Connectome scanning continues to scale up drastically, particularly on fruit flies. davidad highlights some very recent work:
I could be wrong, but we're still currently unable to get that original C. elegans neural map to do anything (like run a simulated worm body), right?
I think @AndrewLeifer is almost there but, yes, still hasn’t gone all the way to a demonstration of behavior in a virtual environment: "A11.00004 : A functional connectivity atlas of C. elegans measured by neural activation".
Neural processing and dynamics are governed by the details of how neural signals propagate from one neuron to the next through the brain. We systematically measured functional properties of neural connections in the head of the nematode Caenorhabditis elegans by direct optogenetic activation and simultaneous calcium imaging of 10,438 neuron pairs. By measuring responses to neural activation, we extracted the strength, sign, temporal properties, and causal direction of the connections and created an atlas of causal functional connectivity.
We find that functional connectivity differs from predictions based on anatomy, in part, because of extrasynaptic signaling. The measured properties of the connections are encoded in kernels which describe signal propagation in the network and which we fit from the data. Using such kernels, we can run numerical simulations of neural activity in the worm's brain using exclusively information that comes from the data, as opposed to simulations based on the anatomical connectome which require assumptions on many parameters.
We show that functional connectivity better predicts spontaneous activity than anatomy, suggesting that functional connectivity captures properties of the network that are critical for interpreting neural function.
An important feature of his work is that he’s shown functional connectivity is not only strictly more informative than anatomical connectomics, but that simulations actually get worse when incorporating connectomic constraints (because e.g. hormones work external to synapses).
The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain.
Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors.
We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes.
Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.
We can now measure the connectivity of every neuron in a neural circuit, but we are still blind to other biological details, including the dynamical characteristics of each neuron. The degree to which connectivity measurements alone can inform understanding of neural computation is an open question.
Here we show that with only measurements of the connectivity of a biological neural network, we can predict the neural activity underlying neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the motion pathways of the fruit fly optic lobe but with unknown parameters for the single neuron and single synapse properties. We then optimized the values of these unknown parameters using techniques from deep learning, to allow the model network to detect visual motion.
Our mechanistic model makes detailed experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 24 studies.
Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements. We show that this strategy is more likely to be successful when neurons are sparsely connected—a universally observed feature of biological neural networks across species and brain regions.
Another report of progress: Mapping the Mind: Worm’s Brain Activity Fully Decoded (full paper).
Since the early 21st century, some transhumanist proponents and futuristic researchers claim that Whole Brain Emulation (WBE) is not merely science fiction - although still hypothetical, it's said to be a potentially viable technology in the near future. Such beliefs attracted significant fanfare in tech communities such as LessWrong.
In 2011 at LessWrong, jefftk did a literature review on the emulation of a worm, C. elegans, as an indicator of WBE research progress.
There were three research projects from the 1990s to the 2000s, but all are dead-ends that were unable to reach the full research goals, giving a rather pessimistic vision of WBE. However, immediately after the initial publication of that post, LW readers Stephen Larson (slarson) & David Dalrymple (davidad) pointed out in the comments that they were working on it, the two ongoing new projects of their own made the future look promising again.
The first was the OpenWorm project, coordinated by slarson. Its goal is to create a complete model and simulation of C. elegans, and to release all tools and data as free and open source software. Implementing a structural model of all 302 C. elegans neurons in the NeuroML description language was an early task completed by the project.
The next was another research effort at MIT by davidad. David explained that the OpenWorm project focused on anatomical data from dead worms, but very little data exists from living animals' cells. They can't tell scientists about the relative importance of connections between neurons within the worm's neural system, only that a connection exists.
In a year or two, he believed an automated device can be built to gather such data. And he was confident.
When asked by gwern for a statement for PredictionBook.com, davidad said:
(disappointingly, these statements were not actually recorded on PredictionBook).
Unfortunately, 10 years later, both projects appear to have made no significant progress and failed to develop a working simulation that is able to resemble biological behaviors. In a 2015 CNN interview, slarson said the OpenWorm project was "only 20 to 30 percent of the way towards where we need to get", and seems to be in the development hell forever since. Meanwhile, I was unable to find any breakthrough from davaidad before the project ended. David personally left the project in 2012.
When the initial review was published, there was already 25 years of works on C. elegans, and right now yet another decade has passed, yet we're still unable to "upload" a nematode. Therefore, I have to end my post with the pessimistic vision of WBE by quoting the original post.
This is discouraging.
Closing thoughts: What went wrong? What are the unsolvable difficulties here?
Update
Some technical insights behind the failure was given in a 2014 update ("We Haven't Uploaded Worms"), jefftk showed the major problems are:
The best we can do is modeling a generic worm - pretraining and running the neural network with fixed weights. Thus, no worm is "uploaded" because we can't read the weights, and these simulations are far from realistic because they are not capable of learning. Hence, it's merely a boring artificial neural network, not a brain emulation.
Furthermore, in a Quora answer, davidad hinted that his project was discontinued partially due to the lack of funding.
Conclusion: Relevant neural recording technologies are needed to collect data from living worms, but they remain undeveloped, and the funding simply isn't there.
Update 2
I just realized David actually had an in-depth talk about his work and the encountered difficulties at MIRI's AI Risk for Computer Scientists workshop in 2020, according to this LW post ("AIRCS Workshop: How I failed to be recruited at MIRI").
Does anyone know any additional information? Is the content of that talk available in paper form?
Update 3
Note to the future readers: within a week of the initial publication of this post, I received some helpful insider comments, including David himself, on the status of this field. The followings are especially worth reading.