ARTICLE Communicated by Ben Tsuda Recurrent Connections in the Primate Ventral Visual Stream Mediate a Trade-Off Between Task Performance and Network Size During Core Object Recognition Aran Nayebi anayebi@stanford.edu Javier Sagastuy-Brena jvrsgsty@stanford.edu Daniel M. Bear dbear@stanford.edu Stanford University, Stanford, Californie 94305, U.S.A. Kohitij Kar kohitij@mit.edu MIT, Cambridge, MA 02139, U.S.A. Jonas Kubilius qbilius@gmail.com MIT, Cambridge, MA 02139, USA., and KU Leuven, Leuven 3000, Belgium Surya

REVIEW Communicated by Fernando Perez-Peña Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks Amirhossein Javanshir a.javanshir@deakin.edu.au School of Engineering, Deakin University, Geelong, VIC 3216, Australia Thanh Thi Nguyen thanh.nguyen@deakin.edu.au School of Information Technology, Deakin University (Burwood Campus) Burwood, VIC 3125, Australia M. UN. Parvez Mahmud m.a.mahmud@deakin.edu.au Abbas Z. Kouzani abbas.kouzani@deakin.edu.au School of Engineering, Deakin University, Geelong, VIC 3216, Australia Artificial neural networks (ANNs)

LETTER Communicated by Alberto Fachechi Full-Span Log-Linear Model and Fast Learning Algorithm Kazuya Takabatake k.takabatake@aist.go.jp Shotaro Akaho s.akaho@aist.go.jp Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, 305-8568, Japan | . . . |Xn−1 The full-span log-linear (FSLL) model introduced in this letter is con- sidered an nth order Boltzmann machine, where n is the number of all variables

ARTICLE Communicated by Sarah Schwoebel Bayesian Brains and the Rényi Divergence Noor Sajid noor.sajid.18@ucl.ac.uk Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, ROYAUME-UNI. Francesco Faccio francesco@idsia.ch The Swiss AI Lab IDSIA, USI, SUPSI, 6962, Viganello, Lugano, Switzerland Lancelot Da Costa l.da-costa@imperial.ac.uk Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, U.K., and Department of Mathematics, Imperial College London, London SW7

LETTER Communicated by Litian Liu Understanding Dynamics of Nonlinear Representation Learning and Its Application Kenji Kawaguchi kkawaguchi@fas.harvard.edu Harvard University, Cambridge, MA 02138, U.S.A. Linjun Zhang linjun.zhang@rutgers.edu Rutgers University, New Brunswick, New Jersey 08901 Zhun Deng zhundeng@g.harvard.edu Harvard University Cambridge, MA 02138, U.S.A. Representations of the world environment play a crucial role in artifi- cial intelligence. It is often inefficient to conduct reasoning and infer- ence directly

ARTICLE Communicated by Anthony Neville Burkitt Single Circuit in V1 Capable of Switching Contexts During Movement Using an Inhibitory Population as a Switch Doris Voina dvoina@uw.edu Applied Mathematics, University of Washington, Seattle, WA 98195 U.S.A. Stefano Recanatesi stefano.recanatesi@gmail.com Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, U.S.A. Brian Hu brian.hu@kitware.com Allen Institute for Brain Science, Seattle, WA 98109 U.S.A. Eric Shea-Brown etsb@uw.edu

Communicated by Stephen José Hanson Bridging the Gap Between Neurons and Cognition Through Assemblies of Neurons Christos H. Papadimitriou christos@columbia.edu Columbia University, New York, New York 10027, U.S.A. Angela D. Friederici friederici@cbs.mpg.de Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, D-04303 Leipzig, Germany During recent decades, our understanding of the brain has advanced dramatically at both the cellular and molecular levels and

REVIEW Communicated by Dana Ballard Predictive Coding, Variational Autoencoders, and Biological Connections Joseph Marino* josephmarino@deepmind.com Computation and Neural Systems, California Institute of Technology, Pasadena, Californie 91125, U.S.A. We present a review of predictive coding, from theoretical neuroscience, and variational autoencoders, from machine learning, identifying the common origin and mathematical framework underlying both areas. As each area is prominent within its respective field, more firmly connect-

ARTICLE Communicated by Jianqiao Zhu A Normative Account of Confirmation Bias During Reinforcement Learning Germain Lefebvre germain.lefebvre@outlook.com MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, ROYAUME-UNI. Christopher Summerfield christopher.summerfield@psy.ox.ac.uk Department of Experimental Psychology, University of Oxford, Oxford OX3 9DU, ROYAUME-UNI. Rafal Bogacz rafal.bogacz@ndcn.ox.ac.uk MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford

