The availability of multiple connectomes from behaviourally different species will reveal features of the network design that are common to all, and that enable comparison with networks from different levels of biological organization, as well as with those from networks that have evolved from human technologies.
Ich würde argumentieren, dass wir die Vielfalt, welche durch die Evolution bereitgestellt wird, nutzen sollten, um unser Denken über neuronale Schaltkreise und die Signalverarbeitungslogik ihrer Mikroarchitektur zu befördern und die zugrunde liegenden Regeln ihrer Operationen zu identifizieren.
The brain has been shaped by evolution, and its connectome reflects that history. Comparative neuroscience research, framed by evolutionary relationships, is key to interpreting connectome organization and can address fundamental circuit questions that are not accessible through single-species connectomics efforts.
Martijn P. van den Heuvel, Edward T. Bullmore, Olaf Sporns
Studies consistently report pronounced community structure, short communication paths, and the formation of hubs and rich clubs, features that appear to be universal across many species. … Within the space of wiring possibilities that conform to these common principles, we argue that differences in connectome organization between closely related species support adaptations in cognition and behavior.
Nicholas J. Strausfeld, Irina Sinakevitch, Sheena M. Brown, Sarah M. Farris
This account compares four types of insect brains, representing key phylogenetic positions and behavioral ecologies, to demonstrate that irrespective of whether or not mushroom bodies are equipped with calyces their lobes reveal a common ground plan of organization.
Six hundred million years of metazoan evolution have generated many solutions to comparable neurobiological challenges, all constrained by the common physics of our environment. …. Informed comparisons can help us separate implementation details — interesting in and of themselves and essential for practical applications, such as in human and animal medicine — from computational and algorithmic principles, those that will ultimately help us derive a theory of the brain.
Both cellular and systems neuroscience are making steady progress, but the critical bridge between them, understanding how large numbers of neurons organize themselves into functional networks, is still unbuilt. The transition from being able to image one or two neurons at a time to being able to digitize whole multi-neuronal networks may be the solution.