In “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) explain the impediments to complete understanding of what is going on in a brain, examining a recent study of a crab neural network (Jomah crab or Cancer borrealis):
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.
Nonmaterialist neuroscientists call it neuroplasticity – brains are always reorganizing themselves.
The authors conclude,
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.  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.
No argument here, and remember, these are just crabs. Wait till neuroscience gets to …
Functional connectivity in a rhythmic inhibitory circuit using Granger causality
Tilman Kispersky*, Gabrielle J Gutierrez and Eve Marder
* Corresponding author: Tilman Kispersky email@example.com
Neural Systems & Circuits 2011, 1:9 doi:10.1186/2042-1001-1-9
Published: 25 May 2011
Understanding circuit function would be greatly facilitated by methods that allow the simultaneous estimation of the functional strengths of all of the synapses in the network during ongoing network activity. Towards that end, we used Granger causality analysis on electrical recordings from the pyloric network of the crab Cancer borealis, a small rhythmic circuit with known connectivity, and known neuronal intrinsic properties.
Granger causality analysis reported a causal relationship where there is no anatomical correlate because of the strong oscillatory behavior of the pyloric circuit. Additionally, we failed to find a direct relationship between synaptic strength and Granger causality in a set of pyloric circuit models.
We conclude that the lack of a relationship between synaptic strength and functional connectivity occurs because Granger causality essentially collapses the direct contribution of the synapse with the intrinsic properties of the postsynaptic neuron. We suggest that the richness of the dynamical properties of most biological neurons complicates the simple interpretation of the results of functional connectivity analyses using Granger causality.