Mobile QR Code QR CODE


G. Bi and M. Poo, “Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type,” J. Neurosci., vol. 18, no. 24, pp. 10464-10472, Dec. 1998.DOI
N. Brunel, “Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons,” J. Comput. Neurosci., vol. 8, no. 3, pp. 183-208, May 2000.DOI
R. Brette et al., “Simulation of networks of spiking neurons: a review of tools and strategies,” J. Comput. Neurosci., vol. 23, no. 3, pp. 349-398, Dec. 2007.DOI
S. Song, et al, “Competitive Hebbian learning through spike-timing-dependent synaptic plasticity,” Nat. Neurosci., vol. 3, no. 9, Art. no. 9, Sep. 2000.DOI
M. Mikaitis, et al, “Neuromodulated Synaptic Plasticity on the SpiNNaker Neuromorphic System,” Front. Neurosci., vol. 12, 2018.DOI
Bodo Rueckauer, et al, “Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification,” Front. Neurosci., vol. 11, 2017.DOI
S. Kim, et al, “Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection,” Proc. AAAI Conf. Artif. Intell., vol. 34, no. 07, Art. no. 07, Apr. 2020.DOI
J. H. Lee, et al, “Training Deep Spiking Neural Networks Using Back-propagation,” Front. Neurosci., vol. 10, 2016.DOI
Y. Wu, et al, “Direct training for spiking neural networks: faster, larger, better,” in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, pp. 1311-1318, Jan. 2019.DOI
W. Zhang and P. Li, “Temporal spike sequence learning via backpropagation for deep spiking neural networks,” in Proceedings of the 34th International Conference on Neural Information Processing Systems, pp. 12022-12033, Dec. 2020.URL
F. Akopyan, et al., “TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip,” IEEE Trans. Comput.Aided Des. Integr. Circuits Syst., vol. 34, no. 10, pp. 1537-1557, Oct. 2015.DOI
M. Davies, et al., “Loihi: A Neuromorphic Manycore Processor with On-Chip Learning,” IEEE Micro, vol. 38, no. 1, pp. 82-99, Jan. 2018.DOI
G. K. Chen, et al, “A 4096-Neuron 1M-Synapse 3.8-pJ/SOP Spiking Neural Network With On-Chip STDP Learning and Sparse Weights in 10-nm FinFET CMOS,” IEEE J. Solid-State Circuits, vol. 54, no. 4, pp. 992-1002, Apr. 2019.DOI
S. K. Esser, et al., “Convolutional networks for fast, energy-efficient neuromorphic computing,” Proc. Natl. Acad. Sci., vol. 113, no. 41, pp. 11441-11446, Oct. 2016.DOI
S. Narayanan, et al, “SpinalFlow: An Architecture and Dataflow Tailored for Spiking Neural Networks,” in 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA) , pp. 349-362, May 2020.DOI
J.-J. Lee, et al, “Parallel Time Batching: Systolic-Array Acceleration of Sparse Spiking Neural Computation,” in 2022 IEEE International Sympo-sium on High-Performance Computer Architecture (HPCA), pp. 317-330, Apr. 2022.DOI
Y.-H. Chen, et al, “Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks,” in 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA), pp. 367-379, Jun. 2016.DOI
A. Paszke, et al., “PyTorch: an imperative style, high-performance deep learning library,” in Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp. 8026-8037, 2019.URL
Y. Kuang, et al., “A 64K-Neuron 64M-1b-Synapse 2.64pJ/SOP Neuromorphic Chip With All Memory on Chip for Spike-Based Models in 65nm CMOS,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 68, no. 7, pp. 2655-2659, Jul. 2021.DOI
J. Tandon, et al, “The OpenRISC processor: open hardware and Linux,” Linux J., vol. 2011, no. 6, Dec. 2011.URL
E. M. Izhikevich, “Simple Model of Spiking Neurons”, IEEE Trans. on Neural Networks, vol. 14, no. 6, pp. 1569-1572, Nov. 2003.DOI
A. L. Hodgkin and A. F. Huxley, “A quantitative description of membrane current and its application to conduction and excitation in nerve”, The J. of Physiology, vol. 117, pp. 500-544, Aug. 1952.DOI
A. Basu, L. Deng, C. Frenkel and X. Zhang, "Spiking Neural Network Integrated Circuits: A Review of Trends and Future Directions," 2022 IEEE Custom Integrated Circuits Conference (CICC), pp. 1-8, 2022.DOI