Second-order conditioning in Drosophila

Diverse memory paradigms in Drosophila reveal diverse neural mechanisms

In this review, we aggregated the different types of learning and memory paradigms developed in adult Drosophila and attempted to assess the similarities and differences in the neural mechanisms supporting diverse types of memory. The simplest association memory assays are conditioning paradigms (olfactory, visual, and gustatory). A great deal of work has been done on these memories, revealing hundreds of genes and neural circuits supporting this memory. Variations of conditioning assays (reversal learning, trace conditioning, latent inhibition, and extinction) also reveal interesting memory mechanisms, whereas mechanisms supporting spatial memory (thermal maze, orientation memory, and heat box) and the conditioned suppression of innate behaviors (phototaxis, negative geotaxis, anemotaxis, and locomotion) remain largely unexplored. In recent years, there has been an increased interest in multisensory and multicomponent memories (context-dependent and cross-modal memory) and higher-order memory (sensory preconditioning and second-order conditioning). Some of this work has revealed how the intricate mushroom body (MB) neural circuitry can support more complex memories. Finally, the most complex memories are arguably those involving social memory: courtship conditioning and social learning (mate-copying and egg-laying behaviors). Currently, very little is known about the mechanisms supporting social memories. Overall, the MBs are important for association memories of multiple sensory modalities and multisensory integration, whereas the central complex is important for place, orientation, and navigation memories. Interestingly, several different types of memory appear to use similar or variants of the olfactory conditioning neural circuitry, which are repurposed in different ways.

Cognitive limits of larval Drosophila: testing for conditioned inhibition, sensory preconditioning, and second-order conditioning

Drosophila larvae are an established model system for studying the mechanisms of innate and simple forms of learned behavior. They have about 10 times fewer neurons than adult flies, and it was the low total number of their neurons that allowed for an electron microscopic reconstruction of their brain at synaptic resolution. Regarding the mushroom body, a central brain structure for many forms of associative learning in insects, it turned out that more than half of the classes of synaptic connection had previously escaped attention. Understanding the function of these circuit motifs, subsequently confirmed in adult flies, is an important current research topic. In this context, we test larval Drosophila for their cognitive abilities in three tasks that are characteristically more complex than those previously studied. Our data provide evidence for (i) conditioned inhibition, as has previously been reported for adult flies and honeybees. Unlike what is described for adult flies and honeybees, however, our data do not provide evidence for (ii) sensory preconditioning or (iii) second-order conditioning in Drosophila larvae. We discuss the methodological features of our experiments as well as four specific aspects of the organization of the larval brain that may explain why these two forms of learning are observed in adult flies and honeybees, but not in larval Drosophila.

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.

Hierarchical architecture of dopaminergic circuits enables second-order conditioning in Drosophila

Dopaminergic neurons with distinct projection patterns and physiological properties compose memory subsystems in a brain. However, it is poorly understood whether or how they interact during complex learning. Here, we identify a feedforward circuit formed between dopamine subsystems and show that it is essential for second-order conditioning, an ethologically important form of higher-order associative learning. The Drosophila mushroom body comprises a series of dopaminergic compartments, each of which exhibits distinct memory dynamics. We find that a slow and stable memory compartment can serve as an effective 'teacher' by instructing other faster and transient memory compartments via a single key interneuron, which we identify by connectome analysis and neurotransmitter prediction. This excitatory interneuron acquires enhanced response to reward-predicting odor after first-order conditioning and, upon activation, evokes dopamine release in the 'student' compartments. These hierarchical connections between dopamine subsystems explain distinct properties of first- and second-order memory long known by behavioral psychologists.

Understanding Associative Learning Through Higher-Order Conditioning

Associative learning is often considered to require the physical presence of stimuli in the environment in order for them to be linked. This, however, is not a necessary condition for learning. Indeed, associative relationships can form between events that are never directly paired. That is, associative learning can occur by integrating information across different phases of training. Higher-order conditioning provides evidence for such learning through two deceptively similar designs – sensory preconditioning and second-order conditioning. In this review, we detail the procedures and factors that influence learning in these designs, describe the associative relationships that can be acquired, and argue for the importance of this knowledge in studying brain function.

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.

Avoiding a feared stimulus: Modelling costly avoidance of learnt fear in a sensory preconditioning paradigm

Avoidance of learnt fear prevents the onset of a feared stimulus and the threat that follows. In anxiety-related disorders, it turns pathological given its cost and persistence in the absence of realistic threat. The current study examined the acquisition of costly avoidance of learnt fear in healthy individuals (n = 45), via a sensory preconditioning paradigm. Two neutral preconditioning stimuli (PSs) were paired with two neutral conditioned stimuli (CSs). One CS then came to predict an aversive outcome whereas the other CS came to predict safety. In test, participants engaged in stronger avoidance to the PS associated with the fear-related CS than the PS associated with the safety-related CS. Of note, executing behavioral avoidance led to missing out a competing reward, thus rendering avoidance costly. The results also provide preliminary evidence that threat anticipation and a negative change in valence play a role in driving costly avoidance of learnt fear. Future studies should examine how avoidance of learnt fear maintains pathological anxiety.

