Predictive olfactory learning in Drosophila

Ionotropic receptors mediate olfactory learning and memory in Drosophila

Phenylacetaldehyde (PAH), an aromatic compound, is present in a diverse range of fruits including overripe bananas and prickly pear cactus, the two major host fruits for Drosophila melanogaster. PAH acts as a potent ligand for the ionotropic receptor 84a (IR84a) in the adult fruit fly and it is detected by the IR84a/IR8a heterotetrameric complex. Its role in the male courtship behavior through IR84a as an environmental aphrodisiac is of additional importance. In D. melanogaster, two distinct kinds of olfactory receptors, that is, odorant receptors (ORs) and ionotropic receptors (IRs), perceive the odorant stimuli. They display unique structural, molecular, and functional characteristics in addition to having different evolutionary origins. Traditionally, olfactory cues detected by the ORs such as ethyl acetate, 1-butanol, isoamyl acetate, 1-octanol, 4-methylcyclohexanol, etc. classified as aliphatic esters and alcohols have been employed in olfactory classical conditioning using fruit flies. This underlines the participation of OR-activated olfactory pathways in learning and memory formation. Our study elucidates that likewise ethyl acetate (EA) (an OR-responsive odorant), PAH (an IR-responsive aromatic compound) too can form learning and memory when associated with an appetitive gustatory reinforcer. The association of PAH with sucrose (PAH/SUC) led to learning and formation of the long-term memory (LTM). Additionally, the Orco1, Ir84aMI00501, and Ir8a1 mutant flies were used to confirm the exclusive participation of the IR84a/IR8a complex in PAH/SUC olfactory associative conditioning. These results highlight the involvement of IRs via an IR-activated pathway in facilitating robust olfactory behavior.

Beyond prediction error: 25 years of modeling the associations formed in the insect mushroom body

The insect mushroom body has gained increasing attention as a system in which the computational basis of neural learning circuits can be unraveled. We now understand in detail the key locations in this circuit where synaptic associations are formed between sensory patterns and values leading to actions. However, the actual learning rule (or rules) implemented by neural activity and leading to synaptic change is still an open question. Here, I survey the diversity of answers that have been offered in computational models of this system over the past decades, including the recurring assumption-in line with top-down theories of associative learning-that the core function is to reduce prediction error. However, I will argue, a more bottom-up approach may ultimately reveal a richer algorithmic capacity in this still enigmatic brain neuropil.

Roles of feedback and feed-forward networks of dopamine subsystems: insights from Drosophila studies

Across animal species, dopamine-operated memory systems comprise anatomically segregated, functionally diverse subsystems. Although individual subsystems could operate independently to support distinct types of memory, the logical interplay between subsystems is expected to enable more complex memory processing by allowing existing memory to influence future learning. Recent comprehensive ultrastructural analysis of the Drosophila mushroom body revealed intricate networks interconnecting the dopamine subsystems-the mushroom body compartments. Here, we review the functions of some of these connections that are beginning to be understood. Memory consolidation is mediated by two different forms of network: A recurrent feedback loop within a compartment maintains sustained dopamine activity required for consolidation, whereas feed-forward connections across compartments allow short-term memory formation in one compartment to open the gate for long-term memory formation in another compartment. Extinction and reversal of aversive memory rely on a similar feed-forward circuit motif that signals omission of punishment as a reward, which triggers plasticity that counteracts the original aversive memory trace. Finally, indirect feed-forward connections from a long-term memory compartment to short-term memory compartments mediate higher-order conditioning. Collectively, these emerging studies indicate that feedback control and hierarchical connectivity allow the dopamine subsystems to work cooperatively to support diverse and complex forms of learning.

Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva

Predicting reinforcement from sensory cues is beneficial for goal-directed behavior. In insect brains, underlying associations between cues and reinforcement, encoded by dopaminergic neurons, are formed in the mushroom body. We propose a spiking model of the Drosophila larva mushroom body. It includes a feedback motif conveying learned reinforcement expectation to dopaminergic neurons, which can compute prediction error as the difference between expected and present reinforcement. We demonstrate that this can serve as a driving force in learning. When combined with synaptic homeostasis, our model accounts for theoretically derived features of acquisition and loss of associations that depend on the intensity of the reinforcement and its temporal proximity to the cue. From modeling olfactory learning over the time course of behavioral experiments and simulating the locomotion of individual larvae toward or away from odor sources in a virtual environment, we conclude that learning driven by prediction errors can explain larval behavior.

