Simplicity is the Ultimate Sophistication
- Leonardo Da Vinci
  • Sorbaro, M., Liu, Q., Bortone, M., and Sheik, S. (2019). Optimizing the energy consumption of spiking neural networks for neuromorphic applications. arXiv preprint arXiv:1912.01268.
  • Live Demonstration: Face Recognition on an Ultra-low Power Event-driven Convolutional Neural Network ASIC. Liu, Q., Richter, O., Nielsen, C., Sheik, S., Indiveri, G., and Qiao, N. (2019). In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.
  • Memory-efficient Synaptic Connectivity for Spike-Timing-Dependent Plasticity. Pedroni, B.U., Joshi, S., Deiss, S., Sheik, S., Detorakis, G., Paul, S., Augustine, C., Neftci, E.O. and Cauwenberghs, G., 2019. Frontiers in Neuroscience, 13, p.357.
  • Unsupervised Synaptic Pruning Strategies for Restricted Boltzmann Machines. Kalyan, S., Joshi, S., Sheik, S., Pedroni, B. U., and Cauwcnbcrghs, G. (2018, October). 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) (pp. 1-4). IEEE.
  • Small-footprint Spiking Neural Networks for Power-efficient Keyword Spotting. Pedroni, B. U., Sheik, S., Mostafa, H., Paul, S., Augustine, C., and Cauwenberghs, G. (2018, October). 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) (pp. 1-4). IEEE.
  • Neural and synaptic array transceiver: A brain-inspired computing framework for embedded learning. Detorakis, G., Sadique Sheik, C.A., Paul, S., Pedroni, B.U., Dutt, N., Krichmar, J., Cauwenberghs, G. and Neftci, E., Frontiers in neuroscience, 12., 2018.
  • Hardware-efficient on-line learning through pipelined truncated-error backpropagation in binary-state networks. H Mostafa, B Pedroni, S Sheik, G Cauwenberghs, Frontiers in Neuroscience 2017.
  • Pipelined Parallel Contrastive Divergence for Continuous Generative Model Learningr. BU Pedroni, S Sheik, G Cauwenberghs, 2017 IEEE International Symposium on Circuits and Systems (ISCAS), 2017 IEEE.
  • Fast Classification Using Sparsely Active Spiking Networks. H Mostafa, BU Pedroni, S Sheik, G Cauwenberghs, 2017 IEEE International Symposium on Circuits and Systems (ISCAS), 2017 IEEE.
  • Spike Based Information Processing in Spiking Neural Networks. S Sheik, 2016 International Conference on Applications in Nonlinear Dynamics, 177-188
  • Membrane-Dependent Neuromorphic Learning Rule for Unsupervised Spike Pattern Detection. S Sheik, S Paul, C Augustine, G Cauwenberghs, Biomedical Circuits and Systems Conference (BioCAS), 2016 IEEE.
  • Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity. BU Pedroni, S Sheik, S Joshi, G Detorakis, S Paul, C Augustine, E Neftci, G Cauwenberghs, (also on arXiv), Biomedical Circuits and Systems Conference (BioCAS), 2016 IEEE,.
  • Synaptic Sampling in Hardware Spiking Neural Networks. Sadique Sheik, Somnath Paul, Charles Augustine, Chinnikrishna Kothapalli, Muhammad M Khellah, Gert Cauwenberghs, Emre Neftci, 2016 IEEE International Symposium on Circuits and Systems (ISCAS), 2016 IEEE.
  • Reservoir Computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring. J Fonollosa, S Sheik, R Huerta, S Marco, Sensors and Actuators B: Chemical, 2014
  • Continuous Prediction in Chemoresistive Gas Sensors Using Reservoir Computing. S Sheik, S Marco, R Huerta, J Fonollosa, Procedia Engineering 87, 843-846, 2014
  • PyNCS: a microkernel for high-level definition and configuration of neuromorphic electronic systems. F Stefanini, E Neftci, S Sheik, G Indiveri, Frontiers in neuroinformatics, 2014
  • Autonomous learning in neuromorphic systems for recognition of spatio-temporal spike patterns S Sheik ETH-Zürich, 2013 (PhD Thesis)
  • Spatio-temporal spike pattern classification in neuromorphic systems. S Sheik, M Pfeiffer, F Stefanini, G Indiveri, Biomimetic and Biohybrid Systems, 262-273, 2013
  • A robust sound perception model suitable for neuromorphic implementation. M Coath, S Sheik, E Chicca, G Indiveri, SL Denham, T Wennekers. Frontiers in neuroscience 2013
  • Exploiting device mismatch in neuromorphic VLSI systems to implement axonal delays S Sheik, E Chicca, G Indiveri Neural Networks (IJCNN), The 2012 International Joint Conference on, 1-6 2012
  • Emergent auditory feature tuning in a real-time neuromorphic VLSI system S Sheik, M Coath, G Indiveri, SL Denham, T Wennekers, E Chicca Frontiers in Neuromorphic Engineering 2012
  • Systematic configuration and automatic tuning of neuromorphic systems S Sheik, F Stefanini, E Neftci, E Chicca, G Indiveri Circuits and Systems (ISCAS), 2011 IEEE International Symposium on, 873-876 6 2011
  • A Model of Stimulus-Specific Adaptation in Neuromorphic Analog VLSI R Mill, S Sheik, G Indiveri, SL Denham Biomedical Circuits and Systems, IEEE Transactions on 5 (5), 413-419 2 2011
  • A model of stimulus-specific adaptation in neuromorphic a VLSI R Mill, S Sheik, G Indiveri, SL Denham Biomedical Circuits and Systems Conference (BioCAS), 2010 IEEE, 266-269
  • Augustine, C., Paul, S., Sheik, S.U.A. and Khellah, M.M., Intel Corp, 2018. "Post synaptic potential-based learning rule." U.S. Patent Application 15/584,510.
  • S. Paul. C. Augustine, M. Khellah, S. Sheik, "Technologies for memory management of neural networks with sparse connectivity",US Patent 20,170,277,628, - 2017
  • C. Augustine, S. Paul, S. Sheik, M. Khellah, "Apparatuses, methods, and systems for stochastic memory circuits using magnetic tunnel junctions", US Patent 9,734,880
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