Elsevier

Current Opinion in Insect Science

Volume 6, December 2014, Pages 80-85
Current Opinion in Insect Science

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

https://doi.org/10.1016/j.cois.2014.10.005Get rights and content

Highlights

  • 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.

Introduction

Decision making is a central process in the brain, enabling living systems to identify objects and scenarios, choose among alternatives, and decide how and when to react 1, 2, 3, 4, 5, 6. Survival depends on the ability to make decisions and its adaptation to different environments. Such processes commonly rely on two critical components [7]: (i) the prediction of environmental changes (regression), and (ii) the recognition of patterns to discriminate situations (classification). Both functions are solved based on the information obtained by sensory circuits. This sensory modality, present in all forms of life, is central for a wide range of tasks in the insect brain and takes a major share of the neural circuits 7, 8.

The nature of the olfactory stimulus is stochastic and non-stationary: wind transports gases by turbulent flows that induce complex filaments 9, 10, 11 (see Figure 1). Although pattern recognition of gases is challenging for modern artificial sensors 9, 12, evolution has provided even the simplest nervous systems with the ability to extract all necessary information for survival by exploiting the random nature of the stimuli 13, 14.

Our goal here is to review the state of the art in computational models in insect olfaction related to decision making functions. Since the main centers of learning and memory are the Mushroom Bodies (MBs) 16, 7, this review will mostly concentrate on relating the Antennal Lobe (AL) and MB functions.

Section snippets

Antennal Lobe function: feature extraction

Thanks to the simplicity of the structural organization, the nature of the neural coding, genetic manipulation techniques, and extensive odor conditioning experiments, the main brain modules involved in olfactory pattern recognition have been identified: the Antennae are sensors, capturing odor information through olfactory receptor cells; the ALs and MBs are respectively feature extraction and pattern recognition devices. Specifically, the AL receives input from the receptor cells that deliver

The computational blueprints of the Mushroom Bodies

Even in honeybees, insects with no more than a million neurons [8], 35% of its neurons are in the MBs. The MBs integrate multimodal information (idea used in computational models [49]) and are at the focal point of learning and memory 50, 51, 16. They also undergo significant synaptic and neural changes mediated by behavioral odor conditioning experiments 52, 53, 33.

Before reaching the MBs (see Figure 2), the olfactory information travels from the Antenna (representing 20% of the insect brain)

Mushroom Body function: Pattern Recognition

For illustration purposes let us show how connectionist approaches do indeed solve pattern recognition problems. In 59, 54, 4 a basic model of learning in the MBs based on mutual inhibition in the output layer was proposed. Each component of the NO-dimension output vector z of the MB (Figure 2) is

zl=Θj=1NKCwlj·yjμk=1NOj=1NKCwkj·yj=Θzˆl,with l = 1, …, NO and zˆl being the synaptic input to the lth output neuron. The function Θ(x) is a non-linear step function, which defines the spiking

Discussion

An insect, during the decision making process, requires to know at least three important pieces of information to choose an action: odor identification or pattern recognition (what food), estimating concentrations (how much food), and distance to source (how far). The odor discrimination function is employed to know whether the odorant is of interest to the insect, the problem of regression or odor concentration estimation is required to know how much food is available to harvest, and the

Acknowledgements

TS Mosqueiro acknowledges support CAPES --process 99999.014572/2013-03. R Huerta acknowledges partial support from NIDCD R01DC011422.

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