A CTRNN is a neural network(NN) that allows recurrent(R) connections and considers continuous time(CT) a factor. It is therefore within the class of dynamic neural networks.
As such, the neurons in a CTRNN may change state over time, with or without any interaction from the external environment. This allows CTRNNs to for example simulate short-time memories or provide fault-tolerant sensor readings.
CTRNNs are by many considered to strike the proper balance between real world cell-complexity and model simplicity: Complex enough to account for the real-world, and simple enough to be effectively simulated and understood. The simple encoding of parameters also make them an optimal substrate for evolutionary algorithms (e.g. genetic algorithms), and especially well-suited for artificial life.
CL-CTRNN makes this functionality available through a CLOS API.
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