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

Abstract

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.

The Role of Information Acquisition and Processing in Decision‐Making by Individual within Insects Colonies

“…Many researches seek to unravel the precise decision‐making mechanisms of insects, that is, how insects process the information received and are able to make a decision related to a specific task or action (Beshers and Fewell, ; Marshall et al ., ; Mosqueiro and Huerta, ; Barron et al ., ).…”

“…The processing of information relies on two important processes (Mosqueiro and Huerta, ): the prediction of environmental conditions and changes (regression), and the recognition of patterns to discriminate situations and selection of suitable responses (classification). The information processing and, consequently, the decision‐making process, occurs in the insect brain.…”

“…Res RESEARCH PAPER processing these types of information, public, private and social, the insects analyse the environmental conditions and make a suitable individual decision expressed by an action. Many researches seek to unravel the precise decision-making mechanisms of insects, that is, how insects process the information received and are able to make a decision related to a specific task or action (Beshers and Fewell, 2001;Marshall et al, 2009;Mosqueiro and Huerta, 2014;Barron et al, 2015).…”

“…The understanding of how learning and decision-making processes occur in the insect brain is an important step to uncover the principles of information processing in the brain. Efforts have been made in the search for a better understanding of how the insect brain processes the different stimuli received, especially visual and olfactory stimuli (Beshers and Fewell, 2001;Marshall et al, 2009;Chittka and Skorupski, 2011;Mosqueiro and Huerta, 2014;Barron et al, 2015).…”

Circuit and Cellular Mechanisms Facilitate the Transformation from Dense to Sparse Coding in the Insect Olfactory System

“…In Drosophila, sparse coding was found to reduce overlap between odor representations and facilitate their discrimination (Lin et al, 2014). Consequently, sparse coding is an essential feature of plasticity models for olfactory learning in insects (Huerta and Nowotny, 2009;Wessnitzer et al, 2012;Ardin et al, 2016;Peng and Chittka, 2016;Müller et al, 2018), and theoretical work has emphasized the analogy of the transformation from a dense code in projection neurons (PNs) to a sparse code in Kenyon cells (KCs) with dimensionality expansion in machine learning methods (Huerta and Nowotny, 2009;Mosqueiro and Huerta, 2014;Schmuker et al, 2014).…”