Odor pattern recognition of olfactory neural network based on neural energy

Every sensory neural system falls under the purview of large-scale neuroscience. The olfactory neural system, as a paradigm within this field, encounters challenges akin to other sensory models, including intricate model construction and the difficulty of aligning computational outcomes with experimental data. Some outcomes, despite their theoretical significance, demand excessive computational resources, presenting formidable barriers. Hence, unraveling the potential mechanisms of olfactory information processing and achieving precise odor identification remain daunting tasks. This article proposes a neural energy theory applicable to large-scale neuroscience research on odor recognition and coding in the olfactory system. Utilizing the W–Z neuron energy model, we developed a neural network model of the olfactory system based on its anatomical structure. By computing the total energy spike sequences for various odors in the piriform cortex and employing kernel function methods for odor pattern recognition in mixtures, we discussed the nonlinear energy coding characteristics of odors in the piriform cortex. Our findings suggest that utilizing the total energy of the olfactory system network for pattern recognition of external odor inputs can yield effective, straightforward, and reliable identification results. This research approach not only harmonizes computational outcomes of olfactory models across different levels but also offers the potential for analyzing and interpreting experimental data obtained at various levels within an energy-centric framework in the future. This underscores the advantage of large-scale neuroscience.