Computing this solution is based on solving a set of non-linear algebraic equations whose parameters are related to the spiking rates of individual cells and their connectivity to other cells, as well as the arrival rates of spikes from outside the network.
The RNN is a recurrent model, i.e. a neural network that is allowed to have complex feedback loops.
[2] A highly energy-efficient implementation of random neural networks was demonstrated by Krishna Palem et al. using the Probabilistic CMOS or PCMOS technology and was shown to be c. 226–300 times more efficient in terms of Energy-Performance-Product.
The learning algorithm for an n-node random neural network that includes feedback loops (it is also a recurrent neural network) is of computational complexity O(n^3) (the number of computations is proportional to the cube of n, the number of neurons).
The RNN has been shown to be a universal approximator for bounded and continuous functions.