Narayan Srinivasa, Ph.D.

Affiliations: 
1994 Mechanical Engineering University of Florida, Gainesville, Gainesville, FL, United States 
 1995-1998 University of Illinois, Urbana-Champaign, Urbana-Champaign, IL 
 1999-2015 HRL Laboratories 
Area:
Neural Networks
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"Narayan Srinivasa"
Mean distance: 18.84 (cluster 15)
 
SNBCP

Parents

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John C. Ziegert grad student 1994 UF Gainesville (Physics Tree)
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Publications

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Pilly PK, Stepp ND, Liapis Y, et al. (2019) Hypercolumn Sparsification for Low-Power Convolutional Neural Networks Acm Journal On Emerging Technologies in Computing Systems. 15: 1-16
Srinivasa N, Stepp ND, Cruz-Albrecht J. (2015) Criticality as a Set-Point for Adaptive Behavior in Neuromorphic Hardware. Frontiers in Neuroscience. 9: 449
Stepp N, Plenz D, Srinivasa N. (2015) Synaptic plasticity enables adaptive self-tuning critical networks. Plos Computational Biology. 11: e1004043
Srinivasa N, Cho Y. (2014) Unsupervised discrimination of patterns in spiking neural networks with excitatory and inhibitory synaptic plasticity. Frontiers in Computational Neuroscience. 8: 159
O'Brien MJ, Thibeault CM, Srinivasa N. (2014) A novel analytical characterization for short-term plasticity parameters in spiking neural networks. Frontiers in Computational Neuroscience. 8: 148
Thibeault CM, Harris FC, Srinivasa N. (2014) Using games to embody spiking neural networks for neuromorphic hardware International Journal of Computers and Their Applications. 21: 40-53
Thibeault CM, Srinivasa N. (2013) Using a hybrid neuron in physiologically inspired models of the basal ganglia. Frontiers in Computational Neuroscience. 7: 88
Thibeault CM, Harris FC, Srinivasa N. (2013) Embodied modeling with spiking neural networks for neuromorphic hardware: A simulation study 26th International Conference On Computer Applications in Industry and Engineering, Caine 2013. 3-10
Stepp N, Srinivasa N. (2012) A Formal Model for Autocatakinetic Systems Ecological Psychology. 24: 204-219
Srinivasa N, Ahuja N. (1999) A topological and temporal correlator network for spatiotemporal pattern learning, recognition, and recall Ieee Transactions On Neural Networks. 10: 356-371
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