Neural processing layers built on divergent connectivity patterns display two types of stimulus-d... more 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.
Bioinspired Neural Networks have in many instances paved the way for significant discoveries in S... more Bioinspired Neural Networks have in many instances paved the way for significant discoveries in Statistical and Machine Learning. Among the many mechanisms employed by biological systems to implement learning, gain control is a ubiquitous and essential component that guarantees standard representation of patterns for improved performance in pattern recognition tasks. Gain control is particularly important for the identification of different odor molecules, regardless of their concentration. In this paper, we explore the functional impact of a biologically plausible model of the gain control on classification performance by representing the olfactory system of insects with a Single Hidden Layer Network (SHLN). Common to all insects, the primary olfactory pathway starts at the Antennal Lobes (ALs) and, then, odor identity is computed at the output of the Mushroom Bodies (MBs). We show that gain-control based on lateral inhibition in the Antennal Lobe robustly solves the classification of highly-concentrated odors. Furthermore, the proposed mechanism does not depend on learning at the AL level, in agreement with biological literature. Due to its simplicity, this bioinspired mechanism may not only be present in other neural systems but can also be further explored for applications, for instance, involving electronic noses.
Neural processing layers built on divergent connectivity patterns display two types of stimulus-d... more 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.
Bioinspired Neural Networks have in many instances paved the way for significant discoveries in S... more Bioinspired Neural Networks have in many instances paved the way for significant discoveries in Statistical and Machine Learning. Among the many mechanisms employed by biological systems to implement learning, gain control is a ubiquitous and essential component that guarantees standard representation of patterns for improved performance in pattern recognition tasks. Gain control is particularly important for the identification of different odor molecules, regardless of their concentration. In this paper, we explore the functional impact of a biologically plausible model of the gain control on classification performance by representing the olfactory system of insects with a Single Hidden Layer Network (SHLN). Common to all insects, the primary olfactory pathway starts at the Antennal Lobes (ALs) and, then, odor identity is computed at the output of the Mushroom Bodies (MBs). We show that gain-control based on lateral inhibition in the Antennal Lobe robustly solves the classification of highly-concentrated odors. Furthermore, the proposed mechanism does not depend on learning at the AL level, in agreement with biological literature. Due to its simplicity, this bioinspired mechanism may not only be present in other neural systems but can also be further explored for applications, for instance, involving electronic noses.
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Papers by Aaron Montero