Versatile Emulation of Spiking Neural Networks on an Accelerated Neuromorphic Substrate
暂无分享,去创建一个
Andreas Hartel | Walter Senn | Gerd Kiene | Yannik Stradmann | Andreas Grübl | Arthur Heimbrecht | Korbinian Schreiber | David Stöckel | Christian Pehle | Sebastian Billaudelle | Christian Mauch | Johannes Schemmel | Philipp Spilger | Eric Müller | Oliver Breitwieser | Vitali Karasenko | Mitja Kleider | Timo C. Wunderlich | Benjamin Cramer | Andreas Baumbach | Dominik Dold | Julian Göltz | Ákos F. Kungl | Mihai Petrovici | Karlheinz Meier | W. Senn | J. Schemmel | K. Meier | A. Baumbach | M. Petrovici | Á. F. Kungl | Yannik Stradmann | David Stöckel | Eric Müller | Philipp Spilger | Andreas Hartel | O. Breitwieser | Christian Mauch | Mitja Kleider | Arthur Heimbrecht | V. Karasenko | Andreas Grübl | Korbinian Schreiber | Christian Pehle | Sebastian Billaudelle | Julian Göltz | Benjamin Cramer | Dominik Dold | Gerd Kiene
[1] Mihai A. Petrovici,et al. Structural plasticity on an accelerated analog neuromorphic hardware system , 2019, Neural Networks.
[2] Walter Senn,et al. Fast and deep neuromorphic learning with time-to-first-spike coding , 2019, ArXiv.
[3] W. Senn,et al. Fast and deep: energy-efficient neuromorphic learning with first-spike times , 2019 .
[4] M. Wibral,et al. Control of criticality and computation in spiking neuromorphic networks with plasticity , 2019, Nature Communications.
[5] Karlheinz Meier,et al. Neuromorphic Hardware Learns to Learn , 2019, Front. Neurosci..
[6] Steve B. Furber,et al. Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype , 2018, Front. Neurosci..
[7] Johannes Schemmel,et al. Demonstrating Advantages of Neuromorphic Computation: A Pilot Study , 2018, Front. Neurosci..
[8] Johannes Schemmel,et al. Stochasticity from function - why the Bayesian brain may need no noise , 2018, Neural Networks.
[9] Dan Husmann de Oliveira,et al. Generative models on accelerated neuromorphic hardware , 2018, ArXiv.
[10] Gert Cauwenberghs,et al. Large-Scale Neuromorphic Spiking Array Processors: A Quest to Mimic the Brain , 2018, Front. Neurosci..
[11] 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.
[12] Johannes Schemmel,et al. A Mixed-Signal Structured AdEx Neuron for Accelerated Neuromorphic Cores , 2018, IEEE Transactions on Biomedical Circuits and Systems.
[13] Hong Wang,et al. Loihi: A Neuromorphic Manycore Processor with On-Chip Learning , 2018, IEEE Micro.
[14] B. Webb,et al. An Anatomically Constrained Model for Path Integration in the Bee Brain , 2017, Current Biology.
[15] W. Senn,et al. Spiking neurons with short-term synaptic plasticity form superior generative networks , 2017, Scientific Reports.
[16] Louis K. Scheffer,et al. A connectome of a learning and memory center in the adult Drosophila brain , 2017, eLife.
[17] Johannes Schemmel,et al. An accelerated analog neuromorphic hardware system emulating NMDA- and calcium-based non-linear dendrites , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[18] Robert A. Legenstein,et al. Pattern representation and recognition with accelerated analog neuromorphic systems , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).
[19] Robert A. Legenstein,et al. Neuromorphic hardware in the loop: Training a deep spiking network on the BrainScaleS wafer-scale system , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[20] Johannes Schemmel,et al. Stochastic inference with spiking neurons in the high-conductance state , 2016, Physical review. E.
[21] H. Mostafa. Supervised Learning Based on Temporal Coding in Spiking Neural Networks , 2016, IEEE Transactions on Neural Networks and Learning Systems.
[22] F. Sommer,et al. Structural Plasticity, Effectual Connectivity, and Memory in Cortex , 2016, Front. Neuroanat..
[23] Johannes Schemmel,et al. Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System , 2016, IEEE Transactions on Biomedical Circuits and Systems.
[24] W. Gerstner,et al. Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules , 2016, Front. Neural Circuits.
[25] David Kappel,et al. Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring , 2015, NIPS.
[26] Johannes Schemmel,et al. Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons , 2014, Front. Comput. Neurosci..
[27] Johannes Schemmel,et al. Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms , 2014, PloS one.
[28] Steve B. Furber,et al. The SpiNNaker Project , 2014, Proceedings of the IEEE.
[29] Johannes Schemmel,et al. An analog dynamic memory array for neuromorphic hardware , 2013, 2013 European Conference on Circuit Theory and Design (ECCTD).
[30] Louis K. Scheffer,et al. A visual motion detection circuit suggested by Drosophila connectomics , 2013, Nature.
[31] Y. Loewenstein,et al. Multiplicative Dynamics Underlie the Emergence of the Log-Normal Distribution of Spine Sizes in the Neocortex In Vivo , 2011, The Journal of Neuroscience.
[32] Guan-Yu Chen,et al. Three-Dimensional Reconstruction of Brain-wide Wiring Networks in Drosophila at Single-Cell Resolution , 2011, Current Biology.
[33] Henning Sprekeler,et al. Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity , 2010, The Journal of Neuroscience.
[34] 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.
[35] Nathan W. Gouwens,et al. Signal Propagation in Drosophila Central Neurons , 2009, The Journal of Neuroscience.
[36] R. Strauss,et al. Analysis of a spatial orientation memory in Drosophila , 2008, Nature.
[37] Gilles Laurent,et al. Estimating Firing Rates from Calcium Signals in Locust Projection Neurons in Vivo , 2007, Frontiers in neural circuits.
[38] Rajesh P. N. Rao,et al. Bayesian brain : probabilistic approaches to neural coding , 2006 .
[39] G. Shepherd,et al. Transient and Persistent Dendritic Spines in the Neocortex In Vivo , 2005, Neuron.
[40] R. Fisher. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .
[41] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.