The anterior paired lateral neuron normalizes odour-evoked activity in the Drosophila mushroom body calyx

To identify and memorize discrete but similar environmental inputs, the brain needs to distinguish between subtle differences of activity patterns in defined neuronal populations. The Kenyon cells of the Drosophila adult mushroom body (MB) respond sparsely to complex olfactory input, a property that is thought to support stimuli discrimination in the MB. To understand how this property emerges, we investigated the role of the inhibitory anterior paired lateral neuron (APL) in the input circuit of the MB, the calyx. Within the calyx, presynaptic boutons of projection neurons (PNs) form large synaptic microglomeruli (MGs) with dendrites of postsynaptic Kenyon cells (KCs). Combining EM data analysis and in vivo calcium imaging, we show that APL, via inhibitory and reciprocal synapses targeting both PN boutons and KC dendrites, normalizes odour-evoked representations in MGs of the calyx. APL response scales with the PN input strength and is regionalized around PN input distribution. Our data indicate that the formation of a sparse code by the Kenyon cells requires APL-driven normalization of their MG postsynaptic responses. This work provides experimental insights on how inhibition shapes sensory information representation in a higher brain centre, thereby supporting stimuli discrimination and allowing for efficient associative memory formation.

Compensatory variability in network parameters enhances memory performance in the Drosophila mushroom body

Neural circuits use homeostatic compensation to achieve consistent behavior despite variability in underlying intrinsic and network parameters. However, it remains unclear how compensation regulates variability across a population of the same type of neurons within an individual and what computational benefits might result from such compensation. We address these questions in the Drosophila mushroom body, the fly’s olfactory memory center. In a computational model, we show that under sparse coding conditions, memory performance is degraded when the mushroom body’s principal neurons, Kenyon cells (KCs), vary realistically in key parameters governing their excitability. However, memory performance is rescued while maintaining realistic variability if parameters compensate for each other to equalize KC average activity. Such compensation can be achieved through both activity-dependent and activity-independent mechanisms. Finally, we show that correlations predicted by our model’s compensatory mechanisms appear in the Drosophila hemibrain connectome. These findings reveal compensatory variability in the mushroom body and describe its computational benefits for associative memory.

Mushroom body input connections form independently of sensory activity in Drosophila melanogaster

Associative brain centers, such as the insect mushroom body, need to represent sensory information in an efficient manner. In Drosophila melanogaster, the Kenyon cells of the mushroom body integrate inputs from a random set of olfactory projection neurons, but some projection neurons, namely those activated by a few ethologically meaningful odors, connect to Kenyon cells more frequently than others. This biased and random connectivity pattern is conceivably advantageous, as it enables the mushroom body to represent a large number of odors as unique activity patterns while prioritizing the representation of a few specific odors. How this connectivity pattern is established remains largely unknown. Here, we test whether the mechanisms patterning the connections between Kenyon cells and projection neurons depend on sensory activity or whether they are hardwired. We mapped a large number of mushroom body input connections in anosmic flies, flies lacking the obligate odorant co-receptor Orco, and in wildtype flies. Statistical analyses of these datasets reveal that the random and biased connectivity pattern observed between Kenyon cells and projection neurons forms normally in the absence of most olfactory sensory activity. This finding supports the idea that even comparatively subtle, population-level patterns of neuronal connectivity can be encoded by fixed genetic programs and are likely to be the result of evolved prioritization of ecologically and ethologically salient stimuli.

Silencing neuronal activity is required for developmental circuit remodeling

Postnatal refinement of neuronal connectivity shapes the mature nervous system. Pruning of exuberant connections involves both cell autonomous and non-cell autonomous mechanisms, such as neuronal activity. While the role of neuronal activity in the plasticity of excitatory synapses has been extensively studied, the involvement of inhibition is less clear. Furthermore, the role of activity during stereotypic developmental remodeling, where competition is not as apparent, is not well understood. Here we use the Drosophila mushroom body as a model to show that regulated silencing of neuronal activity is required for developmental axon pruning of the γ-Kenyon cells. We demonstrate that silencing neuronal activity is mechanistically achieved by cell autonomous expression of the inward rectifying potassium channel (irk1) combined with inhibition by the GABAergic APL neuron. These results support the Hebbian-like rule 'use it or lose it', where inhibition can destabilize connectivity and promote pruning while excitability stabilizes existing connections.

