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.
Connectome scanning continues to scale up drastically, particularly on fruit flies. davidad highlights some very recent work:
Also relevant: “A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing”, Shiu et al 2023:
“Connectome-constrained deep mechanistic networks predict neural responses across the fly visual system at single-neuron resolution”, Lappalainen et al 2023:
Another report of progress: Mapping the Mind: Worm’s Brain Activity Fully Decoded (full paper).