Second-Order Conditioning in Humans

In contrast to the large body of work demonstrating second-order conditioning (SOC) in non-human animals, the evidence for SOC in humans is scant. In this review, I examine the existing literature and suggest theoretical and procedural explanations for why SOC has been so elusive in humans. In particular, I discuss potential interactions with conditioned inhibition, whether SOC is rational, and propose critical parameters needed to obtain the effect. I conclude that SOC is a real but difficult phenomenon to obtain in humans, and suggest directions for future research.

Learning with reinforcement prediction errors in a model of the Drosophila mushroom body

Effective decision making in a changing environment demands that accurate predictions are learned about decision outcomes. In Drosophila, such learning is orchestrated in part by the mushroom body, where dopamine neurons signal reinforcing stimuli to modulate plasticity presynaptic to mushroom body output neurons. Building on previous mushroom body models, in which dopamine neurons signal absolute reinforcement, we propose instead that dopamine neurons signal reinforcement prediction errors by utilising feedback reinforcement predictions from output neurons. We formulate plasticity rules that minimise prediction errors, verify that output neurons learn accurate reinforcement predictions in simulations, and postulate connectivity that explains more physiological observations than an experimentally constrained model. The constrained and augmented models reproduce a broad range of conditioning and blocking experiments, and we demonstrate that the absence of blocking does not imply the absence of prediction error dependent learning. Our results provide five predictions that can be tested using established experimental methods.

The connectome of the adult Drosophila mushroom body provides insights into function

Making inferences about the computations performed by neuronal circuits from synapse-level connectivity maps is an emerging opportunity in neuroscience. The mushroom body (MB) is well positioned for developing and testing such an approach due to its conserved neuronal architecture, recently completed dense connectome, and extensive prior experimental studies of its roles in learning, memory, and activity regulation. Here, we identify new components of the MB circuit in Drosophila, including extensive visual input and MB output neurons (MBONs) with direct connections to descending neurons. We find unexpected structure in sensory inputs, in the transfer of information about different sensory modalities to MBONs, and in the modulation of that transfer by dopaminergic neurons (DANs). We provide insights into the circuitry used to integrate MB outputs, connectivity between the MB and the central complex and inputs to DANs, including feedback from MBONs. Our results provide a foundation for further theoretical and experimental work.

Unlimited Associative Learning and the origins of consciousness: a primer and some predictions

Over the past two decades, Ginsburg and Jablonka have developed a novel approach to studying the evolutionary origins of consciousness: the Unlimited Associative Learning (UAL) framework. The central idea is that there is a distinctive type of learning that can serve as a transition marker for the evolutionary transition from non-conscious to conscious life. The goal of this paper is to stimulate discussion of the framework by providing a primer on its key claims (Part I) and a clear statement of its main empirical predictions (Part II).

Recurrent architecture for adaptive regulation of learning in the insect brain

Dopaminergic neurons (DANs) drive learning across the animal kingdom, but the upstream circuits that regulate their activity and thereby learning remain poorly understood. We provide a synaptic-resolution connectome of the circuitry upstream of all DANs in a learning center, the mushroom body of Drosophila larva. We discover afferent sensory pathways and a large population of neurons that provide feedback from mushroom body output neurons and link distinct memory systems (aversive and appetitive). We combine this with functional studies of DANs and their presynaptic partners and with comprehensive circuit modeling. We find that DANs compare convergent feedback from aversive and appetitive systems, which enables the computation of integrated predictions that may improve future learning. Computational modeling reveals that the discovered feedback motifs increase model flexibility and performance on learning tasks. Our study provides the most detailed view to date of biological circuit motifs that support associative learning.

