Neural Networks may be made much faster and more efficient by reducing the amount of memory and computation used. In this paper, a new type of neural network called an Adaptive Neural Network is introduced. The proposed neural network is comprised of five unique pairings of events. Each pairing is a module and the modules are connected within a single neural network. The pairings are a simulation of respondent conditioning. The simulations do not necessarily represent conditioning in actual organisms. In the theory presented here, the pairings in respondent conditioning become aggregated together to form a basis for operant conditioning. The specific pairings are as follows. The first pairing is between the reinforcer and the neural stimulus that elicits the behavior. This pairing strengthens and makes salient that eliciting neural stimulus. The second pairing is that of the now salient neural stimulus with the external environmental stimulus that precedes the operant behavior. The third is the pairing of the environmental stimulus event with the reinforcing stimulus. The fourth is the pairing of the stimulus elicited by the drive with the reinforcement event, changing the strength of the reinforcer. The fifth pairing is that after repeated exposure the external environmental stimulus is paired with the drive stimulus. This drive stimulus is generated by an intensifying drive.

Within each module, a “0” means no occurrence of a pairing  A of Stimuli A and a “1” means an occurrence of a pairing  A of Stimuli A. Similarly, a “0” means no occurrence of a pairing  B and a “1” means an occurrence of a pairing  B , and so on for all 5 pairings. To obtain an output one multiplies the values of pairings through E . In one trial or instance, all 5 pairings will occur. The results of the multiplications are then accumulated and divided by the number of instances. The use of these simple respondent pairings as a basis for neural networks reduces errors. Examples of problems that may be addressable by such networks are included.