Minimal circuit motifs for second-order conditioning in the insect mushroom body

In well-established first-order conditioning experiments, the concurrence of a sensory cue with reinforcement forms an association, allowing the cue to predict future reinforcement. In the insect mushroom body, a brain region central to learning and memory, such associations are encoded in the synapses between its intrinsic and output neurons. This process is mediated by the activity of dopaminergic neurons that encode reinforcement signals. In second-order conditioning, a new sensory cue is paired with an already established one that presumably activates dopaminergic neurons due to its predictive power of the reinforcement. We explored minimal circuit motifs in the mushroom body for their ability to support second-order conditioning using mechanistic models. We found that dopaminergic neurons can either be activated directly by the mushroom body's intrinsic neurons or via feedback from the output neurons via several pathways. We demonstrated that the circuit motifs differ in their computational efficiency and robustness. Beyond previous research, we suggest an additional motif that relies on feedforward input of the mushroom body intrinsic neurons to dopaminergic neurons as a promising candidate for experimental evaluation. It differentiates well between trained and novel stimuli, demonstrating robust performance across a range of model parameters.

The mushroom body output encodes behavioral decision during sensory-motor transformation

Animals form a behavioral decision by evaluating sensory evidence on the background of past experiences and the momentary motivational state. In insects, we still lack understanding of how and at which stage of the recurrent sensory-motor pathway behavioral decisions are formed. The mushroom body (MB), a central brain structure in insects1 and crustaceans,2,3 integrates sensory input of different modalities4,5,6 with the internal state, the behavioral state, and external sensory context7,8,9,10 through a large number of recurrent, mostly neuromodulatory inputs,11,12 implicating a functional role for MBs in state-dependent sensory-motor transformation.13,14 A number of classical conditioning studies in honeybees15,16 and fruit flies17,18,19 have provided accumulated evidence that at its output, the MB encodes the valence of a sensory stimulus with respect to its behavioral relevance. Recent work has extended this notion of valence encoding to the context of innate behaviors.8,20,21,22 Here, we co-analyzed a defined feeding behavior and simultaneous extracellular single-unit recordings from MB output neurons (MBONs) in the cockroach in response to timed sensory stimulation with odors. We show that clear neuronal responses occurred almost exclusively during behaviorally responded trials. Early MBON responses to the sensory stimulus preceded the feeding behavior and predicted its occurrence or non-occurrence from the single-trial population activity. Our results therefore suggest that at its output, the MB does not merely encode sensory stimulus valence. We hypothesize instead that the MB output represents an integrated signal of internal state, momentary environmental conditions, and experience-dependent memory to encode a behavioral decision.

A dynamical model of C. elegans thermal preference reveals independent excitatory and inhibitory learning pathways

Caenorhabditis elegans is capable of learning and remembering behaviorally relevant cues such as smells, tastes, and temperature. This is an example of associative learning, a process in which behavior is modified by making associations between various stimuli. Since the mathematical theory of conditioning does not account for some of its salient aspects, such as spontaneous recovery of extinguished associations, accurate modeling of behavior of real animals during conditioning has turned out difficult. Here, we do this in the context of the dynamics of the thermal preference of C. elegans. We quantify C. elegans thermotaxis in response to various conditioning temperatures, starvation durations, and genetic perturbations using a high-resolution microfluidic droplet assay. We model these data comprehensively, within a biologically interpretable, multi-modal framework. We find that the strength of the thermal preference is composed of two independent, genetically separable contributions and requires a model with at least four dynamical variables. One pathway positively associates the experienced temperature independently of food and the other negatively associates with the temperature when food is absent. The multidimensional structure of the association strength provides an explanation for the apparent classical temperature-food association of C. elegans thermal preference and a number of longstanding questions in animal learning, including spontaneous recovery, asymmetric response to appetitive vs. aversive cues, latent inhibition, and generalization among similar cues.

Learning accurate path integration in ring attractor models of the head direction system

Ring attractor models for angular path integration have received strong experimental support. To function as integrators, head direction circuits require precisely tuned connectivity, but it is currently unknown how such tuning could be achieved. Here, we propose a network model in which a local, biologically plausible learning rule adjusts synaptic efficacies during development, guided by supervisory allothetic cues. Applied to the Drosophila head direction system, the model learns to path-integrate accurately and develops a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading, and where the network remaps to integrate with different gains in rodents. Our model predicts that path integration requires self-supervised learning during a developmental phase, and proposes a general framework to learn to path-integrate with gain-1 even in architectures that lack the physical topography of a ring.

