Publication: A morphological learning: Increased memory capacity of neuromorphic systems with binary synapses exploiting AER based reconfiguration
A morphological learning: Increased memory capacity of neuromorphic systems with binary synapses exploiting AER based reconfiguration
Date
Date
Date
Citations
Hussain, S., Gopalakrishnan, R., Basu, A., & Liu, S.-C. (2013). A morphological learning: Increased memory capacity of neuromorphic systems with binary synapses exploiting AER based reconfiguration. 2013 International Joint Conference on Neural Networks, 1–7. https://doi.org/10.1109/IJCNN.2013.6706928
Abstract
Abstract
Abstract
Spiking neurons with lumped nonlinearity representing active dendrites can perform a larger number of input-output mappings than is possible by a neuron with linear synaptic summation of its currents. This is possible due to the additional degree of freedom in such cells-its `morphology' reflected in the number of dendrites and the choice of which inputs form synapses on the same dendrite. We present a hardware friendly algorithm for learning such optimal morphologies utilizing correlations between inputs and dendritic branch activati
Metrics
Views
Additional indexing
Creators (Authors)
- Hussain, S
- Gopalakrishnan, R
- Basu, A
- Liu, S-C
Event Title
Event Title
Event Title
Event Location
Event Location
Event Location
Event Country
Event Country
Event Country
Event Start Date
Event Start Date
Event Start Date
Event End Date
Event End Date
Event End Date
Publisher
Publisher
Publisher
Page range/Item number
Page range/Item number
Page range/Item number
Page end
Page end
Page end
Item Type
Item Type
Item Type
In collections
Language
Language
Language
Date available
Date available
Date available
Series Name
Series Name
Series Name
OA Status
OA Status
OA Status
Publisher DOI
Metrics
Views
Citations
Hussain, S., Gopalakrishnan, R., Basu, A., & Liu, S.-C. (2013). A morphological learning: Increased memory capacity of neuromorphic systems with binary synapses exploiting AER based reconfiguration. 2013 International Joint Conference on Neural Networks, 1–7. https://doi.org/10.1109/IJCNN.2013.6706928
