E D I TO R I A L
The practice of theoretical neuroscience
© 2005 Nature Publishing Group http://www.nature.com/natureneuroscience
“I
n theory, there is no difference between theory and prac- the hypothesis and assumptions should be reasonably constrained
tice. But, in practice, there is,” wrote Nobel laureate chemist by available evidence. Theories that are motivated by biology are
Manfred Eigen. In neuroscience, unfortunately, there remains a the ones that are most likely to be influential with biologists. Bold,
considerable difference between the two—particularly in the number of abstract theories may turn out to be right in the end, but if there is
people who appreciate these different ways of doing research. Thus, we no way to conceive of how the brain can implement them nor a way
have devoted part of this issue to a special focus on research presented to test them experimentally in the near future, then the audience that
at the Computational and Systems Neuroscience (Cosyne) meeting ear- may be influenced by the work diminishes.
lier this year, in an effort to illustrate how theoretical and experimental As with any paper, but particularly so for computation papers, the
approaches can work together to provide insight into brain function. essentials must be presented in an intuitive way that can be grasped by
Theory has developed a bit of a bad reputation among experi- scientists outside the field. This means keeping jargon to a minimum,
mentalists. Many scientists are skeptical of claims based on simu- and presenting arguments in sentences, not equations written out in
lated data, feeling that such efforts are too far removed from biology words. Esoteric quantifiers such as ‘model-dependent statistic’ may
to be informative. Others question the utility of attempts to assign be mathematically more elegant than ‘mean’ and ‘standard deviation’
machine-like or—worse still—anthropomorphic operating princi- (and sound more impressive), but a paper using the latter terms is far
ples to the brain. Many find the dense language of theoretical papers more likely to reach its audience. As programs in computational neu-
exhausting and are frustrated when straightforward biological prin- roscience and annual workshops such as the advanced computational
ciples are obfuscated by impenetrable math. Hard experimental evi- neuroscience courses offered at Woods Hole and in the European
dence is the key to understanding the brain, such scientists say, so Union flourish, an increasing number of theorists and biologists
why indulge in these mental exercises? are becoming more facile with the language of the complementary
In reality, theory is an integral part of all good neuroscience approach and are coming to appreciate the value of integrating the
papers—including experimental papers. Any good paper includes two disciplines. However, both fields have a long way to go before it
an intuitive framework for its results and why they came out the way will be commonplace for them to proceed hand in hand.
they did. For example, a study identifying a new protein involved in We feel that the papers presented in this special issue, which was
long-term potentiation is nothing more than a disconnected data put together by Associate Editor I-han Chou, exemplify the applica-
set without a mechanistic framework for how it interacts with other tion of theory to empirical studies. In a departure from our usual
elements in the pathway and an intuition for the functional conse- focus format, which normally includes only commissioned reviews,
quences of these interactions. ‘Theoretical’ papers simply formalize the focus also features primary research papers highlighting the best
and explore these intuitions and mechanisms—sometimes leading work presented at the Cosyne meeting (http://www.cosyne.org). This
to the conclusion that our initial, hand-waving explanations do not meeting was held in March in Salt Lake City, Utah, and brought
provide a good fit to the data. Good theories can synthesize large together a broad range of theorists and experimentalists interested
quantities of empirical data, distilling them to a few simple notions, in systems neuroscience. Reflecting the diversity of attendees at the
and can establish quantitative relationships between individual meeting, the papers span a variety of topics and contain different
observations. They can generate predictions that can serve to validate degrees of theoretical formalization.
current and future experiments. Given the vast number of empirical Every research article in this special issue was subjected to our
studies being generated by the field and the sheer complexity of the regular peer-review process. We applied our usual stringent edito-
brain, it is clear that theoretical approaches have great potential for rial standards to each paper, and each one met the criteria for pub-
making sense of the problem. lication in a regular issue of Nature Neuroscience. To accompany
What makes for a computational paper that is not only a good these papers, we have also commissioned several perspectives on
study but one that will have wide impact among experimental quantitative approaches to probing neural data. Gidon Felsen and
neuroscientists? Fundamentally, a good theory paper contains the Yang Dan discuss the merits of using natural scenes to expand our
same elements as any good paper in cellular, molecular, systems or understanding of the visual system, whereas Nicole Rust and Tony
cognitive neuroscience. The paper should have a thought-provoking Movshon counter with a piece extolling the approach of using syn-
new hypothesis that is of potential interest to a wide audience. thetic stimuli. Jonathan Victor discusses data-analysis techniques
The model should be rigorously tested. Is it robust to biological applied to different experimental disciplines, and possible ways to
variability? Can the model be falsified, and does it survive that test? translate them across fields. We hope that this focus will highlight the
Results, such as network simulations, should be quantified and not value of increased dialogue between theorists and experimentalists,
just demonstrated qualitatively. As with any other neuroscience paper, and spur future integrative efforts.
