  This paper introduces a new neural network model that aims to mimic the
biological brain more closely by structuring the network as a complete directed
graph that processes continuous data for each timestep. Current neural networks
have structures that vaguely mimic the brain structure, such as neurons,
convolutions, and recurrence. The model proposed in this paper adds additional
structural properties by introducing cycles into the neuron connections and
removing the sequential nature commonly seen in other network layers.
Furthermore, the model has continuous input and output, inspired by spiking
neural networks, which allows the network to learn a process of classification,
rather than simply returning the final result.
