Abstract
We approach the C. elegans connectome as an information processing network that receives input from about 90 sensory neurons, processes that information through a highly recurrent network of about 80 interneurons, and it produces a coordinated output from about 120 motor neurons that control the nematode’s muscles. We focus on the feedforward flow of information from sensory neurons to motor neurons, and apply a recently developed network analysis framework referred to as the “hourglass effect”. The analysis reveals that this feedforward flow traverses a small core (“hourglass waist”) that consists of 10-15 interneurons. These are mostly the same interneurons that were previously shown (using a different analytical approach) to constitute the “rich-club” of the C. elegans connectome. This result is robust to the methodology that separates the feedforward from the feedback flow of information. The set of core interneurons remains mostly the same when we consider only chemical synapses or the combination of chemical synapses and gap junctions. The hourglass organization of the connectome suggests that C. elegans has some similarities with encoder-decoder artificial neural networks in which the input is first compressed and integrated in a low-dimensional latent space that encodes the given data in a more efficient manner, followed by a decoding network through which intermediate-level sub-functions are combined in different ways to compute the correlated outputs of the network. The core neurons at the hourglass waist represent the information bottleneck of the system, balancing the representation accuracy and compactness (complexity) of the given sensory information.
Highlights
Natural, technological and information-processing complex systems are often hierarchically modular [1, 2, 3, 4]
The C. elegans nematode is the only species for which the complete wiring diagram (“connectome”) of its neural system has been mapped
The connectome provides architectural constraints that limit the scope of possible functions of a neural system
Summary
Technological and information-processing complex systems are often hierarchically modular [1, 2, 3, 4]. Modularity and hierarchy are often viewed as essential principles that provide benefits in terms of design effort (compared to “flat” or “monolithic” designs in which the entire system is a single module), development cost (design a module once, reuse it many times), and agility (upgrade, modify or replace modules without affecting the entire system) [14, 15, 16]. The benefits of modularity and hierarchy are often viewed in terms of evolvability (the ability to adapt and develop novel features can be accomplished with minor modifications in how existing modules are interconnected) [17, 18, 19] and robustness (the ability to maintain a certain function even when there are internal or external perturbations can be accomplished using existing modules in different ways) [20, 21, 22]
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