Importin-α2 mediates brain development, learning and memory consolidation in Drosophila

Neuronal development and memory consolidation are conserved processes that rely on nuclear-cytoplasmic transport of signaling molecules to regulate gene activity and initiate cascades of downstream cellular events. Surprisingly, few reports address and validate this widely accepted perspective. Here we show that Importin-α2 (Imp-α2), a soluble nuclear transporter that shuttles cargoes between the cytoplasm and nucleus, is vital for brain development, learning and persistent memory in Drosophila melanogaster. Mutations in importin-α2 (imp-α2, known as Pendulin or Pen and homologous with human KPNA2) are alleles of mushroom body miniature B (mbmB), a gene known to regulate aspects of brain development and influence adult behavior in flies. Mushroom bodies (MBs), paired associative centers in the brain, are smaller than normal due to defective proliferation of specific intrinsic Kenyon cell (KC) neurons in mbmB mutants. Extant KCs projecting to the MB β-lobe terminate abnormally on the contralateral side of the brain. mbmB adults have impaired olfactory learning but normal memory decay in most respects, except that protein synthesis-dependent long-term memory (LTM) is abolished. This observation supports an alternative mechanism of persistent memory in which mutually exclusive protein-synthesis-dependent and -independent forms rely on opposing cellular mechanisms or circuits. We propose a testable model of Imp-α2 and nuclear transport roles in brain development and conditioned behavior. Based on our molecular characterization, we suggest that mbmB is hereafter referred to as imp-α2^mbmB.

An optogenetic analogue of second-order reinforcement in Drosophila

In insects, odours are coded by the combinatorial activation of ascending pathways, including their third-order representation in mushroom body Kenyon cells. Kenyon cells also receive intersecting input from ascending and mostly dopaminergic reinforcement pathways. Indeed, in Drosophila, presenting an odour together with activation of the dopaminergic mushroom body input neuron PPL1-01 leads to a weakening of the synapse between Kenyon cells and the approach-promoting mushroom body output neuron MBON-11. As a result of such weakened approach tendencies, flies avoid the shock-predicting odour in a subsequent choice test. Thus, increased activity in PPL1-01 stands for punishment, whereas reduced activity in MBON-11 stands for predicted punishment. Given that punishment-predictors can themselves serve as punishments of second order, we tested whether presenting an odour together with the optogenetic silencing of MBON-11 would lead to learned odour avoidance, and found this to be the case. In turn, the optogenetic activation of MBON-11 together with odour presentation led to learned odour approach. Thus, manipulating activity in MBON-11 can be an analogue of predicted, second-order reinforcement.

Application of a Prediction Error Theory to Pavlovian Conditioning in an Insect

Elucidation of the conditions in which associative learning occurs is a critical issue in neuroscience and comparative psychology. In Pavlovian conditioning in mammals, it is thought that the discrepancy, or error, between the actual reward and the predicted reward determines whether learning occurs. This theory stems from the finding of Kamin’s blocking effect, in which after pairing of a stimulus with an unconditioned stimulus (US), conditioning of a second stimulus is blocked when the two stimuli are presented in compound and paired with the same US. Whether this theory is applicable to any species of invertebrates, however, has remained unknown. We first showed blocking and one-trial blocking of Pavlovian conditioning in the cricket Gryllus bimaculatus, which supported the Rescorla–Wagner model but not attentional theories, the major competitive error-correction learning theories to account for blocking. To match the prediction error theory, a neural circuit model was proposed, and prediction from the model was tested: the results were consistent with the Rescorla–Wagner model but not with the retrieval theory, another competitive theory to account for blocking. The findings suggest that the Rescorla–Wagner model best accounts for Pavlovian conditioning in crickets and that the basic computation rule underlying Pavlovian conditioning in crickets is the same to those suggested in mammals. Moreover, results of pharmacological studies in crickets suggested that octopamine and dopamine mediate prediction error signals in appetitive and aversive conditioning, respectively. This was in contrast to the notion that dopamine mediates appetitive prediction error signals in mammals. The functional significance and evolutionary implications of these findings are discussed.

Trace Conditioning in Drosophila Induces Associative Plasticity in Mushroom Body Kenyon Cells and Dopaminergic Neurons

Dopaminergic neurons (DANs) signal punishment and reward during associative learning. In mammals, DANs show associative plasticity that correlates with the discrepancy between predicted and actual reinforcement (prediction error) during classical conditioning. Also in insects, such as Drosophila, DANs show associative plasticity that is, however, less understood. Here, we study associative plasticity in DANs and their synaptic partners, the Kenyon cells (KCs) in the mushroom bodies (MBs), while training Drosophila to associate an odorant with a temporally separated electric shock (trace conditioning). In most MB compartments DANs strengthened their responses to the conditioned odorant relative to untrained animals. This response plasticity preserved the initial degree of similarity between the odorant- and the shock-induced spatial response patterns, which decreased in untrained animals. Contrary to DANs, KCs (α'/β'-type) decreased their responses to the conditioned odorant relative to untrained animals. We found no evidence for prediction error coding by DANs during conditioning. Rather, our data supports the hypothesis that DAN plasticity encodes conditioning-induced changes in the odorant's predictive power.

