Learning and encoding higher order rules in neural networks

Some researchers state that whereas neural networks are fine for pattern recognition and categorization, complex rule formation requires a separate “symbolic” level. However, the human brain is a connectionist system and, however imperfectly, does complex reasoning and inference. Familiar modeling principles (e.g., Hebbian or associative learning, lateral inhibition, opponent processing, neuromodulation) could recur, in different combinations, in architectures that can learn diverse rules. These rules include, for example, “go to the most novel object,” “alternate between two given objects,” and “touch three given objects, without repeats, in any order.” Frontal lobe damage interferes with learning all three of those rules. Hence, network models of rule learning and encoding should include a module analogous to the prefrontal cortex. They should also include modules analogous to the hippocampus for episode setting and analogous to the amygdala for emotional evaluation.