Stimulus space complexity determines the ratio of specialist and generalist neurons during pattern recognition

Neural processing layers built on divergent connectivity patterns display two types of stimulus-dependent responses: neurons that react to a few stimuli, specialists, and other ones that respond to a wide range of inputs, generalists. Specialists are essential for the discrimination of stimuli and generalists extract common and generic properties from them. This neural heterogeneity could have emerged because of animal adaptation to the environment. Thus, we suggest that there is a relationship between the percentage of specialists and generalists and the stimulus complexity. In order to study this possible relationship, we use patterns with different complexities in a bio-inspired neural network and calculate their classification errors for different ratios of these types of neurons. This study shows that, when the complexity of the stimuli is low, the minimum classification error is achieved with almost any specialist-generalist ratio. Thus, in this case, the role of these neurons during pattern recognition is unspecific. When this complexity is intermediate, both are needed to minimize the classification error, usually in a similar proportion. For increasing stimulus complexity, the importance of generalists decreases, until their relevance is fully nullified when the complexity is high. Therefore, if we adjust the specialist-generalist ratio to the complexity of patterns, we can build more effective neural networks for pattern recognition. Finally, we propose an estimation of stimulus complexity based on the proportion of these types of neurons observed by neural recordings. This offers the possibility to evaluate the stimulus complexity to which animals are adapted.