Corpus ID: 39513138

Methods for applying the Neural Engineering Framework to neuromorphic hardware

@article{Voelker2017MethodsFA,
  title={Methods for applying the Neural Engineering Framework to neuromorphic hardware},
  author={Aaron R. Voelker and C. Eliasmith},
  journal={ArXiv},
  year={2017},
  volume={abs/1708.08133}
}
We review our current software tools and theoretical methods for applying the Neural Engineering Framework to state-of-the-art neuromorphic hardware. These methods can be used to implement linear and nonlinear dynamical systems that exploit axonal transmission time-delays, and to fully account for nonideal mixed-analog-digital synapses that exhibit higher-order dynamics with heterogeneous time-constants. This summarizes earlier versions of these methods that have been discussed in a more… Expand
7 Citations
Robust robotic control on the neuromorphic research chip Loihi
  • 3
  • PDF
Dynamical Systems in Spiking Neuromorphic Hardware
  • 8
Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells
  • 19
  • PDF
IEEE 754 floating-point addition for neuromorphic architecture
  • 3
  • PDF
Robust Trajectory Generation for Robotic Control on the Neuromorphic Research Chip Loihi
  • 2

References

SHOWING 1-10 OF 30 REFERENCES
Extending the neural engineering framework for nonideal silicon synapses
  • 17
  • PDF
A wafer-scale neuromorphic hardware system for large-scale neural modeling
  • 494
  • PDF
Mapping arbitrary mathematical functions and dynamical systems to neuromorphic VLSI circuits for spike-based neural computation
  • 27
  • PDF
Silicon Neurons That Compute
  • 93
  • PDF
An efficient SpiNNaker implementation of the Neural Engineering Framework
  • 43
  • Highly Influential
Efficient SpiNNaker simulation of a heteroassociative memory using the Neural Engineering Framework
  • 16
A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses
  • 353
  • PDF
A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm
  • 24
  • PDF
Improving Spiking Dynamical Networks: Accurate Delays, Higher-Order Synapses, and Time Cells
  • 19
  • PDF
Point Neurons with Conductance-Based Synapses in the Neural Engineering Framework
  • 6
  • PDF
...
1
2
3
...