All frameworks have limitations. Although I ask a lot of questions on this site in regards to the advantages of the Neural Engineering Framework (NEF), there must be significant limitations. What are they?
The limitations of the NEF fall into three main categories.
1. Simplistic Use of Neurotransmitters
The NEF uses neurotransmitters and their rate of propagation to set a time constant on an exponential low-pass filter for spikes, as well as for modulation of learning rules (dopamine). This is currently the only use of neurotransmitters in the NEF, despite their being many functions that they could serve in terms of signalling and modulation.
2. Limitations When Using Complex Neuron Models
Although different filters are being investigated by Aaron Voelker, the vast majority of filters used, as mentioned in the last point, still rely on the single time constant and on linear decoding. More complex neural models, although easily included in a feed-forward connection, usually have multiple time constants and non-linear synpatic effects, which make it very difficult to capture their dynamics. This is covered in chapter 4 of Bryan Tripp's PhD thesis.
3. Lack of Developmental Explanation
This last problem may be more of a reflection on the state of neuroscience, than an actual limitation of the NEF.
The NEF describes fully formed, adaptive, but non-developmental networks. Although the NEF can learn functions, it does not describe how certain structures such as the Basal Ganglia came to be formed. Neither does it give any description of how neurogenesis might occur within the framework, although it is easy to imagine how it might be leveraged to increase accuracy of certain populations.
Although the NEF is the only framework I know of that tries base behaviour on a biologically plausible substrate, it is not the silver bullet of theoretical neuroscience. It is very much a zeroth-order guess at the computations that the brain my perform.