Neuroinformatics for Degenerate Brains

Neuroinformatics, Dec 2015

Erik De Schutter

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Neuroinformatics for Degenerate Brains

The ups and downs of neuroscience shares. Neuroinformatics Neuroinformatics for Degenerate Brains Erik De Schutter 0 Erik De Schutter 0 0 Computational Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University , Okinawa , Japan Several editorials in this journals have focused on why so little neuroscience data is being shared and what can be done to improve this situation.1,2 This editorial is on a different challenge: what data should be shared and how should this data be annotated and classified to promote efficient brain research? If the reader's first reaction to this statement is that surely this is a well understood problem then please read on because I have got news for you: the traditional neuroscience research paradigm that emphasizes hypothesis-driven approaches is ill adapted to study real brains. This is because brains are degenerate systems. Degeneracy is the ability of elements that are structurally different to execute the same function or produce the same output.3 This should not be confused with the more familiar concept of redundancy, which describes systems where identical elements are replicated so that if one fails another can take over the function. In his seminal 2001 paper,3 that - should be required reading for all biologists, Gerald Edelman describes 21 examples of degeneracy in different biological systems. Many of these are general cellular properties, but six fall within the specific scope of neuroscience including behavior. The ultimate example of degeneracy is interanimal communication, with the multitude of human languages all serving the same function. Degeneracy is related to complexity4 in that more degenerate systems are more complex, but it is not a general property of complex systems. Edelman3, however, argues that degeneracy is an essential property of all biological systems because they had to evolve. Without degeneracy it would be very difficult for living organisms to compensate for deleterious mutations and, because many random mutations will result in some loss of function, this implies that evolution would on average result in less fit individuals. Of course many lethal and disease generating mutations are known, but most mutations are relatively innocent because degeneracy allows for compensatory adjustments. Conversely, some mutations may lead to improved adaptation to environmental conditions and become a selective advantage, a process called evolution… An additional advantage of degenerate systems is that they allow for more flexibility: although different entities may be able to perform the same function, they often do so with small differences. Therefore, depending on prevailing conditions, one type may be favored over another because of its improved performance. Many hypothesis-driven neuroscience studies can be summarized as ‘we observed property X in system Y and hypothesized that entity Z is causing X’ followed by a series of 4 Tononi, G., Sporns, O., & Edelman, G. M. (1999). Measures of degeneracy and redundancy in biological networks. Proceedings of the National Academy of Sciences of the United States of America, 96(6), 3257–3262. experiments that confirm the second part of the statement. Examples are attributing specific functions to ion channel type Z in producing excitability property X in neuron type Y, and neuron type Z or synaptic connectivity Z in brain structure Y causing behavior X. The fallacy of ‘proving’ such an hypothesis in a degenerate system is that it provides incomplete information about structure Y, because it ignores both the many other functions and properties that Z may contribute to and the involvement of other elements in causing X. In other words, most brain functions depend on the dynamic interaction of many actors in a flexible manner. Take, for example, mechanisms contributing to synaptic plasticity. Edelman 3 reminds the reader that many presynaptic and postsynaptic mechanisms are involved in synaptic plasticity and goes on to say BThe complexity of the system includes many sites at which a variety of changes can modulate synaptic efficacy in a similar manner. Whenever evidence for each of these changes has been sought experimentally, it has been found.^ This makes synaptic plasticity an archetypical example of degeneracy where many (competing) hypotheses may be true at the same time. This probably explains why in cerebellar learning, depending on the experimental setup, researchers find that cerebellar long-term depression is sometimes essential to learn conditioned behavior and sometimes not.5 Returning to specific functions of channels, it has been well established both for voltage-gated and synaptic channels that very specific neuronal properties can be produced by different combinations of channels acting together.6 The consequences of degeneracy for neuroinformatics are complex but two issues stand out: the problems of selective data in support of specific hypotheses and of generatin (...truncated)


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Erik De Schutter. Neuroinformatics for Degenerate Brains, Neuroinformatics, 2016, pp. 1-3, Volume 14, Issue 1, DOI: 10.1007/s12021-015-9294-1