Abstract
Connectomics, the study of circuit architecture, has the potential to reveal the connectivity of any brain or brain area with single-synapse resolution. This is extremely exciting but at the same time quite daunting. The exciting part is obvious. The daunting part is less so, and relates to the challenge of extracting principles from overwhelming masses of high-resolution data. You might say that it is a nice problem to have, and I will agree. What I will argue here is that, if our goal is to derive from such data a general and theoretical understanding of the brain, we must now more than ever take advantage of comparative approaches.
Highlights
The brain, like any biological entity, is a product of natural evolution
Yakov Frenkel, the famous 20th century Russian scientist, advocated model diversity in his own domain, condensed matter physics, in these terms: A good theoretical model of a complex system should be like a good caricature: it should emphasize those features which are most important and should downplay the inessential details
Summary
The brain, like any biological entity, is a product of natural evolution. This statement applies to the brain of humans as it does to that of flies or redeyed tree frogs. A comparative approach is useful in that it forces us to identify, or at least be explicit about, the features that matter to reach a functional understanding This laboratory has worked extensively with insect nervous systems in the past and some of this work has led to the description of computational phenomena, such as cellular multiplication for looming detection in vision [2, 3] that have an algorithmic equivalent in thalamic neurons of birds [13]. It is not that one aspect is more or less interesting than the other: details of implementation will obviously matter if your goal is therapeutics development for human medicine These examples illustrate the value of diversity in model systems and approaches for modern neuroscience, precisely because they enable us to separate implementation detail from function and computation. The diversity of mammalian early visual system cortical architecture, for example, suggests that some macroscopic structural attributes identified in original experimental studies (e. g., maps of orientation in cat or macaque primary visual cortex) may not be a general rule [7]
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