LINK TO ORIGINAL ARTICLE LINK TO INITIAL CORRESPONDENCE other hand, one of my mentors, David Tank, argued that for a true understanding of a On testing neural network models neural circuit we should be able to actu- ally build it, which is a stricter definition Rafael Yuste of a successful theory (D. Tank, personal communication) Finally, as mentioned in In my recent Timeline article, I described existing neural network models have enough the Timeline article, one will also need to the emergence of neural network models predictive value to be considered valid or connect neural network models to theories as an important paradigm in neuroscience useful for explaining brain circuits.” (REF. 1)). and facts at the structural and biophysical research (From the neuron doctrine to neu- There are many exciting areas of progress levels of neural circuits and to those in cog- ral networks. Nat. Rev. Neurosci. 16, 487–497 in current neuroscience detailing phenom- nitive sciences as well, for proper ‘scientific (2015))1. In his correspondence (Neural enology that is consistent with some neural knowledge’ to occur in the Kantian sense. networks in the future of neuroscience network models, some of which I tried to research. Nat. Rev. Neurosci. http://dx.doi. summarize and illustrate, but at the same Rafael Yuste is at the Neurotechnology Center and org/10.1038/nrn4042 (2015))2, Rubinov time we are still far from a rigorous demon- Kavli Institute of Brain Sciences, Departments of provides some thoughtful comments about stration of any neural network model with Biological Sciences and Neuroscience, Columbia University, New York, New York 10027, USA. the distinction between artificial neural causal experiments. I therefore could not networks and biologically inspired ones and agree more with Rubinov that we still have e-mail: [email protected] about how a strictly data-driven approach “largely not bridged the gap between elegant may succeed at providing a general theory theory and neuroscientific observation”. But doi:10.1038/nrn4043 Published online 21 October 2015 of neural circuits. I thank Rubinov for these when will we know that we have bridged that comments and note that this theory agnosti- gap? This is a difficult question to answer, 1. Yuste, R. From the neuron doctrine to neural networks. Nat. Rev. Neurosci. 16, 487–497 (2015). cism is a methodological approach that we depending on the particular viewpoint, 2. Rubinov, M. Neural networks in the future of respect and indeed sponsored in our Brain and I would leave this open to the reader’s neuroscience research. Nat. Rev. Neurosci. http://dx. doi.org/10.1038/nrn4042 (2015). Activity Map proposal that led to the BRAIN own interpretation. In my mind, a success- 3. Alivisatos, A. P. et al. The brain activity map project Initiative3. Also, although in my Timeline ful neural model should have quantitative and the challenge of functional connectomics. Neuron article I tried to provide a brief summary accuracy in predicting either the behaviour, 74, 970–974 (2012). of the history of artificial neural network mental or perceptual state of the animal, or Acknowledgements The author is supported by the US National Institutes of models, I am not yet personally convinced at least the future internal dynamics of the Health (DP1EY024503) and ARO W911NF-12-1-0594 that there are clear instances in which a bio- system. Another characteristic of a success- (MURI). logically inspired neural network model has ful model could be its effective use in design- Competing interests statement yet been validated (“…it is unclear whether ing therapies of brain-based diseases. On the The author declares no competing interests. © 2015 Macmillan Publishers Limited. All rights reserved CORRESPONDENCE LINK TO ORIGINAL ARTICLE LINK TO AUTHOR’S REPLY Mikail Rubinov is at the Department of Psychiatry and Churchill College, University of Cambridge, Cambridge CB3 0DS, UK; and the Janelia Research Neural networks in the future of Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147, USA. neuroscience research e-mail: [email protected] doi:10.1038/nrn4042 Mikail Rubinov Published online 21 October 2015 1. Yuste, R. From the neuron doctrine to neural networks. Neural networks are increasingly seen to Yuste’s emphasis on some classic artificial Nat. Rev. Neurosci. 16, 487–497 (2015). 2. Rumelhart, D. E., McClelland, J. L. & The PDP Research supersede neurons as fundamental units neural network models does not seem to be Group. Parallel Distributed Processing: Explorations in of complex brain function. In his Timeline supported by the evidence of, or the promise the Microstructure of Cognition (MIT Press, 1986). 3. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. article (From the neuron doctrine to neural for, the problem-solving capacity of these Nature 521, 436–444 (2015). networks. Nat. Rev. Neurosci. 16, 487–497 models in neuroscience6. 