Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities

Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor

Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.

Future avenues in Drosophila mushroom body research

How does the brain translate sensory information into complex behaviors? With relatively small neuronal numbers, readable behavioral outputs, and an unparalleled genetic toolkit, the Drosophila mushroom body (MB) offers an excellent model to address this question in the context of associative learning and memory. Recent technological breakthroughs, such as the freshly completed full-brain connectome, multiomics approaches, CRISPR-mediated gene editing, and machine learning techniques, led to major advancements in our understanding of the MB circuit at the molecular, structural, physiological, and functional levels. Despite significant progress in individual MB areas, the field still faces the fundamental challenge of resolving how these different levels combine and interact to ultimately control the behavior of an individual fly. In this review, we discuss various aspects of MB research, with a focus on the current knowledge gaps, and an outlook on the future methodological developments required to reach an overall view of the neurobiological basis of learning and memory.

Invasive ant learning is not affected by seven potential neuroactive chemicals

Argentine ants Linepithema humile are one of the most damaging invasive alien species worldwide. Enhancing or disrupting cognitive abilities, such as learning, has the potential to improve management efforts, for example by increasing preference for a bait, or improving ants' ability to learn its characteristics or location. Nectar-feeding insects are often the victims of psychoactive manipulation, with plants lacing their nectar with secondary metabolites such as alkaloids and non-protein amino acids which often alter learning, foraging, or recruitment. However, the effect of neuroactive chemicals has seldomly been explored in ants. Here, we test the effects of seven potential neuroactive chemicals-two alkaloids: caffeine and nicotine; two biogenic amines: dopamine and octopamine, and three nonprotein amino acids: β-alanine, GABA and taurine-on the cognitive abilities of invasive L. humile using bifurcation mazes. Our results confirm that these ants are strong associative learners, requiring as little as one experience to develop an association. However, we show no short-term effect of any of the chemicals tested on spatial learning, and in addition no effect of caffeine on short-term olfactory learning. This lack of effect is surprising, given the extensive reports of the tested chemicals affecting learning and foraging in bees. This mismatch could be due to the heavy bias towards bees in the literature, a positive result publication bias, or differences in methodology.

The Relationship between Cognition and Brain Size or Neuron Number

The comparative approach is a powerful way to explore the relationship between brain structure and cognitive function. Thus far, the field has been dominated by the assumption that a bigger brain somehow means better cognition. Correlations between differences in brain size or neuron number between species and differences in specific cognitive abilities exist, but these correlations are very noisy. Extreme differences exist between clades in the relationship between either brain size or neuron number and specific cognitive abilities. This means that correlations become weaker, not stronger, as the taxonomic diversity of sampled groups increases. Cognition is the outcome of neural networks. Here we propose that considering plausible neural network models will advance our understanding of the complex relationships between neuron number and different aspects of cognition. Computational modelling of networks suggests that adding pathways, or layers, or changing patterns of connectivity in a network can all have different specific consequences for cognition. Consequently, models of computational architecture can help us hypothesise how and why differences in neuron number might be related to differences in cognition. As methods in connectomics continue to improve and more structural information on animal brains becomes available, we are learning more about natural network structures in brains, and we can develop more biologically plausible models of cognitive architecture. Natural animal diversity then becomes a powerful resource to both test the assumptions of these models and explore hypotheses for how neural network structure and network size might delimit cognitive function.

How honey bees make fast and accurate decisions

Honey bee ecology demands they make both rapid and accurate assessments of which flowers are most likely to offer them nectar or pollen. To understand the mechanisms of honey bee decision-making, we examined their speed and accuracy of both flower acceptance and rejection decisions. We used a controlled flight arena that varied both the likelihood of a stimulus offering reward and punishment and the quality of evidence for stimuli. We found that the sophistication of honey bee decision-making rivalled that reported for primates. Their decisions were sensitive to both the quality and reliability of evidence. Acceptance responses had higher accuracy than rejection responses and were more sensitive to changes in available evidence and reward likelihood. Fast acceptances were more likely to be correct than slower acceptances; a phenomenon also seen in primates and indicative that the evidence threshold for a decision changes dynamically with sampling time. To investigate the minimally sufficient circuitry required for these decision-making capacities, we developed a novel model of decision-making. Our model can be mapped to known pathways in the insect brain and is neurobiologically plausible. Our model proposes a system for robust autonomous decision-making with potential application in robotics.