This study source was downloaded by 100000898182462 from CourseHero.com on 05-05-2025 23:47:16 GMT -05:00
NATURE NEUROSCIENCE VOLUME 8 | NUMBER 12 | DECEMBER 2005 1627
https://www.coursehero.com/file/154399071/Nature-Neuroscience-2005-dec-01-vol-8-iss-12-The-practice-of-theoretical-neuroscience-2005-/
The practice of theoretical neuroscience
© 2005 Nature Publishing Group http://www.nature.com/natureneuroscience
“I
n theory, there is no difference between theory and prac- the hypothesis and assumptions should be reasonably constrained
tice. But, in practice, there is,” wrote Nobel laureate chemist by available evidence. Theories that are motivated by biology are
Manfred Eigen. In neuroscience, unfortunately, there remains a the ones that are most likely to be influential with biologists. Bold,
considerable difference between the two—particularly in the number of abstract theories may turn out to be right in the end, but if there is
people who appreciate these different ways of doing research. Thus, we no way to conceive of how the brain can implement them nor a way
have devoted part of this issue to a special focus on research presented to test them experimentally in the near future, then the audience that
at the Computational and Systems Neuroscience (Cosyne) meeting ear- may be influenced by the work diminishes.
lier this year, in an effort to illustrate how theoretical and experimental As with any paper, but particularly so for computation papers, the
approaches can work together to provide insight into brain function. essentials must be presented in an intuitive way that can be grasped by
Theory has developed a bit of a bad reputation among experi- scientists outside the field. This means keeping jargon to a minimum,
mentalists. Many scientists are skeptical of claims based on simu- and presenting arguments in sentences, not equations written out in
lated data, feeling that such efforts are too far removed from biology words. Esoteric quantifiers such as ‘model-dependent statistic’ may
to be informative. Others question the utility of attempts to assign be mathematically more elegant than ‘mean’ and ‘standard deviation’
machine-like or—worse still—anthropomorphic operating princi- (and sound more impressive), but a paper using the latter terms is far
ples to the brain. Many find the dense language of theoretical papers more likely to reach its audience. As programs in computational neu-
exhausting and are frustrated when straightforward biological prin- roscience and annual workshops such as the advanced computational
ciples are obfuscated by impenetrable math. Hard experimental evi- neuroscience courses offered at Woods Hole and in the European
dence is the key to understanding the brain, such scientists say, so Union flourish, an increasing number of theorists and biologists
why indulge in these mental exercises? are becoming more facile with the language of the complementary
In reality, theory is an integral part of all good neuroscience approach and are coming to appreciate the value of integrating the
papers—including experimental papers. Any good paper includes two disciplines. However, both fields have a long way to go before it
an intuitive framework for its results and why they came out the way will be commonplace for them to proceed hand in hand.
they did. For example, a study identifying a new protein involved in We feel that the papers presented in this special issue, which was
long-term potentiation is nothing more than a disconnected data put together by Associate Editor I-han Chou, exemplify the applica-
set without a mechanistic framework for how it interacts with other tion of theory to empirical studies. In a departure from our usual
elements in the pathway and an intuition for the functional conse- focus format, which normally includes only commissioned reviews,
quences of these interactions. ‘Theoretical’ papers simply formalize the focus also features primary research papers highlighting the best
and explore these intuitions and mechanisms—sometimes leading work presented at the Cosyne meeting (http://www.cosyne.org). This
to the conclusion that our initial, hand-waving explanations do not meeting was held in March in Salt Lake City, Utah, and brought
provide a good fit to the data. Good theories can synthesize large together a broad range of theorists and experimentalists interested
quantities of empirical data, distilling them to a few simple notions, in systems neuroscience. Reflecting the diversity of attendees at the
and can establish quantitative relationships between individual meeting, the papers span a variety of topics and contain different
observations. They can generate predictions that can serve to validate degrees of theoretical formalization.
current and future experiments. Given the vast number of empirical Every research article in this special issue was subjected to our
studies being generated by the field and the sheer complexity of the regular peer-review process. We applied our usual stringent edito-
brain, it is clear that theoretical approaches have great potential for rial standards to each paper, and each one met the criteria for pub-
making sense of the problem. lication in a regular issue of Nature Neuroscience. To accompany
What makes for a computational paper that is not only a good these papers, we have also commissioned several perspectives on
study but one that will have wide impact among experimental quantitative approaches to probing neural data. Gidon Felsen and
neuroscientists? Fundamentally, a good theory paper contains the Yang Dan discuss the merits of using natural scenes to expand our
same elements as any good paper in cellular, molecular, systems or understanding of the visual system, whereas Nicole Rust and Tony
cognitive neuroscience. The paper should have a thought-provoking Movshon counter with a piece extolling the approach of using syn-
new hypothesis that is of potential interest to a wide audience. thetic stimuli. Jonathan Victor discusses data-analysis techniques
The model should be rigorously tested. Is it robust to biological applied to different experimental disciplines, and possible ways to
variability? Can the model be falsified, and does it survive that test? translate them across fields. We hope that this focus will highlight the
Results, such as network simulations, should be quantified and not value of increased dialogue between theorists and experimentalists,
just demonstrated qualitatively. As with any other neuroscience paper, and spur future integrative efforts.
This study source was downloaded by 100000898182462 from CourseHero.com on 05-05-2025 23:47:16 GMT -05:00
NATURE NEUROSCIENCE VOLUME 8 | NUMBER 12 | DECEMBER 2005 1627
https://www.coursehero.com/file/154399071/Nature-Neuroscience-2005-dec-01-vol-8-iss-12-The-practice-of-theoretical-neuroscience-2005-/