4. Marcus, G. in The Future of the Brain: Essays by the World’s Leading Neuroscientists (eds Marcus, G. & 1 (2015)) , Yuste provides a timely overview What could be an alternative promising Freeman, J.) 205–215 (Princeton Univ. Press, 2014). of this process, but does not clearly differ- approach to biologically valid neural network 5. Zador, A. in The Future of the Brain: Essays by the World’s Leading Neuroscientists (eds Marcus, G. & entiate between biological neural network modelling? At present we can only specu- Freeman, J.) 40–49 (Princeton Univ. Press, 2014). models (broadly and imprecisely defined late, but the ongoing development of high- 6. Laudan, L. Progress and Its Problems: Towards a Theory of Scientific Growth (University of California as empirically valid models of (embodied) resolution high-throughput brain imaging Press, 1978). neuronal or brain systems, which enable technologies — including those being devel- 7. Alivisatos, A. P. et al. Nanotools for neuroscience and brain activity mapping. ACS Nano 7, 1850–1866 (2013). 7 the emergence of complex brain function oped as part of the BRAIN Initiative — and 8. Oh, S. W. et al. A mesoscale connectome of the mouse through distributed computation) and arti- the consequent availability of increasingly brain. Nature 508, 207–214 (2014). 9. Ahrens, M. B. et al. Brain-wide neuronal dynamics 8 9 ficial neural network models (a relatively large structural and functional imaging during motor adaptation in zebrafish. Nature 485, well-defined class of networks originally data sets, make it appealing to initially search 471–477 (2012). 10. Sporns, O. Discovering the Human Connectome (MIT 2 designed to model complex brain function for patterns in such data in less theory- Press, 2012). but now mainly viewed as a class of biologi- bound and more data-driven ways10,11, and 11. Vogelstein, J. T. et al. Discovery of brainwide neural- behavioral maps via multiscale unsupervised structure cally inspired data-analysis algorithms useful to subsequently construct theories a priori learning. Science 344, 386–392 (2014). in diverse scientific fields3). constrained on these discovered patterns12. A 12. Sejnowski, T. J., Churchland, P. S. & Movshon, J. A. Putting big data to good use in neuroscience. Nat. A distinction between biological and arti- famous example of this approach in biology Neurosci. 17, 1440–1441 (2014). ficial neural network models is important is the formulation of the theory of evolution 13. Kell, D. B. & Oliver, S. G. Here is the evidence, now what is the hypothesis? The complementary roles of as the neuroscience network paradigm is by natural selection; this theory arose from inductive and hypothesis-driven science in the post- mainly driven by the aim of uncovering bio- an initial aim to catalogue all living biological genomic era. BioEssays 26, 99–105 (2004). 14. Helmstaedter, M. et al. Connectomic reconstruction of logically valid mechanisms of neural com- organisms on earth, and from a subsequent the inner plexiform layer in the mouse retina. Nature putation. Artificial neural networks were careful analysis of the obtained diverse bio- 500, 168–174 (2013). 13 initially proposed as candidate models for logical data . Interestingly, artificial neural Acknowledgements such computation but, despite being enthu- networks may yet prove to be important in The author thanks C. Chang for helpful comments. The author has received funding from the NARSAD Young Investigator siastically researched at the end of the twen- this quest but in the role of powerful tools for Award, the Isaac Newton Trust Research Grant and the Parke tieth century, they have largely not bridged analysing complex imaging data sets14, rather Davis Exchange Fellowship. the gap between elegant theory and neu- than as a theoretical foundation for how the Competing interests statement roscientific observation4,5. In this context, brain computes. The author declares no competing interests. NATURE REVIEWS | NEUROSCIENCE www.nature.com/reviews/neuro © 2015 Macmillan Publishers Limited. All rights reserved PERSPECTIVES be a useful paradigm, or act as guideposts, to TIMELINE understand many brain computations. This article does not provide an exhaustive review From the neuron doctrine to but instead illustrates with a small number of examples the transition between these two neural networks paradigms of neuroscience. History of the neuron doctrine Rafael Yuste Origins. Many neuroscience textbooks begin Abstract | For over a century, the neuron doctrine — which states that the neuron by explaining Cajal’s proposal that the unit of the structure of the nervous system is is the structural and functional unit of the nervous system — has provided a the individual neuron2,16,17 (FIGS 1,2a). This conceptual foundation for neuroscience. This viewpoint reflects its origins in a time idea, actively debated at the time, contrasted when the use of single-neuron anatomical and physiological techniques was with the ‘reticular theory’ — defended by prominent. However, newer multineuronal recording methods have revealed that Golgi himself — which hypothesized that ensembles of neurons, rather than individual cells, can form physiological units and neurons were linked in a single overarch- ing syncytium1.