Computational models to understand decision making and pattern recognition in the insect brain

• We provide an integrative overview of the computational literature from the pattern recognition point of view.
• We summarize the computational elements and architecture of the Mushroom Bodies required for solving odor discrimination tasks inspired by a half a century of experimental discoveries.
• We show how computational models help to generate new hypotheses that can be experimentally verified. Examples on the role of lateral inhibition and neural circuit robustness are provided.
• We highlight the challenges and gaps that the computational models currently face and suggest research directions where computational models can be helpful.

Odor stimuli reaching olfactory systems of mammals and insects are characterized by remarkable non-stationary and noisy time series. Their brains have evolved to discriminate subtle changes in odor mixtures and find meaningful variations in complex spatio-temporal patterns. Insects with small brains can effectively solve two computational tasks: identify the presence of an odor type and estimate the concentration levels of the odor. Understanding the learning and decision making processes in the insect brain can not only help us to uncover general principles of information processing in the brain, but it can also provide key insights to artificial chemical sensing. Both olfactory learning and memory are dominantly organized in the Antennal Lobe (AL) and the Mushroom Bodies (MBs). Current computational models yet fail to deliver an integrated picture of the joint computational roles of the AL and MBs. This review intends to provide an integrative overview of the computational literature analyzed in the context of the problem of classification (odor discrimination) and regression (odor concentration estimation), particularly identifying key computational ingredients necessary to solve pattern recognition.