any semblance of semantic information processing.
A human is much more complex than a fly. I can’t believe for a second that this approach would work scale to a human’s brain. If you captured and simulated every neuron in the human brain, you’d be left with a feedforward process simulating a particular neural state of a particular person. No feeling, no reflection, no introspection, no semantic meaning. Just a neat toy.
That’s not true. You might simulate fly intelligence, but you aren’t simulating a fly. Flys are an entity driven by their phenomenological experience of the world (as all us nervous systems are). It would be a rather strange thing to say that a fly is only the pattern of behavior you recognize as a fly’s behavior.
Note that flies also have this capacity to self-evolve over generations. There is no single fly that you can point to and say, “that’s the right one. Copy It.” So, your “fly simulations” are always at best a behavioral approximation. Given enough time, say a decade or century, this ought be obvious by the fact that your simulation no longer accurately resembles the most modern flies.
That’s just not true. Consider “What The Frog Eye Tells The Frog Brain.” Put briefly, the eye encodes and transmits semantic information — as opposed to the more common belief that it transmits raw visual information. That said, there a trillions of differences between how that might work in a human versus in a frog, let alone a fly.
I think you’ve discovered “neurons work similarly across species,” which is like saying “thing does same thing when used in other location.” This doesn’t tell you how neurons work to drive that behavior, it doesn’t tell you what it’s like to experience your neurons firing that way, it doesn’t tell you why the neurons were developed that way topologically over time… At best, it helps you develop a timebound understanding of fly automata to neural architecture. That’s without any understanding of phenomenology to neural architecture, and likely without being able to decompose the neural system into any semblance of semantic information processing.
A human is much more complex than a fly. I can’t believe for a second that this approach would work scale to a human’s brain. If you captured and simulated every neuron in the human brain, you’d be left with a feedforward process simulating a particular neural state of a particular person. No feeling, no reflection, no introspection, no semantic meaning. Just a neat toy.