Unfortunately, not. We get the graph of the connections, but there are tons of essential parameters that are not captured. Such as the synaptic weights, the complex non-linear dynamics of the real neurons, their intricate modulation by various chemicals, etc.
For example, after the connectome of the worm were finished, despite it being quite small, for many years it proved to be impossible to simulate the dynamics, because of so many unknown parameters.
This was one of the criticisms that the opponents of connectomics have always brought up. "You spend a lot of money that could have been used for other research, but in the end you do not get a true insight into how the brain really works." For the researchers who thought that knowing all the connections was important, it was an uphill battle to overcome such attitudes.
But one has to start somewhere -- like a genome, the connectome is not the whole story, but it is a very important part of it, on which many other advances can be built up.
> after the connectome of the worm were finished, despite it being quite small, for many years it proved to be impossible to simulate the dynamics, because of so many unknown parameters.
Apparently they have been able to simulate dynamics with the fruit fly connectome(?) [0]:
> researchers used the connectome to create a computer model of the entire fruit-> fly brain, including all the connections between neurons. They tested it by activating neurons that they knew either sense sweet or bitter tastes. These neurons then launched a cascade of signals through the virtual fly’s brain, ultimately triggering motor neurons tied to the fly’s proboscis — the equivalent of the mammalian tongue. When the sweet circuit was activated, a signal for extending the proboscis was transmitted, as if the insect was preparing to feed; when the bitter circuit was activated, this signal was inhibited. To validate these findings, the team activated the same neurons in a real fruit fly.
The researchers have taken a very simple idealized mathematical model of a neuron, assumed that all synaptic weights were the same, ignored modulation, ignored base level inhibitory inputs, and have shown that even in such a crude setting, for some important inputs (especially for a taste of sugar) the "logic" of how the inputs result in the activation of certain outputs still works, based on the connectome information alone.
This is certainly very cool. But as the authors themselves point out [1], much more work remains to be done to reproduce more subtle features of the dynamics of the system.
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.
Depends what you mean by "modeled". You can probably create a visualization of it, but the data doesn't include any information about the dynamics of the system, how the neurons behave. So, you can't "simulate a brain" to any extent with this data, if that's what you were thinking.
