Is there any intuition for choosing between a multi-D
Ensemble vs. multiple 1-D
EnsembleArray provides a very nice encapsulation of the latter, so the in/out
Connection interface between the two options is similar.
- representation efficiency: accuracy vs. number of neurons
- CPU performance: build time (w/ and w/o caching) and run time
- compatibility with various backends, including the neuromorphic hardware
Example NEF applications:
- representing RGB values over a high-D feature, like a color image
- ImageNet images would be 3x482x418 = 604k variables
- representing a vector field over a volume (i.e. 3 scalar functions defined over a grid)
- A volume with 40 grid points per dimension would be 3x40x40x40 = 192k variables
The key difference there is that RGB is just a representation that likely feeds forward into another layer, whereas a vector field could use recurrent connectivity to, for example, calculate a divergence function.