The connectome of an insect brain

Abstract

Brains contain networks of interconnected neurons, so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an insect brain (Drosophilalarva) with rich behavior, including learning, value-computation, and action-selection, comprising 3,013 neurons and 544,000 synapses. We characterized neuron-types, hubs, feedforward and feedback pathways, and cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled powerful machine learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.

Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome

“…For instance, the probability of a connection from a neuron in group k to a neuron in group l is set by the parameter B kl , where B is a K × K matrix of connection probabilities if there are K groups. Here, we used broad cell type categorizations from Winding et al [14] to determine each neuron's group. Alternatively, there are many methods for estimating these assignments to groups for each neuron which we do not explore here (see Section 3.2 for discussion on this point).…”

“…Further, a rich literature exists on inferring the partition for an SBM from the observed connectivity [32][33][34][35][36][37] -this is one perspective for clustering neurons based on their observed connectivity, much like clustering procedures are used to predict meaningful groups of neurons based on morphology, activity, or gene expression. Applying these techniques to a connectome would yield alternative groupings of neurons (as in Winding et al [14]) to use for a group connection test, which again, could change its conclusions. However, this approach requires further study, as it introduces a new source of uncertainty since more model parameters are estimated from the data.…”

“…Here, we explain how we generated networks for the bilateral symmetry comparison. We started from a network of all neurons in the brain and sensory neurons which project into it for a larval Drosophila [14]. As in Winding et al [14], we removed neurons which were considered partially differentiated.…”

“…We started from a network of all neurons in the brain and sensory neurons which project into it for a larval Drosophila [14]. As in Winding et al [14], we removed neurons which were considered partially differentiated. From this network, we selected only the left-to-left (ipsilateral) induced subgraph, and likewise for the right-to-right.…”

“…From this network, we selected only the left-to-left (ipsilateral) induced subgraph, and likewise for the right-to-right. We ignored a pair of neurons which had no left/right designation, as their cell bodies lie on the midline [14]. To ensure we had fully connected networks on either hemisphere, we took the largest weakly connected component of neurons on the left, and likewise on the right.…”