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
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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)