Chronic larval exposure to thiacloprid impairs honeybee antennal selectivity, learning and memory performances

The use of agricultural neonicotinoid insecticides has sub-lethal chronic effects on bees that are more prevalent than acute toxicity. Among these insecticides, thiacloprid, a commonly used compound with low toxicity, has attracted significant attention due to its potential impact on the olfactory and learning abilities of honeybees. The effect of sub-lethal larval exposure to thiacloprid on the antennal activity of adult honeybees (Apis mellifera L.) is not yet fully understood. To address this knowledge gap, laboratory-based experiments were conducted in which honeybee larvae were administered thiacloprid (0.5 mg/L and 1.0 mg/L). Using electroantennography (EAG), the impacts of thiacloprid exposure on the antennal selectivity to common floral volatiles were evaluated. Additionally, the effects of sub-lethal exposure on odor-related learning and memory were also assessed. The results of this study reveal, for the first time, that sub-lethal larval exposure to thiacloprid decreased honeybee antenna EAG responses to floral scents, leading to increased olfactory selectivity in the high-dose (1.0 mg/L) group compared to the control group (0 mg/L vs. 1.0 mg/L: p = 0.042). The results also suggest that thiacloprid negatively affected odor-associated paired learning acquisition, as well as medium-term (1 h) (0 mg/L vs. 1.0 mg/L: p = 0.019) and long-term memory (24 h) (0 mg/L vs. 1.0 mg/L: p = 0.037) in adult honeybees. EAG amplitudes were dramatically reduced following R-linalool paired olfactory training (0 mg/L vs. 1.0 mg/L: p = 0.001; 0 mg/L vs. 0.5 mg/L: p = 0.027), while antennal activities only differed significantly in the control between paired and unpaired groups. Our results indicated that exposure to sub-lethal concentrations of thiacloprid may affect olfactory perception and learning and memory behaviors in honeybees. These findings have important implications for the safe use of agrochemicals in the environment.

Honey bees respond to multimodal stimuli following the Principle of Inverse Effectiveness

Multisensory integration is assumed to entail benefits for receivers across multiple ecological contexts. However, signal integration effectiveness is constrained by features of the spatiotemporal and intensity domains. How sensory modalities are integrated during tasks facilitated by learning and memory, such as pollination, remains unsolved. Honey bees use olfactory and visual cues during foraging, making them a good model to study the use of multimodal signals. Here, we examined the effect of stimulus intensity on both learning and memory performance of bees trained using unimodal or bimodal stimuli. We measured the performance and the latency response across planned discrete levels of stimulus intensity. We employed the conditioning of the proboscis extension response protocol in honey bees using an electromechanical setup allowing us to control simultaneously and precisely olfactory and visual stimuli at different intensities. Our results show that the bimodal enhancement during learning and memory was higher as the intensity decreased when the separate individual components were least effective. Still, this effect was not detectable for the latency of response. Remarkably, these results support the principle of inverse effectiveness, traditionally studied in vertebrates, predicting that multisensory stimuli are more effectively integrated when the best unisensory response is relatively weak. Thus, we argue that the performance of the bees while using a bimodal stimulus depends on the interaction and intensity of its individual components. We further hold that the inclusion of findings across all levels of analysis enriches the traditional understanding of the mechanics and reliance of complex signals in honey bees.

Summary: Bimodal enhancement during learning and memory tasks in africanized honey bees increases as the stimulus intensity of its unimodal components decreases; this indicates that learning performance depends on the interaction between the intensity of its components and the nature of the sensory modalities involved, supporting the principle of inverse effectiveness.

Ants resort to heuristics when facing relational-learning tasks they cannot solve

We humans sort the world around us into conceptual groups, such as 'the same' or 'different', which facilitates many cognitive tasks. Applying such abstract concepts can improve problem-solving success and is therefore worth the cognitive investment. In this study, we investigated whether ants (Lasius niger) can learn the relational rule of 'the same' or 'different' by training them in an odour match-to-sample test over 48 visits. While ants in the 'different' treatment improved significantly over time, reaching around 65% correct decisions, ants in the 'same' treatment did not. Ants did not seem able to learn such abstract relational concepts, but instead created their own individual strategy to try to solve the problem: some ants decided to 'always go left', others preferred a 'go to the more salient cue' heuristic which systematically biased their decisions. These heuristics even occasionally lowered the success rate in the experiment below chance, indicating that following any rule may be more desirable then making truly random decisions. As the finding that ants resort to heuristics when facing hard-to-solve decisions was discovered post-hoc, we strongly encourage other researchers to ask whether employing heuristics in the face of challenging tasks is a widespread phenomenon in insects.

Bumblebees Learn a Relational Rule but Switch to a Win-Stay/Lose-Switch Heuristic After Extensive Training

Mapping animal performance in a behavioral task to underlying cognitive mechanisms and strategies is rarely straightforward, since a task may be solvable in more than one manner. Here, we show that bumblebees perform well on a concept-based visual discrimination task but spontaneously switch from a concept-based solution to a simpler heuristic with extended training, all while continually increasing performance. Bumblebees were trained in an arena to find rewards on displays with shapes of different sizes where they could not use low-level visual cues. One group of bees was rewarded at displays with larger shapes and another group at displays with smaller shapes. Analysis of total choices shows bees increased their performance over 30 bouts to above chance. However, analyses of first and sequential choices suggest that after approximately 20 bouts, bumblebees changed to a win-stay/lose-switch strategy. Comparing bees' behavior to a probabilistic model based on a win-stay/lose-switch strategy further supports the idea that bees changed strategies with extensive training. Analyses of unrewarded tests indicate that bumblebees learned and retained the concept of relative size even after they had already switched to a win-stay, lost-shift strategy. We propose that the reason for this strategy switching may be due to cognitive flexibility and efficiency.

