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WILL YOU EVER BE ABLE TO UPLOAD YOUR BRAIN?

By KENNETH D. MILLER, a professor of neuroscience at Columbia and a co-director of the Center for Theoretical Neuroscience.


* * *

SOME hominid along the evolutionary path to humans was probably the first animal with the cognitive ability to understand that it would someday die. To be human is to cope with this knowledge. Many have been consoled by the religious promise of life beyond this world, but some have been seduced by the hope that they can escape death in this world. Such hopes, from Ponce de León’s quest to find a fountain of youth to the present vogue for cryogenic preservation, inevitably prove false.

In recent times it has become appealing to believe that your dead brain might be preserved sufficiently by freezing so that some future civilization could bring your mind back to life. Assuming that no future scientists will reverse death, the hope is that they could analyze your brain’s structure and use this to recreate a functioning mind, whether in engineered living tissue or in a computer with a robotic body. By functioning, I mean thinking, feeling, talking, seeing, hearing, learning, remembering, acting. Your mind would wake up, much as it wakes up after a night’s sleep, with your own memories, feelings and patterns of thought, and continue on into the world.

I am a theoretical neuroscientist. I study models of brain circuits, precisely the sort of models that would be needed to try to reconstruct or emulate a functioning brain from a detailed knowledge of its structure. I don’t in principle see any reason that what I’ve described could not someday, in the very far future, be achieved (though it’s an active field of philosophical debate). But to accomplish this, these future scientists would need to know details of staggering complexity about the brain’s structure, details quite likely far beyond what any method today could preserve in a dead brain.

How much would we need to know to reconstruct a functioning brain? Let’s begin by defining some terms. Neurons are the cells in the brain that electrically carry information: Their electrical activity somehow amounts to your seeing, hearing, thinking, acting and all the rest. Each neuron sends a highly branched wire, or axon, out to connect or electrically “talk” to other neurons. The specialized connecting points between neurons are called synapses. Memories are commonly thought to be largely stored in the patterns of synaptic connections between neurons, which in turn shape the electrical activities of the neurons.

Much of the current hope of reconstructing a functioning brain rests on connectomics: the ambition to construct a complete wiring diagram, or “connectome,” of all the synaptic connections between neurons in the mammalian brain. Unfortunately connectomics, while an important part of basic research, falls far short of the goal of reconstructing a mind, in two ways. First, we are far from constructing a connectome. The current best achievement was determining the connections in a tiny piece of brain tissue containing 1,700 synapses; the human brain has more than a hundred billion times that number of synapses. While progress is swift, no one has any realistic estimate of how long it will take to arrive at brain-size connectomes. (My wild guess: centuries.)

Second, even if this goal were achieved, it would be only a first step toward the goal of describing the brain sufficiently to capture a mind, which would mean understanding the brain’s detailed electrical activity. If neuron A makes a synaptic connection onto neuron B, we would need to know the strength of the electrical signal in neuron B that would be caused by each electrical event from neuron A. The connectome might give an average strength for each connection, but the actual strength varies over time. Over short times (thousandths of a second to tens of seconds), the strength is changed, often sharply, by each signal that A sends. Over longer times (minutes to years), both the overall strength and the patterns of short-term changes can alter more permanently as part of learning. The details of these variations differ from synapse to synapse. To describe this complex transmission of information by a single fixed strength would be like describing air traffic using only the average number of flights between each pair of airports.

Underlying this complex behavior is a complex structure: Each synapse is an enormously complicated molecular machine, one of the most complicated known in biology, made up of over 1,000 different proteins with multiple copies of each. Why does a synapse need to be so complex? We don’t know all of the things that synapses do, but beyond dynamically changing their signal strengths, synapses may also need to control how changeable they are: Our best current theories of how we store new memories without overwriting old ones suggest that each synapse needs to continually reintegrate its past experience (the patterns of activity in neuron A and neuron B) to determine how fixed or changeable it will be in response to the next new experience. Take away this synapse-by-synapse malleability, current theory suggests, and either our memories would quickly disappear or we would have great difficulty forming new ones. Without being able to characterize how each synapse would respond in real time to new inputs and modify itself in response to them, we cannot reconstruct the dynamic, learning, changing entity that is the mind.

