Model of Neuromorphic Odorant-Recognition Network

We propose a new model for a neuromorphic olfactory analyzer based on memristive synapses. The model comprises a layer of receptive neurons that perceive various odors and a layer of decoder neurons that recognize these odors. It is demonstrated that connecting these layers with memristive synapses enables the training of the decoder layer to recognize two types of odorants of varying concentrations. In the absence of such synapses, the layer of decoder neurons does not exhibit specificity in recognizing odorants. The recognition of the 'odorant' occurs through the neural activity of a group of decoder neurons that have acquired specificity for the odorant in the learning process. The proposed phenomenological model showcases the potential use of a memristive synapse in practical odorant recognition applications.

Automated summary

We propose a new model for a neuromorphic olfactory analyzer based on memristive synapses. The model consists of receptive neurons that perceive various odors and decoder neurons that recognize these odors. Experimental results demonstrate that connecting these layers with memristive synapses allows the training of decoder neurons to recognize two types of odorants of varying concentrations. Without such synapses, the decoder neuron layer lacks specificity in odorant recognition. Odorant recognition occurs through the neural activity of a group of decoder neurons that have acquired specificity for the odorant in the learning process. The proposed phenomenological model highlights the potential use of memristive synapses in practical odorant recognition applications.