Bumblebees Use Sequential Scanning of Countable Items in Visual Patterns to Solve Numerosity Tasks

Most research in comparative cognition focuses on measuring if animals manage certain tasks; fewer studies explore how animals might solve them. We investigated bumblebees’ scanning strategies in a numerosity task, distinguishing patterns with two items from four and one from three, and subsequently transferring numerical information to novel numbers, shapes, and colors. Video analyses of flight paths indicate that bees do not determine the number of items by using a rapid assessment of number (as mammals do in “subitizing”); instead, they rely on sequential enumeration even when items are presented simultaneously and in small quantities. This process, equivalent to the motor tagging (“pointing”) found for large number tasks in some primates, results in longer scanning times for patterns containing larger numbers of items. Bees used a highly accurate working memory, remembering which items have already been scanned, resulting in fewer than 1% of re-inspections of items before making a decision. Our results indicate that the small brain of bees, with less parallel processing capacity than mammals, might constrain them to use sequential pattern evaluation even for low quantities.

The case for emulating insect brains using anatomical "wiring diagrams" equipped with biophysical models of neuronal activity

Developing whole-brain emulation (WBE) technology would provide immense benefits across neuroscience, biomedicine, artificial intelligence, and robotics. At this time, constructing a simulated human brain lacks feasibility due to limited experimental data and limited computational resources. However, I suggest that progress toward this goal might be accelerated by working toward an intermediate objective, namely insect brain emulation (IBE). More specifically, this would entail creating biologically realistic simulations of entire insect nervous systems along with more approximate simulations of non-neuronal insect physiology to make "virtual insects." I argue that this could be realistically achievable within the next 20 years. I propose that developing emulations of insect brains will galvanize the global community of scientists, businesspeople, and policymakers toward pursuing the loftier goal of emulating the human brain. By demonstrating that WBE is possible via IBE, simulating mammalian brains and eventually the human brain may no longer be viewed as too radically ambitious to deserve substantial funding and resources. Furthermore, IBE will facilitate dramatic advances in cognitive neuroscience, artificial intelligence, and robotics through studies performed using virtual insects.

Adaptive integrate-and-fire model reproduces the dynamics of olfactory receptor neuron responses in a moth

In order to understand how olfactory stimuli are encoded and processed in the brain, it is important to build a computational model for olfactory receptor neurons (ORNs). Here, we present a simple and reliable mathematical model of a moth ORN generating spikes. The model incorporates a simplified description of the chemical kinetics leading to olfactory receptor activation and action potential generation. We show that an adaptive spike threshold regulated by prior spike history is an effective mechanism for reproducing the typical phasic-tonic time course of ORN responses. Our model reproduces the response dynamics of individual neurons to a fluctuating stimulus that approximates odorant fluctuations in nature. The parameters of the spike threshold are essential for reproducing the response heterogeneity in ORNs. The model provides a valuable tool for efficient simulations of olfactory circuits.

How foresight might support the behavioral flexibility of arthropods

The small brains of insects and other invertebrates are often thought to constrain these animals to live entirely 'in the moment'. In this view, each one of their many seemingly hard-wired behavioral routines is triggered by a precisely defined environmental stimulus configuration, but there is no mental appreciation of the possible outcomes of one's actions, and therefore little flexibility. However, many studies show problem-solving behavior in various arthropod species that falls outside the range of fixed behavior routines. We propose that a basic form of foresight, the ability to predict the outcomes of one's own actions, is at the heart of such behavioral flexibility, and that the evolutionary roots of such outcome expectation are found in the need to disentangle sensory input that is predictable from self-generated motion versus input generated by changes in the outside world. Based on this, locusts, grasshoppers, dragonflies and flies seem to use internal models of the surrounding world to tailor their actions adaptively to predict the imminent future. Honeybees and orb-weaving spiders appear to act towards a desired outcome of their respective constructions, and the genetically pre-programmed routines that govern these constructions are subordinate to achieving the desired goal. Jumping spiders seem to preplan their route to prey suggesting they recognize the spatial challenge and actions necessary to obtain prey. Bumblebees and ants utilize objects not encountered in the wild as types of tools to solve problems in a manner that suggests an awareness of the desired outcome. Here we speculate that it may be simpler, in terms of the required evolutionary changes, computation and neural architecture, for arthropods to recognize their goal and predict the outcomes of their actions towards that goal, rather than having a large number of pre-programmed behaviors necessary to account for their observed behavioral flexibility.