3,013 neurons, half a million synapses: the complete #connectome of the whole #Drosophila larval brain!
Winding, Pedigo et al. 2022. "The connectome of an insect brain" https://www.biorxiv.org/content/10.1101/2022.11.28.516756v1
We’ve mapped and analysed its circuit architecture, from sensory neurons to brain output neurons, as reconstructed from volume electron microscopy, and here is what we found. 1/
Our map of the #Drosophila larval brain #connectome is complete, with all inputs and all outputs, and everything in between: all polysynaptic pathways from sensory neurons all the way to brain output neurons, across both brain hemispheres. 2/
Our analysis of the #Drosophila larval brain starts by recognizing that neurons are polarized: 95.5% of all brain neurons present clearly segregated axons and dendrites.
In the #connectome, we found 66% axo-dendritic synapses, 26% axo-axonic, 6% dendro-dendritic and 2% dendro-axonic.
This matters because inputs onto dendrites contribute to the integration function of a neuron; inputs onto an axon modulate its output. Analysing them separately makes sense.
Having split the #Drosophila larval brain #connectome into 4 types of edges, hierarchical spectral clustering defined about 90 groups of neurons.
Remarkably, clusters defined by connectivity alone were internally consistent for other features, such as neuron morphology or function.
Clusters were sorted from sensory neurons (SNs) to descending neurons (DNs) using the Walk-Sort algorithm. To the right, example clusters with intracluster morphological similarity score using NBLAST.
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Next, we explored the #Drosophila larval #connectome with multi-hop signal cascades (left) that extended across synapses up to a depth of 5. We sorted neurons into labelled lines and multisensory (right).
Neurons were considered to receive sensory input when visited in most cascade iterations.
The majority of brain neurons integrate from all sensory types, but a few neurons integrated from only one sensory modality (labelled line) or from a combination.
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Then we studied recurrent circuits in the #Drosophila larval brain.
By starting bi-directional multi-hop signal cascades at any one cluster, we found that the cluster containing the dopaminergic neurons (DANs) of the centre for associative learning and memory, the insect mushroom body (MB), present the most cascades where the beginning and the end of the cascade is itself!
In other words DANs, which mediate learning, are the most recurrent neurons in the brain.
With all descending neurons (DNs) mapped, we could have a look at how the #Drosophila larval brain drives locomotion.
By determining the spatial projection pattern of all axons of DNs, and the known contribution of each body segment to locomotion, we inferred which behaviours can be controlled by which DNs, and then, which brain neurons control those DNs.
Artificial neural networks work in a very different way to biological ones. For one, it takes a deep neural network to emulate the capabilities of a single pyramidal neuron in the mammalian cortex. And ANNs lack axo-axonic synapses, active dendritic spikes, redundant inputs across different dendritic branches, and more. All of which matter a lot and are the subject of a number of scientific publications. The differences are huge. Not at all comparable beyond: both are networks.
@alex_p_roe For the time being I am content with mapping brain circuits and making sense of them through a combination of genetics, functional imaging, observation of behavioural perturbations, and computational modeling. All of this is possible in a tiny organism, and not at all on a large one, at least, not if one has the ambition of studying the complete brain at nanometre resolution.
As per the "hallucinations", large language models don't hallucinate. Instead, they are simply statistical models of language, and therefore what they generate is what is plausible, given a corpus of texts it was trained on. It does not reason. They also fall prey to the curse of dimensionality, where the higher the number dimensions, the more "empty space" – regions of the activation space where nothing makes sense – there is.
Artificial general intelligence would require at the very least explicitly modeling the world and predicting future trajectories for objects in it. At the very least. Indeed, not understanding yet how specific functions such as wayfinding/navigation, learning in its many forms, and perception of the own body work in the brain, there's little hope so far for a constructed system to do so, beyond very limited use cases in highly controlled and constrained environments.
Albert Cardona
Alex P Roe 