On testing neural network models

Access through your institution Buy or subscribe In my recent Timeline article, I described the emergence of neural network models as an important paradigm in neuroscience research (From the

neuron doctrine to neural networks. _Nat. Rev. Neurosci._ 16, 487–497 (2015))1. In his correspondence (Neural networks in the future of neuroscience research. _Nat. Rev. Neurosci._

http://dx.doi.org/10.1038/nrn4042 (2015))2, Rubinov provides some thoughtful comments about the distinction between artificial neural networks and biologically inspired ones and about how a

strictly data-driven approach may succeed at providing a general theory of neural circuits. I thank Rubinov for these comments and note that this theory agnosticism is a methodological

approach that we respect and indeed sponsored in our Brain Activity Map proposal that led to the BRAIN Initiative3. Also, although in my Timeline article I tried to provide a brief summary

of the history of artificial neural network models, I am not yet personally convinced that there are clear instances in which a biologically inspired neural network model has yet been

validated (“...it is unclear whether existing neural network models have enough predictive value to be considered valid or useful for explaining brain circuits.” (Ref. 1)). There are many

exciting areas of progress in current neuroscience detailing phenomenology that is consistent with some neural network models, some of which I tried to summarize and illustrate, but at the

same time we are still far from a rigorous demonstration of any neural network model with causal experiments. I therefore could not agree more with Rubinov that we still have “largely not

bridged the gap between elegant theory and neuroscientific observation”. But when will we know that we have bridged that gap? This is a difficult question to answer, depending on the

particular viewpoint, and I would leave this open to the reader's own interpretation. In my mind, a successful neural model should have quantitative accuracy in predicting either the

behaviour, mental or perceptual state of the animal, or at least the future internal dynamics of the system. Another characteristic of a successful model could be its effective use in

designing therapies of brain-based diseases. On the other hand, one of my mentors, David Tank, argued that for a true understanding of a neural circuit we should be able to actually build

it, which is a stricter definition of a successful theory (D. Tank, personal communication) Finally, as mentioned in the Timeline article, one will also need to connect neural network models

to theories and facts at the structural and biophysical levels of neural circuits and to those in cognitive sciences as well, for proper 'scientific knowledge' to occur in the

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which are calculated during checkout ADDITIONAL ACCESS OPTIONS: * Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support REFERENCES * Yuste, R. From the

neuron doctrine to neural networks. _Nat. Rev. Neurosci._ 16, 487–497 (2015). Article  CAS  Google Scholar  * Rubinov, M. Neural networks in the future of neuroscience research. _Nat. Rev.

Neurosci._ http://dx.doi.org/10.1038/nrn4042 (2015). * Alivisatos, A. P. et al. The brain activity map project and the challenge of functional connectomics. _Neuron_ 74, 970–974 (2012).

Article  CAS  Google Scholar  Download references ACKNOWLEDGEMENTS The author is supported by the US National Institutes of Health (DP1EY024503) and ARO W911NF-12-1-0594 (MURI). AUTHOR

INFORMATION AUTHORS AND AFFILIATIONS * Departments of Biological Sciences and Neuroscience, Rafael Yuste is at the Neurotechnology Center and Kavli Institute of Brain Sciences, Columbia

University, New York, New York 10027, USA., Rafael Yuste Authors * Rafael Yuste View author publications You can also search for this author inPubMed Google Scholar CORRESPONDING AUTHOR

Correspondence to Rafael Yuste. ETHICS DECLARATIONS COMPETING INTERESTS The author declares no competing financial interests. RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS

ARTICLE CITE THIS ARTICLE Yuste, R. On testing neural network models. _Nat Rev Neurosci_ 16, 767 (2015). https://doi.org/10.1038/nrn4043 Download citation * Published: 21 October 2015 *

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