21–30 of 208 posts
Are all fruit fry brains the same? Does anyone know what has actually been mapped and why it would generalize from one fruit fly to the next?
Then you analyze the slice images and determine the neurons and their connection. This is the hard part, and the breakthrough is an AI based method.
Pretty sure they've only mapped one brain so far.
Earlier quoted context omitted.
It's my (layman) understanding that it's more or less a wiring diagram. Synapse #8217492 connects neuron #27472 and neuron #27865. It's a graph with 140,000 nodes (neurons) and 54.5 million edges (synapses). And then some labels for them like neurotransmitter type, which class of brain operations they're associated with, its size and position in 3D, etc. They have a cool website that lets you browse the data: https:/…
Is the data such that it can be modeled in software?
It was my understanding that all this connectome-based research was largely a deadend, because it doesnt capture dynamics, nor a vast array of interactions. if you've ever seen neurones being grown (go search YT), you'll see it's a massive gelatinous structure which is highly plastic and highly dynamic. Even in the simplest brains (eg., of elgans), you get 10^x exponential growth in number of neurones and their conne…
"human brains could follow" feels like a few jumps ahead? a fruit fly has on the order of 100k neurons, a human brain has on the order of 100 billion neurons. that's 6 orders of magnitude larger. that's like saying "A map of San Francisco has been completed, the entire solar system could follow!"
The amount of data may mean we have to wait for Moore's Law to keep improving things for a while though.
It was my understanding that all this connectome-based research was largely a deadend, because it doesnt capture dynamics, nor a vast array of interactions. if you've ever seen neurones being grown (go search YT), you'll see it's a massive gelatinous structure which is highly plastic and highly dynamic. Even in the simplest brains (eg., of elgans), you get 10^x exponential growth in number of neurones and their conne…
It is like getting a static map of the country's roads with no cars on it.
You can not make it come alive with cars (activity), but you can infer where people need to drive but you don't know when and why they drive or what they are doing, but it is a major clue.
It was my understanding that all this connectome-based research was largely a deadend, because it doesnt capture dynamics, nor a vast array of interactions. if you've ever seen neurones being grown (go search YT), you'll see it's a massive gelatinous structure which is highly plastic and highly dynamic. Even in the simplest brains (eg., of elgans), you get 10^x exponential growth in number of neurones and their conne…
It is useful. It is like getting a static map of the country's roads with no cars on it. You can not make it come alive with cars (activity), but you can infer where people need to drive but you don't know when and why they drive or what they are doing, but it is a major clue.
I was thinking it was more like giving somebody iPhone schematics and die shots of all the chips and then asking them to figure out how Portrait Mode works in the Camera app.
It was my understanding that all this connectome-based research was largely a deadend, because it doesnt capture dynamics, nor a vast array of interactions. if you've ever seen neurones being grown (go search YT), you'll see it's a massive gelatinous structure which is highly plastic and highly dynamic. Even in the simplest brains (eg., of elgans), you get 10^x exponential growth in number of neurones and their conne…
One of the main open questions in neuroscience right now is how network structure, dynamics, and function are related in the brain. Connectomes provide tremendous insight into structure, but as mentioned this does not generically solve either the dynamics or function problem. For example, for many of these neurons we don't have a good understanding of their input-output relationship, and the nature of this relationship can strongly affect the dynamics that emerge in a highly connected network. Individual variability across connectomes, and how connectomes change over development are also a significant issue, but at least for the fly it's thought that many of the basic structures are pretty conserved across adult animals, even if many of the details could differ.
Modulo these caveats, knowing the physical network structure of the brain does still impose huge constraints on what kinds of models we should be using for gaining insight into dynamics and function. For example, there are well known areas (the "mushroom bodies") with specific feed-forward connectivity patterns that are very different from a random recurrent network. Further, there are at least some areas in the fly brain where we think there are indeed quite clean structure-function relationships, e.g. in the central complex of the fly brain, which contains a physical ring of neurons and is thought to support a "bump" of activity that acts as a sort of compass that helps flies navigate via a ring-attractor-like dynamical system. Thus, even though it has many missing pieces, a wiring diagram like this can be tremendously useful for generating hypotheses to guide more targeted experiments and theoretical studies.
Simulated too? I assume that if you can map it then you can simulate it. Am I correct?