ARTICLE Communicated by Hrushikesh Mhaskar On PDE Characterization of Smooth Hierarchical Functions Computed by Neural Networks Khashayar Filom filom@umich.edu Department of Mathematics, Université du Michigan, Ann-Arbor, MI 48109, U.S.A. Roozbeh Farhoodi roozbeh@seas.upenn.edu Konrad Paul Kording kording@upenn.edu Departments of Bioengineering and Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvanie 1910, U.S.A. Neural networks are versatile tools for computation, having the ability to approximate a broad

LETTER Communicated by Hiroki Mori Completion of the Infeasible Actions of Others: Goal Inference by Dynamical Invariant Takuma Torii tak.torii@jaist.ac.jp Shohei Hidaka shhidaka@jaist.ac.jp Japan Advanced Institute of Science and Technology, Nomi, Ishikawa 923-1211, Japan To help another person, we need to infer his or her goal and intention and then perform the action that he or she was unable to perform to meet the intended

LETTER Communicated by Iris Groen Temporal Variabilities Provide Additional Category-Related Information in Object Category Decoding: A Systematic Comparison of Informative EEG Features Hamid Karimi-Rouzbahani hamid.karimi-rouzbahani@mrc-cbu.cam.ac.uk Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, U.K.; Perception in Action Research Centre and Department of Cognitive Science; and Department of Computing, Macquarie University, NSW 2109, Australia Mozhgan Shahmohammadi mozhganshahmohamadi1368@gmail.com Department of Computer

ARTICLE Communicated by Terrence Sejnowski Parametric UMAP Embeddings for Representation and Semisupervised Learning Tim Sainburg timsainb@gmail.com University of California San Diego, La Jolla, Californie 92093, U.S.A. Leland McInnes leland.mcinnes@gmail.com Tutte Institute for Mathematics and Computing, Ottawa, Ontario Canada Timothy Q. Gentner tgentner@ucsd.edu University of California San Diego, La Jolla, Californie 92093, U.S.A. UMAP is a nonparametric graph-based dimensionality reduction algo- rithm using applied Riemannian geometry

LETTER Communicated by Arindam Banerjee Multilinear Common Component Analysis via Kronecker Product Representation Kohei Yoshikawa yoshikawa.kohei615@gmail.com Shuichi Kawano skawano@ai.lab.uec.ac.jp Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofu-shi, Tokyo 182-8585, Japan We consider the problem of extracting a common structure from multi- ple tensor data sets. For this purpose, we propose multilinear common component analysis (MCCA) based on Kronecker products of mode-wise covariance

ARTICLE Communicated by Nick Hardy Stimulus-Driven and Spontaneous Dynamics in Excitatory-Inhibitory Recurrent Neural Networks for Sequence Representation Alfred Rajakumar aar653@nyu.edu Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, U.S.A. John Rinzel rinzel@cns.nyu.edu Courant Institute of Mathematical Sciences and Center for Neural Science, New York University, New York, New York 10012, Etats-Unis. Zhe S. Chen zhe.chen@nyulangone.org Department of Psychiatry and Neuroscience Institute, Nouveau

ARTICLE Communicated by Luke Prince A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis David Lipshutz dlipshutz@flatironinstitute.org Yanis Bahroun ybahroun@flatironinstitute.org Siavash Golkar sgolkar@flatironinstitute.org Center for Computational Neuroscience, Flatiron Institute, New York, New York 10010, U.S.A. Anirvan M. Sengupta anirvans@physics.rutgers.edu Center for Computational Neuroscience, Flatiron Institute, New York, New York 10010, USA., and Department of Physics and Astronomy, Rutgers University, Piscataway, New Jersey 08854 U.S.A. Dmitri B. Chklovskii

LETTER Communicated by Peer Neubert Simulating and Predicting Dynamical Systems With Spatial Semantic Pointers Aaron R. Voelker arvoelke@uwaterloo.ca Peter Blouw peter.blouw@appliedbrainresearch.com Xuan Choo xuan.choo@appliedbrainresearch.com Applied Brain Research, Waterloo, ON N2L 3G1, Canada Nicole Sandra-Yaffa Dumont ns2dumont@uwaterloo.ca Cheriton School of Computer Science, Université de Waterloo, Waterloo, Ontario, N2L 3G1, Canada Terrence C. Stewart terrence.stewart@nrc-cnrc.gc.ca National Research Council of Canada, University of Waterloo Collaboration Centre, Waterloo, ON

ARTICLE Communicated by Joel Zylberberg Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings Young Joon Kim yjkimnada@gmail.com Columbia University, New York, New York 10027, U.S.A. Nora Brackbill nbrack@stanford.edu Stanford University, Stanford, Californie 94305, U.S.A. Eleanor Batty erb2180@columbia.edu JinHyung Lee jl4303@columbia.edu Catalin Mitelut mitelutco@gmail.com William Tong wlt2115@columbia.edu Columbia University, New York, New York 10027, U.S.A. E. J.. Chichilnisky ej@stanford.edu Stanford University, Stanford, CA U.S.A. Liam