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Organization of Information-Processing Biological Networks

  • Author(s): How, Javier Josue
  • Advisor(s): Navlakha, Saket
  • et al.
Abstract

Despite considerable effort over the past several decades, it is unclear how an organism’s nervous system extracts ecologically relevant information about its environment. The connections between individual neurons (i.e. the structural connectome), and the ways in which these connections are used (i.e. the functional connectome), likely constrain how a nervous system performs this feat. The nematode Caenorhabditis elegans (C. elegans) presents a rare opportunity to study both of these aspects of a nervous system, since the complete structural connectome of one individual has been published, and recent technological advances enable scientists to record from a large part of its nervous system. I studied the C. elegans nervous system from both of these perspectives to better understand the structural features and network-level dynamics that allow it to extract information about the environment.

I found that the C. elegans structural connectome, along with those of several other biological networks, display Rentian scaling – a power-law relationship between the number of nodes in a module and the number of connections to nodes in other modules. This indicates that these biological networks, but not other social and technological networks, must negotiate a trade-off between the efficiency of information transfer and cost to maintain connections. Thus, Rentian scaling may be a feature unique to information-processing networks, either because it presents an organizational principle used to process information, or because it indicates the existence of fundamental design constraints these networks share.

While a structural connectome constrains which neurons may communicate, a functional connectome can indicate which pathways of information transfer are actually used, with the caveat that statistical dependencies may reveal interactions that are actually false positives. I recorded from most neurons in the brain of adult C. elegans, and characterized their inferred functional connectome using tools from graph theory. I found that some patterns of activity were consistently modulated in a valence- or identity-specific manner. Moreover, these patterns could be used to identify which chemical an animal was experiencing. This indicates that studying the functional connectome of large-scale neural recordings may provide a mechanistic account of how neural networks extract and represent information about their environment.

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