Review of advances in neural networks: Neural design technology stack

[1]  Ivo D. Dinov,et al.  Deep learning for neural networks , 2018 .

[2]  Irene Yates,et al.  Cat Out of the Bag , 2017 .

[3]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[4]  Iddo Greental,et al.  Genetic algorithms for evolving deep neural networks , 2014, GECCO.

[5]  Kiara F. Bruggeman,et al.  How to build a brain , 2014 .

[6]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[7]  Tadashi Yamazaki,et al.  Realtime cerebellum: A large-scale spiking network model of the cerebellum that runs in realtime using a graphics processing unit , 2013, Neural Networks.

[8]  Victor C. M. Leung,et al.  Extreme Learning Machines [Trends & Controversies] , 2013, IEEE Intelligent Systems.

[9]  Giacomo Indiveri,et al.  Synthesizing cognition in neuromorphic electronic systems , 2013, Proceedings of the National Academy of Sciences.

[10]  Björn W. Schuller,et al.  YouTube Movie Reviews: Sentiment Analysis in an Audio-Visual Context , 2013, IEEE Intelligent Systems.

[11]  Chris Eliasmith,et al.  How to Build a Brain: A Neural Architecture for Biological Cognition , 2013 .

[12]  Wolfgang Maass,et al.  Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity , 2013, PLoS Comput. Biol..

[13]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  Dipankar Das,et al.  Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining , 2013, IEEE Intelligent Systems.

[15]  Marc'Aurelio Ranzato,et al.  Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[16]  Hongming Zhou,et al.  Extreme Learning Machines [Trends & Controversies] , 2013 .

[17]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[18]  Trevor Bekolay,et al.  A Large-Scale Model of the Functioning Brain , 2012, Science.

[19]  Eric L. Denovellis,et al.  Synchronous Oscillatory Neural Ensembles for Rules in the Prefrontal Cortex , 2012, Neuron.

[20]  Wulfram Gerstner,et al.  Theory and Simulation in Neuroscience , 2012, Science.

[21]  E. D’Angelo.  The human brain project. , 2012, Functional neurology.

[22]  Sean L. Hill,et al.  Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits , 2012, Proceedings of the National Academy of Sciences.

[23]  Jürgen Schmidhuber,et al.  Multi-column deep neural network for traffic sign classification , 2012, Neural Networks.

[24]  Dong Yu,et al.  Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[25]  Massimiliano Di Ventra,et al.  Neuromorphic, Digital, and Quantum Computation With Memory Circuit Elements , 2010, Proceedings of the IEEE.

[26]  Geoffrey E. Hinton,et al.  Acoustic Modeling Using Deep Belief Networks , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[27]  Dharmendra S. Modha,et al.  A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm , 2011, 2011 IEEE Custom Integrated Circuits Conference (CICC).

[28]  Johannes Schemmel,et al.  A comprehensive workflow for general-purpose neural modeling with highly configurable neuromorphic hardware systems , 2010, Biological Cybernetics.

[29]  D. Debanne,et al.  Axon physiology. , 2011, Physiological reviews.

[30]  Henning Sprekeler,et al.  Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity , 2010, The Journal of Neuroscience.

[31]  Jennifer Chu-Carroll,et al.  Building Watson: An Overview of the DeepQA Project , 2010, AI Mag..

[32]  Gayle M. Wittenberg,et al.  Spike Timing Dependent Plasticity: A Consequence of More Fundamental Learning Rules , 2010, Front. Comput. Neurosci..

[33]  Wolfgang Maass,et al.  STDP enables spiking neurons to detect hidden causes of their inputs , 2009, NIPS.

[34]  Yoshua Bengio,et al.  Exploring Strategies for Training Deep Neural Networks , 2009, J. Mach. Learn. Res..

[35]  Dharmendra S. Modha,et al.  The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses , 2009, Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis.

[36]  Wulfram Gerstner,et al.  How Good Are Neuron Models? , 2009, Science.

[37]  J. Schmidhuber,et al.  A Novel Connectionist System for Unconstrained Handwriting Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  D. McCormick,et al.  Cortical Action Potential Backpropagation Explains Spike Threshold Variability and Rapid-Onset Kinetics , 2008, The Journal of Neuroscience.

[39]  Chris Eliasmith,et al.  Solving the Problem of Negative Synaptic Weights in Cortical Models , 2008, Neural Computation.

[40]  Lyle N. Long,et al.  Character Recognition using Spiking Neural Networks , 2007, 2007 International Joint Conference on Neural Networks.

[41]  Y. Leblebici,et al.  Fault tolerance of feed-forward artificial neural network architectures targeting nano-scale implementations , 2007, 2007 50th Midwest Symposium on Circuits and Systems.

[42]  E. Izhikevich.  Solving the distal reward problem through linkage of STDP and dopamine signaling , 2007, BMC Neuroscience.

[43]  Daniel E. Feldman,et al.  A Dynamic Spatial Gradient of Hebbian Learning in Dendrites , 2006, Neuron.

[44]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[45]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[46]  E. Adrian,et al.  The impulses produced by sensory nerve-endings: Part II. The response of a Single End-Organ. , 2006, The Journal of physiology.

[47]  Sung-Bae Cho,et al.  Evolutionary neural networks for anomaly detection based on the behavior of a program , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[48]  Eugene M. Izhikevich,et al.  Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.

[49]  Nicholas T. Carnevale,et al.  ModelDB: A Database to Support Computational Neuroscience , 2004, Journal of Computational Neuroscience.

[50]  Ronald,et al.  Learning representations by backpropagating errors , 2004 .

[51]  Chris Eliasmith,et al.  Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems , 2004, IEEE Transactions on Neural Networks.

[52]  Cândida Ferreira,et al.  Designing Neural Networks Using Gene Expression Programming , 2004, WSC.

[53]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[54]  Wlodzislaw Duch.  Coloring black boxes: visualization of neural network decisions , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[55]  Bruno A. Olshausen,et al.  Book Review , 2003, Journal of Cognitive Neuroscience.

[56]  Wulfram Gerstner,et al.  SPIKING NEURON MODELS Single Neurons , Populations , Plasticity , 2002 .

[57]  Michael Collins,et al.  Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms , 2002, EMNLP.

[58]  Wulfram Gerstner,et al.  Spiking Neuron Models: An Introduction , 2002 .

[59]  Sander M. Bohte,et al.  Error-backpropagation in temporally encoded networks of spiking neurons , 2000, Neurocomputing.

[60]  Chris Eliasmith,et al.  Neural Engineering , 2020 .

[61]  Peter Dayan,et al.  Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .

[62]  Alexander Borst,et al.  Information theory and neural coding , 1999, Nature Neuroscience.

[63]  X. Yao.  Evolving Artificial Neural Networks , 1999 .

[64]  R Van Rullen,et al.  Face processing using one spike per neurone. , 1998, Bio Systems.

[65]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[66]  H. Markram,et al.  Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.

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

[68]  D. Field,et al.  Natural image statistics and efficient coding. , 1996, Network.

[69]  Kazuyuki Aihara,et al.  Chaotic simulated annealing by a neural network model with transient chaos , 1995, Neural Networks.

[70]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990, Bulletin of mathematical biology.

[71]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[72]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[73]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[74]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[75]  J. von Neumann,et al.  The Computer and the Brain , 1959 .

[76]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[77]  E. Adrian,et al.  The impulses produced by sensory nerve endings , 1926, The Journal of physiology.

[78]  P. Gastaldo,et al.  Combining ELMs with Random Projections , 2022 .