Mcm3 replicative helicase mutation impairs neuroblast proliferation and memory in Drosophila

In the developing Drosophila brain, a small number of neural progenitor cells (neuroblasts) generate in a co-ordinated manner a high variety of neuronal cells by integration of temporal, spatial and cell-intrinsic information. In this study, we performed the molecular and phenotypic characterization of a structural brain mutant called small mushroom bodies (smu), which was isolated in a screen for mutants with altered brain structure. Focusing on the mushroom body neuroblast lineages we show that failure of neuroblasts to generate the normal number of mushroom body neurons (Kenyon cells) is the major cause of the smu phenotype. In particular, the premature loss of mushroom body neuroblasts caused a pronounced effect on the number of late-born Kenyon cells. Neuroblasts showed no obvious defects in processes controlling asymmetric cell division, but generated less ganglion mother cells. Cloning of smu uncovered a single amino acid substitution in an evolutionarily conserved protein interaction domain of the Minichromosome maintenance 3 (Mcm3) protein. Mcm3 is part of the multimeric Cdc45/Mcm/GINS (CMG) complex, which functions as a helicase during DNA replication. We propose that at least in the case of mushroom body neuroblasts, timely replication is not only required for continuous proliferation but also for their survival. The absence of Kenyon cells in smu reduced learning and early phases of conditioned olfactory memory. Corresponding to the absence of late-born Kenyon cells projecting to α'/β' and α/β lobes, smu is profoundly defective in later phases of persistent memory.

Learning and cognition in insects

Insects possess small brains but exhibit sophisticated behavioral performances. Recent works have reported the existence of unsuspected cognitive capabilities in various insect species, which go beyond the traditional studied framework of simple associative learning. In this study, I focus on capabilities such as attention, social learning, individual recognition, concept learning, and metacognition, and discuss their presence and mechanistic bases in insects. I analyze whether these behaviors can be explained on the basis of elemental associative learning or, on the contrary, require higher-order explanations. In doing this, I highlight experimental challenges and suggest future directions for investigating the neurobiology of higher-order learning in insects, with the goal of uncovering l architectures underlying cognitive processing.

SSCC TD: a serial and simultaneous configural-cue compound stimuli representation for temporal difference learning

This paper presents a novel representational framework for the Temporal Difference (TD) model of learning, which allows the computation of configural stimuli--cumulative compounds of stimuli that generate perceptual emergents known as configural cues. This Simultaneous and Serial Configural-cue Compound Stimuli Temporal Difference model (SSCC TD) can model both simultaneous and serial stimulus compounds, as well as compounds including the experimental context. This modification significantly broadens the range of phenomena which the TD paradigm can explain, and allows it to predict phenomena which traditional TD solutions cannot, particularly effects that depend on compound stimuli functioning as a whole, such as pattern learning and serial structural discriminations, and context-related effects.

Parallel pathways for cross-modal memory retrieval in Drosophila

Memory-retrieval processing of cross-modal sensory preconditioning is vital for understanding the plasticity underlying the interactions between modalities. As part of the sensory preconditioning paradigm, it has been hypothesized that the conditioned response to an unreinforced cue depends on the memory of the reinforced cue via a sensory link between the two cues. To test this hypothesis, we studied cross-modal memory-retrieval processing in a genetically tractable model organism, Drosophila melanogaster. By expressing the dominant temperature-sensitive shibire(ts1) (shi(ts1)) transgene, which blocks synaptic vesicle recycling of specific neural subsets with the Gal4/UAS system at the restrictive temperature, we specifically blocked visual and olfactory memory retrieval, either alone or in combination; memory acquisition remained intact for these modalities. Blocking the memory retrieval of the reinforced olfactory cues did not impair the conditioned response to the unreinforced visual cues or vice versa, in contrast to the canonical memory-retrieval processing of sensory preconditioning. In addition, these conditioned responses can be abolished by blocking the memory retrieval of the two modalities simultaneously. In sum, our results indicated that a conditioned response to an unreinforced cue in cross-modal sensory preconditioning can be recalled through parallel pathways.

Learning by observation emerges from simple associations in an insect model

Recent debate has questioned whether animal social learning truly deserves the label "social". Solitary animals can sometimes learn from conspecifics, and social learning abilities often correlate with individual learning abilities, so there may be little reason to view the underlying learning processes as adaptively specialized. Here, we demonstrate how learning by observation, an ability common to primates, birds, rodents, and insects, may arise through a simple Pavlovian ability to integrate two learned associations. Bumblebees are known to learn how to recognize rewarding flower colors by watching conspecifics from behind a screen, and we found that previous associations between conspecifics and reward are critical to this process. Bees that have previously been rewarded for joining conspecifics copy color preferences, but bees that lack such experience do not, and those that associate conspecifics with bitter substances actively avoid those flower colors where others have been seen. Our findings place a seemingly complex social learning phenomenon within a simple associative framework that is common to social and solitary species alike.