Appreciating the variety of goals in computational neuroscience.

Within computational neuroscience, informal interactions with modelers often reveal wildly divergent goals. In this opinion piece, we explicitly address the diversity of goals that motivate and ultimately influence modeling efforts. We argue that a wide range of goals can be meaningfully taken to be of highest importance. A simple informal survey conducted on the Internet confirmed the diversity of goals in the community. However, different priorities or preferences of individual researchers can lead to divergent model evaluation criteria. We propose that many disagreements in evaluating the merit of computational research stem from differences in goals and not from the mechanics of constructing, describing, and validating models. We suggest that authors state explicitly their goals when proposing models so that others can judge the quality of the research with respect to its stated goals.

[1]  N. Chater,et al.  Ten years of the rational analysis of cognition , 1999, Trends in Cognitive Sciences.

[2]  Michael I. Jordan,et al.  Optimal feedback control as a theory of motor coordination , 2002, Nature Neuroscience.

[3]  S. Chandrasekhar Truth and Beauty: Aesthetics and Motivations in Science , 1987 .

[4]  Gunnar Blohm,et al.  Corrective response times in a coordinated eye-head-arm countermanding task. , 2018, Journal of neurophysiology.

[5]  Wendy S. Parker,et al.  Computer simulation and philosophy of science , 2012 .

[6]  M. A. MacIver,et al.  Neuroscience Needs Behavior: Correcting a Reductionist Bias , 2017, Neuron.

[7]  E. Lawson,et al.  Problems identified by secondary review of accepted manuscripts. , 1990, JAMA.

[8]  T. Sejnowski,et al.  Neural representation and neural computation , 1990 .

[9]  Thomas Serre,et al.  Realistic Modeling of Simple and Complex Cell Tuning in the HMAX Model, and Implications for Invariant Object Recognition in Cortex , 2004 .

[10]  Daniel W. Byrne,et al.  Common Reasons for Rejecting Manuscripts at Medical Journals: A Survey of Editors and Peer Reviewers , 2000 .

[11]  Nick Chater,et al.  The Rational Analysis Of Mind And Behavior , 2000, Synthese.

[12]  Kendrick N Kay,et al.  Bottom-up and top-down computations in word- and face-selective cortex , 2017, eLife.

[13]  T. Carlson,et al.  Patterns of Activity in the Categorical Representations of Objects , 2003, Journal of Cognitive Neuroscience.

[14]  C S Green,et al.  Alterations in choice behavior by manipulations of world model , 2010, Proceedings of the National Academy of Sciences.

[15]  Marc Fleurbaey,et al.  Two variants of Harsanyi's aggregation theorem , 2009 .

[16]  Bruno A Olshausen,et al.  Sparse coding of sensory inputs , 2004, Current Opinion in Neurobiology.

[17]  T. Lombrozo Explanation and Abductive Inference , 2012 .

[18]  John R. Josephson,et al.  Abductive inference : computation, philosophy, technology , 1994 .

[19]  Konrad Paul Kording,et al.  A How-to-Model Guide for Neuroscience , 2020, eNeuro.

[20]  Peter C. Fishburn,et al.  Arrow's impossibility theorem: Concise proof and infinite voters , 1970 .

[21]  H. B. Barlow,et al.  Possible Principles Underlying the Transformations of Sensory Messages , 2012 .

[22]  Hasan Al-Nashash,et al.  EEG signal modeling using adaptive Markov process amplitude , 2004, IEEE Transactions on Biomedical Engineering.

[23]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[24]  Y. Dan,et al.  Spike Timing-Dependent Plasticity of Neural Circuits , 2004, Neuron.

[25]  Gunnar Blohm,et al.  Decoding the cortical transformations for visually guided reaching in 3D space. , 2009, Cerebral cortex.

[26]  T. H. Brown,et al.  Biophysical model of a Hebbian synapse. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[27]  D. Pierson,et al.  The top 10 reasons why manuscripts are not accepted for publication. , 2004, Respiratory care.

[28]  Wulfram Gerstner,et al.  A neuronal learning rule for sub-millisecond temporal coding , 1996, Nature.

[29]  Konrad Paul Kording,et al.  The dynamics of memory as a consequence of optimal adaptation to a changing body , 2007, Nature Neuroscience.

[30]  Wei Ji Ma,et al.  Bayesian inference with probabilistic population codes , 2006, Nature Neuroscience.

[31]  Socrates Dokos,et al.  Computational comparison of conventional and novel electroconvulsive therapy electrode placements for the treatment of depression , 2019, European Psychiatry.

[32]  P. Fitts,et al.  Information capacity of discrete motor responses under different cognitive sets. , 1966, Journal of experimental psychology.

[33]  Jean-Jacques Orban de Xivry,et al.  Kalman Filtering Naturally Accounts for Visually Guided and Predictive Smooth Pursuit Dynamics , 2013, The Journal of Neuroscience.

[34]  Daniel M. Wolpert,et al.  Making smooth moves , 2022 .

[35]  John C. Harsanyi,et al.  Bayesian Decision Theory, Rule Utilitarianism, And Arrow’s Impossibility Theorem , 1979 .

[36]  Konrad Paul Kording,et al.  Decision Theory: What "Should" the Nervous System Do? , 2007, Science.

[37]  Paul R. Schrater,et al.  Structure Learning in Human Sequential Decision-Making , 2008, NIPS.

[38]  John A. Weymark,et al.  Harsanyi's Social Aggregation Theorem and the Weak Pareto Principle , 1993 .

[39]  Lutz Bornmann,et al.  A content analysis of referees’ comments: how do comments on manuscripts rejected by a high-impact journal and later published in either a low- or high-impact journal differ? , 2010, Scientometrics.