Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
暂无分享,去创建一个
Johannes Schemmel | Karlheinz Meier | Syed Ahmed Aamir | Andreas Hartel | Gerd Kiene | Yannik Stradmann | Mihai A. Petrovici | Timo Wunderlich | Akos F. Kungl | Eric Müller | Andreas Grübl | Arthur Heimbrecht | Korbinian Schreiber | David Stöckel | Christian Pehle | Sebastian Billaudelle | Christian Mauch | Timo C. Wunderlich | J. Schemmel | K. Meier | M. Petrovici | Á. F. Kungl | S. Billaudelle | Yannik Stradmann | David Stöckel | Eric Müller | Andreas Hartel | Christian Mauch | Arthur Heimbrecht | G. Kiene | Andreas Grübl | Korbinian Schreiber | Christian Pehle | Sebastian Billaudelle | Gerd Kiene | Mihai A. Petrovici | S. A. Aamir
[1] Johannes Schemmel,et al. Stochastic inference with spiking neurons in the high-conductance state , 2016, Physical review. E.
[2] Peter Dayan,et al. A Neural Substrate of Prediction and Reward , 1997, Science.
[3] Robert A. Legenstein,et al. A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback , 2008, PLoS Comput. Biol..
[4] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[5] Steve B. Furber,et al. Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model , 2018, Front. Neurosci..
[6] Steve B. Furber,et al. The SpiNNaker Project , 2014, Proceedings of the IEEE.
[7] Y. Niv. Reinforcement learning in the brain , 2009 .
[8] Johannes Schemmel,et al. A wafer-scale neuromorphic hardware system for large-scale neural modeling , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.
[9] E. Fetz,et al. Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles. , 1973, Journal of neurophysiology.
[10] Henning Sprekeler,et al. Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity , 2010, The Journal of Neuroscience.
[11] Elke Edelmann,et al. Dopamine Modulates Spike Timing-Dependent Plasticity and Action Potential Properties in CA1 Pyramidal Neurons of Acute Rat Hippocampal Slices , 2011, Front. Syn. Neurosci..
[12] N. Guttman,et al. Operant conditioning, extinction, and periodic reinforcement in relation to concentration of sucrose used as reinforcing agent. , 1953, Journal of experimental psychology.
[13] Steve B. Furber,et al. Neuromodulated Synaptic Plasticity on the SpiNNaker Neuromorphic System , 2018, Front. Neurosci..
[14] J. Kerr,et al. Dopamine Receptor Activation Is Required for Corticostriatal Spike-Timing-Dependent Plasticity , 2008, The Journal of Neuroscience.
[15] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[16] Johannes Schemmel,et al. An Accelerated LIF Neuronal Network Array for a Large-Scale Mixed-Signal Neuromorphic Architecture , 2018, IEEE Transactions on Circuits and Systems I: Regular Papers.
[17] Hong Wang,et al. Loihi: A Neuromorphic Manycore Processor with On-Chip Learning , 2018, IEEE Micro.
[18] Liz Campbell,et al. Performance Comparison of the Digital Neuromorphic Hardware SpiNNaker and the Neural Network Simulation Software NEST for a Full-Scale Cortical Microcircuit Model , 2018 .
[19] Rodrigo Alvarez-Icaza,et al. Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations , 2014, Proceedings of the IEEE.
[20] Johannes Schemmel,et al. Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms , 2014, PloS one.
[21] A. Destexhe,et al. The high-conductance state of neocortical neurons in vivo , 2003, Nature Reviews Neuroscience.
[22] Giacomo Indiveri,et al. A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses , 2015, Front. Neurosci..
[23] E. Fetz,et al. Volitional control of single cortical neurons in a brain–machine interface , 2011, Journal of neural engineering.
[24] J. Hollerman,et al. Dopamine neurons report an error in the temporal prediction of reward during learning , 1998, Nature Neuroscience.
[25] W. Schultz,et al. Retroactive modulation of spike timing-dependent plasticity by dopamine , 2015, eLife.
[26] Wulfram Gerstner,et al. Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail , 2009, PLoS Comput. Biol..
[27] Timothée Masquelier,et al. First-Spike-Based Visual Categorization Using Reward-Modulated STDP , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[28] Wolfgang Maass,et al. Noise as a Resource for Computation and Learning in Networks of Spiking Neurons , 2014, Proceedings of the IEEE.
[29] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[30] Johannes Schemmel,et al. Full wafer redistribution and wafer embedding as key technologies for a multi-scale neuromorphic hardware cluster , 2017, 2017 IEEE 19th Electronics Packaging Technology Conference (EPTC).
[31] Andrew S. Cassidy,et al. A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.
[32] Wolfgang Maass,et al. Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning. , 2014, Cerebral cortex.
[33] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[34] Wulfram Gerstner,et al. Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons , 2013, PLoS Comput. Biol..
[35] Johannes Schemmel,et al. Reward-based learning under hardware constraints—using a RISC processor embedded in a neuromorphic substrate , 2013, Front. Neurosci..
[36] Manish Kumar. Large-scale neuromorphic computing systems , 2016 .
[37] Johannes Schemmel,et al. An analog dynamic memory array for neuromorphic hardware , 2013, 2013 European Conference on Circuit Theory and Design (ECCTD).
[38] Johannes Schemmel,et al. A highly tunable 65-nm CMOS LIF neuron for a large scale neuromorphic system , 2016, ESSCIRC Conference 2016: 42nd European Solid-State Circuits Conference.
[39] Xiaohui Xie,et al. Learning in neural networks by reinforcement of irregular spiking. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.
[40] Mitsuhisa Sato,et al. Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers , 2018, Front. Neuroinform..
[41] E. Izhikevich. Solving the distal reward problem through linkage of STDP and dopamine signaling , 2007, BMC Neuroscience.
[42] W. Gerstner,et al. Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules , 2016, Front. Neural Circuits.
[43] Johannes Schemmel,et al. Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System , 2016, IEEE Transactions on Biomedical Circuits and Systems.
[44] Simon J. Thorpe,et al. Combining STDP and Reward-Modulated STDP in Deep Convolutional Spiking Neural Networks for Digit Recognition , 2018, ArXiv.
[45] P. Glimcher,et al. Midbrain Dopamine Neurons Encode a Quantitative Reward Prediction Error Signal , 2005, Neuron.
[46] M. Farries,et al. Reinforcement learning with modulated spike timing dependent synaptic plasticity. , 2007, Journal of neurophysiology.
[47] Gert Cauwenberghs,et al. Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.