But that’s not all. Neurons themselves are complex and variable. Axons vary in their speed and reliability of transmission. Each neuron makes a treelike branching structure that reaches out to receive synaptic input from other neurons, as a tree’s branches reach out to sunlight. The branches, called dendrites, differ in their sensitivity to synaptic input, with the molecular composition as well as shape of a dendrite determining how it would respond to the electrical input it receives from synapses.

Nor are any of these parts of a living brain fixed entities. The brain’s components, including the neurons, axons, dendrites and synapses (and more), are constantly adapting to their electrical and chemical “experience,” as part of learning, to maintain the ability to give appropriately different responses to different inputs, and to keep the brain stable and prevent seizures. These adaptations depend on the dynamic molecular machinery in each neural structure. The states of all of these components are constantly being modulated by a wash of chemicals from brainstem neurons that determine such things as when we are awake or attentive and when we are asleep, and by hormones from the body that help drive our motivations. Each element differs in its susceptibility to these influences.

To reconstruct a mind, perhaps one would not need to replicate every molecular detail; given enough structure, the rest might be self-correcting. But an extraordinarily deep level of detail would be required, not only to characterize the connectome but also to understand how the neurons, dendrites, axons and synapses would dynamically operate, change and adapt themselves.

I don’t wish to suggest that only hopelessly complicated models of the brain are useful. Quite the contrary. Our most powerful theoretical research tools for understanding brain function are often enormously simplified models of small pieces of the brain — for example, characterizing synapses by a single overall strength and ignoring dendritic structure. I make my living studying such models. These simple models, developed in close interaction with experimental findings, can reveal basic mechanisms operating in brain circuits. Adding complexity to our models does not necessarily give us a more realistic picture of brain circuits because we do not know enough about the details of this complexity to model it accurately, and the complexity can obscure the relationships we are trying to grasp. But far more information would be needed before we could characterize the dynamic operation of even a generic whole brain. Capturing all of the structure that makes it one person’s individual mind would be fantastically more complicated still.

Neuroscience is progressing rapidly, but the distance to go in understanding brain function is enormous. It will almost certainly be a very long time before we can hope to preserve a brain in sufficient detail and for sufficient time that some civilization much farther in the future, perhaps thousands or even millions of years from now, might have the technological capacity to “upload” and recreate that individual’s mind.

I certainly have my own fears of annihilation. But I also know that I had no existence for the 13.8 billion years that the universe existed before my birth, and I expect the same will be true after my death. The universe is not about me or any other individual; we come and we go as part of a much larger process. More and more I am content with this awareness. We all find our own solutions to the problem death poses. For the foreseeable future, bringing your mind back to life will not be one of them.

Source.

So, with that complexity of the human brain, it looks like it should be far easier to create an accurate model/simulation of our Milky Way galaxy with all its stars and their planets and their rings and satellites. Because there are much fewer of them, than synapses in the human brain, and they interact in a far simpler way...

The professor, being obviously a smart guy, captures the exact basic cause of all this cryonics/uploading idiocy. Namely, the fear of death, same source as all religions have. Fine, how he kind of puts his whole article in brackets with this problem, providing the simplest and the only reasonable solution to it in the end.

Date: 2017-03-10 06:45 (UTC)
ext_1723685: (Default)
From: [identity profile] balansirovschik.livejournal.com
Я бы не был так пессимистичен (хотя в загрузку сознания в том виде, как это себе представляют тупые айтишнеги, тоже не верю). Чтобы что-то воспроизвести или использовать, не всегда обязательно в деталях знать, как это работает. Вот атомную бомбу и атомную же электростанцию запилили, почти ничего не зная о механизме сильного взаимодействия, например. Те же айтишнеги сейчас успешно паразитируют на нейронных сетях, несмотря на то что средний гопник понимает в биологии и матане больше, чем средний айтишнег.

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