Robustness and fault tolerance make brains harder to study
Shyam Srinivasan and Charles F Stevens
BMC Biology 2011, 9:46 | doi:10.1186/1741-7007-9-46
Abstract:
Brains increase the survival value of organisms by being robust and fault tolerant. That is, brain circuits continue to operate as the organism needs, even when the circuit properties are significantly perturbed. Kispersky and colleagues, in a recent paper in Neural Systems & Circuits, have found that Granger Causality analysis, an important method used to infer circuit connections from the behavior of neurons within the circuit, is defeated by the mechanisms that give rise to this robustness and fault tolerance.
Concluding paras:
Invertebrates have many pattern generation networks. This same problem would be expected to arise in any of them because they all have been designed to keep working even when something goes wrong with the network. One might ask whether this is a problem unique to invertebrates who have very simple (numerically, at least) neural circuits. Actually, the problem is likely to be worse in the vertebrate brain because vertebrates rely on redundant neurons in their circuits to achieve fault tolerance. The logic behind the use of redundant neurons to produce fault tolerance is that the overall pattern generated does not depend on any single connection being present. No two of these networks have exactly the same connections, but they still work as they need to, and they continue working even when connections or cells are eliminated (up to a point).
A very nice analysis of this phenomenon in mammals has been carried out by Schwab et al. [8] for the preBotziner network, a pattern generator for breathing. Because of redundancies in this network, its output is invariant as individual neurons are removed (up to a critical number) and in such a network, analytical techniques (such as Granger Causality) would be expected to identify synaptic connections between neurons even where none exist. Although this example is for a pattern generator, the same principle of fault tolerance through redundancy holds for all sorts of networks, and they all present the same problem for the application of Granger Causality.
In summary, neural networks have been designed to have outputs that degrade gracefully as network elements are eliminated or their properties perturbed. Such a design principle makes the networks work better for the animals, but simultaneously makes life harder for neuroscientists who want to learn how the network works by making measurements on the network as it does its job.
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NOTA CAUSTICANTE DESTE BLOGGER:
Ué, mas Darwin, o homem que teve a maior ideia que toda a humanidade já teve, não tinha eliminado DESIGN de uma vez por todas em biologia? NADA MAIS FALSO!!!
Ué, mas Darwin, o homem que teve a maior ideia que toda a humanidade já teve, não tinha eliminado DESIGN de uma vez por todas em biologia? NADA MAIS FALSO!!!