An incentive circuit for memory dynamics in the mushroom body of Drosophila melanogaster

Insects adapt their response to stimuli, such as odours, according to their pairing with positive or negative reinforcements, such as sugar or shock. Recent electrophysiological and imaging findings in Drosophila melanogaster allow detailed examination of the neural mechanisms supporting the acquisition, forgetting, and assimilation of memories. We propose that this data can be explained by the combination of a dopaminergic plasticity rule that supports a variety of synaptic strength change phenomena, and a circuit structure (derived from neuroanatomy) between dopaminergic and output neurons that creates different roles for specific neurons. Computational modelling shows that this circuit allows for rapid memory acquisition, transfer from short term to long term, and exploration/exploitation trade-off. The model can reproduce the observed changes in the activity of each of the identified neurons in conditioning paradigms and can be used for flexible behavioural control.

Insect-Inspired Robots: Bridging Biological and Artificial Systems

This review article aims to address common research questions in hexapod robotics. How can we build intelligent autonomous hexapod robots that can exploit their biomechanics, morphology, and computational systems, to achieve autonomy, adaptability, and energy efficiency comparable to small living creatures, such as insects? Are insects good models for building such intelligent hexapod robots because they are the only animals with six legs? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying relevant and future directions in the field of hexapod robotics over the next decade. After an introduction in section (1), the sections will respectively cover the following three key areas: (2) biomechanics focused on the design of smart legs; (3) locomotion control; and (4) high-level cognition control. These interconnected and interdependent areas are all crucial to improving the level of performance of hexapod robotics in terms of energy efficiency, terrain adaptability, autonomy, and operational range. We will also discuss how the next generation of bioroboticists will be able to transfer knowledge from biology to robotics and vice versa.

Collective action or individual choice: Spontaneity and individuality contribute to decision-making in Drosophila

Our own unique character traits make our behavior consistent and define our individuality. Yet, this consistency does not entail that we behave repetitively like machines. Like humans, animals also combine personality traits with spontaneity to produce adaptive behavior: consistent, but not fully predictable. Here, we study an iconically rigid behavioral trait, insect phototaxis, that nevertheless also contains both components of individuality and spontaneity. In a light/dark T-maze, approximately 70% of a group of Drosophila fruit flies choose the bright arm of the T-Maze, while the remaining 30% walk into the dark. Taking the photopositive and the photonegative subgroups and re-testing them reveals the spontaneous component: a similar 70–30 distribution emerges in each of the two subgroups. Increasing the number of choices to ten choices, reveals the individuality component: flies with an extremely negative series of first choices were more likely to show photonegative behavior in subsequent choices and vice versa. General behavioral traits, independent of light/dark preference, contributed to the development of this individuality. The interaction of individuality and spontaneity together explains why group averages, even for such seemingly stereotypical behaviors, are poor predictors of individual choices.

Learning and memory: Clashing engrams in the fly brain

Prior experience is known to deeply influence new learning. A recent study describes a neural mechanism where initial sensory experience promotes the establishment of a competing and/or cooperative memory trace that shapes behavioral expression of subsequent learning.

A Mechanistic Model for Reward Prediction and Extinction Learning in the Fruit Fly

Extinction learning, the ability to update previously learned information by integrating novel contradictory information, is of high clinical relevance for therapeutic approaches to the modulation of maladaptive memories. Insect models have been instrumental in uncovering fundamental processes of memory formation and memory update. Recent experimental results in Drosophila melanogaster suggest that, after the behavioral extinction of a memory, two parallel but opposing memory traces coexist, residing at different sites within the mushroom body (MB). Here, we propose a minimalistic circuit model of the Drosophila MB that supports classical appetitive and aversive conditioning and memory extinction. The model is tailored to the existing anatomic data and involves two circuit motives of central functional importance. It employs plastic synaptic connections between Kenyon cells (KCs) and MB output neurons (MBONs) in separate and mutually inhibiting appetitive and aversive learning pathways. Recurrent modulation of plasticity through projections from MBONs to reinforcement-mediating dopaminergic neurons (DAN) implements a simple reward prediction mechanism. A distinct set of four MBONs encodes odor valence and predicts behavioral model output. Subjecting our model to learning and extinction protocols reproduced experimental results from recent behavioral and imaging studies. Simulating the experimental blocking of synaptic output of individual neurons or neuron groups in the model circuit confirmed experimental results and allowed formulation of testable predictions. In the temporal domain, our model achieves rapid learning with a step-like increase in the encoded odor value after a single pairing of the conditioned stimulus (CS) with a reward or punishment, facilitating single-trial learning.