Computational models to understand decision making and pattern recognition in the insect brain
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
with l = 1, …, NO and 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.
References (78)
- et al.
A model study on the circuit mechanism underlying decision-making in drosophila
Neural Networks
(2011) Cognitive neuroethology: dissecting non-elemental learning in a honeybee brain
Curr Opin Neurobiol
(2003)- et al.
On the performance of gas sensor arrays in open sampling systems using Inhibitory Support Vector Machines
Sens Actuators B: Chem
(2013) - et al.
On the calibration of sensor arrays for pattern recognition using the minimal number of experiments
Chemometr Intell Lab Syst
(2014) - et al.
Odour classification system for continuous monitoring applications
Sens Actuators B: Chem
(2009) - et al.
A chemosensory gene family encoding candidate gustatory and olfactory receptors in drosophila
Cell
(2001) - et al.
A presynaptic gain control mechanism fine-tunes olfactory behavior.
Neuron
(2008) - et al.
A model for temporal and intensity coding in insect olfaction by a network of inhibitory neurons
Biosystems
(1996) - et al.
Model of cellular and network mechanisms for odor-evoked temporal patterning in the locust antennal lobe
Neuron
(2001) - et al.
A computational model of the reponse of honey bee antennal lobe circuitry to odor mixtures: Overshadowing, blocking and unblocking can arise from lateral inhibition
Behav Brain Res
(1997)
Intensity versus identity coding in an olfactory system
Neuron
On learning, information, lateral inhibition, and transmitters
Math Biosci
Is there a support vector machine hiding in the dentate gyrus?
Neurocomputing
Learning in spiking neural networks by reinforcement of stochastic synaptic transmission.
Neuron
Chemical gas sensor drift compensation using classifier ensembles
Sens Actuators B: Chem
Neural response to reward anticipation under risk is nonlinear in probabilities.
J Neurosci
Transient cognitive dynamics, metastability, and decision making
PLoS Comput Biol
The neural basis of decision making
Annu Rev Neurosci
A computational framework for understanding decision making through integration of basic learning rules
J Neurosci
Honey bees selectively avoid difficult choices
Proc Natl Acad Sci U S A
The honeybee as a model for understanding the basis of cognition.
Nat Rev Neurosci
Localization of remote odor sources by metal-oxide gas sensors in turbulent plumes
Measurement of odor-plume structure in a wind tunnel using a photoionization detector and a tracer gas
Environ Fluid Mech
Odour-plume dynamics influence the brain's olfactory code
Nature
Interaction of cellular and network mechanisms for efficient pheromone coding in moths.
Proc Natl Acad Sci U S A
Mushroom body memoir: from maps to models.
Nat Rev Neurosci
Representations of odour mixtures visualized in the honeybee brain
Nature
The glomerular code for odor representation is species specific in the honeybee Apis mellifera
Nat Neurosci
Odor-driven attractor dynamics in the antennal lobe allow for simple and rapid olfactory pattern classification
Neural Comput
Sensory processing in the drosophila antennal lobe increases reliability and separability of ensemble odor representations
Nat Neurosci
Rapid odor processing in the honeybee antennal lobe network
Front Comput Neurosci
Temporal representations of odors in an olfactory network
J Neurosci
Response patterns of amphibian olfactory bulb neurons to odor stimulation
J Physiol Lond
Patterned response to odor in mammalian olfactory bulb: the influence of intensity.
J Neurophysiol
Dynamic optimization of odor representations by slow temporal patterning of mitral cell activity
Science
Structure and response patterns of olfactory interneurons in the honeybee Apis mellifera
J Comp Neurol
Impaired odour discrimination on desynchronization of odour-encoding neural assemblies.
Nature
Role of inhibition for temporal and spatial odor representation in olfactory output neurons: a calcium imaging study
J Neurophysiol
Role of gabaergic inhibition in shaping odor-evoked spatiotemporal patterns in the drosophila antennal lobe
J Neurosci
Cited by (17)
Drug effect and addiction research with insects – From Drosophila to collective reward in honeybees
2022, Neuroscience and Biobehavioral ReviewsDrosophila reward system - A summary of current knowledge
2021, Neuroscience and Biobehavioral ReviewsCitation Excerpt :Secondly, they use concepts from the whole network science to create theoretical frameworks of MB activities, which are concepts that may be extremely difficult to describe when solely depending on biological sciences to analyse the individual network elements independently (overview of basic elements for the architecture of neuro-inspired computational models Patanè et al., 2018a.). The first computational models were based on a modular way of organizing the brain and captured the aforementioned main neuronal connections of the olfactory system as well as some feedback relationships, but excluded many complicated elements (Huerta et al., 2004; Nowotny et al., 2005; Wessnitzer and Webb, 2006; Smith et al., 2008; Arena et al., 2010; review of computational olfaction models of Drosophila Mosqueiro and Huerta, 2014; Gupta et al., 2018). The basic architecture of these models is similar.
A computational model of conditioning inspired by Drosophila olfactory system
2017, Neural NetworksCitation Excerpt :Insects constitute a diverse and successful group of animals with comparatively simple nervous systems; nevertheless their small brains have evolved to provide intelligent solutions to a wide range of problems imposed by changing environments. Such simple brains, comparing to higher vertebrate animals, allow for developing computational models to understand neural system–behavior relationships (Borst, 2014; Gupta & Stopfer, 2014; Mosqueiro & Huerta, 2014a, 2014b). Studies on insect brains suggest that their relatively simple neural circuits regulate different behaviors and adapt the performance of these behaviors by integrating sensory information with memories from previous experiences (Egelhaaf et al., 2014).
Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring
2016, Chemometrics and Intelligent Laboratory SystemsVisual processing and collective motion-related decision-making in desert locusts
2023, Proceedings of the Royal Society B: Biological SciencesDriving Hexapods Through Insect Brain
2023, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)