The anterior paired lateral neuron normalizes odour-evoked activity at the mushroom body calyx

To identify and memorize discrete but similar environmental inputs, the brain needs to distinguish between subtle differences of activity patterns in defined neuronal populations. The Kenyon cells of the Drosophila adult mushroom body (MB) respond sparsely to complex olfactory input, a property that is thought to support stimuli discrimination in the MB. To understand how this property emerges, we investigated the role of the inhibitory anterior paired lateral neuron (APL) in the input circuit of the MB, the calyx. Within the calyx, presynaptic boutons of projection neurons (PNs) form large synaptic microglomeruli (MGs) with dendrites of postsynaptic Kenyon cells (KCs). Combining EM data analysis and in vivo calcium imaging, we show that APL, via inhibitory and reciprocal synapses targeting both PN boutons and KC dendrites, normalizes odour-evoked representations in MGs of the calyx. APL response scales with the PN input strength and is regionalized around PN input distribution. Our data indicate that the formation of a sparse code by the Kenyon cells requires APL-driven normalization of their MG postsynaptic responses. This work provides experimental insights on how inhibition shapes sensory information representation in a higher brain centre, thereby supporting stimuli discrimination and allowing for efficient associative memory formation.

Differential conditioning produces merged long-term memory in Drosophila

Multiple spaced trials of aversive differential conditioning can produce two independent long-term memories (LTMs) of opposite valence. One is an aversive memory for avoiding the conditioned stimulus (CS+), and the other is a safety memory for approaching the non-conditioned stimulus (CS-). Here, we show that a single trial of aversive differential conditioning yields one merged LTM (mLTM) for avoiding both CS+ and CS-. Such mLTM can be detected after sequential exposures to the shock-paired CS+ and unpaired CS-, and be retrieved by either CS+ or CS-. The formation of mLTM relies on triggering aversive-reinforcing dopaminergic neurons and subsequent new protein synthesis. Expressing mLTM involves αβ Kenyon cells and corresponding approach-directing mushroom body output neurons (MBONs), in which similar-amplitude long-term depression of responses to CS+ and CS- seems to signal the mLTM. Our results suggest that animals can develop distinct strategies for occasional and repeated threatening experiences.

Location and Strength of Malvolio Expression in Chinese Honeybee Reveals Its Potential Role in Labor Division

The malvolio (mvl) gene plays an important role in the transition from nursing to foraging in honeybees (Apis mellifera). Apis cerana cerana (A. c. cerana) is a subspecies of the eastern honeybee, well-known for its pollinator role throughout China. Although A. c. cerana shares many characteristics with A. mellifera, it is unclear whether Acmvl plays a similar role to Ammvl in foraging behavior. In this study, Acmvl expression was quantified during the transition from nursing to foraging in A. c. cerana. Acmvl protein production was also characterized in different tissues in bees from three behavioral groups. Finally, in situ hybridization was used to describe Acmvl expression patterns in forager bee brains. Acmvl expression was low early in life but then showed a major peak, which suggests its role in labor division. Examination of tissue type revealed that Acmvl expression was highest in the thoraxes of nurse bees and the heads of forager bees. In bee brains, Acmvl was selectively expressed in the somata of Kenyon cells in the mushroom bodies, optic lobes and antennal lobes. Taken together, these findings suggest that Acmvl plays a role in the nurse–forager transition of A. c. cerana.

Predictive olfactory learning in Drosophila

Olfactory learning and conditioning in the fruit fly is typically modelled by correlation-based associative synaptic plasticity. It was shown that the conditioning of an odor-evoked response by a shock depends on the connections from Kenyon cells (KC) to mushroom body output neurons (MBONs). Although on the behavioral level conditioning is recognized to be predictive, it remains unclear how MBONs form predictions of aversive or appetitive values (valences) of odors on the circuit level. We present behavioral experiments that are not well explained by associative plasticity between conditioned and unconditioned stimuli, and we suggest two alternative models for how predictions can be formed. In error-driven predictive plasticity, dopaminergic neurons (DANs) represent the error between the predictive odor value and the shock strength. In target-driven predictive plasticity, the DANs represent the target for the predictive MBON activity. Predictive plasticity in KC-to-MBON synapses can also explain trace-conditioning, the valence-dependent sign switch in plasticity, and the observed novelty-familiarity representation. The model offers a framework to dissect MBON circuits and interpret DAN activity during olfactory learning.

Connectivity patterns shape sensory representation in a cerebellum-like network

SUMMARYCerebellum-like structures are found in many brains and share a basic fan-out–fan-in network architecture. How the specific structural features of these networks give rise to their learning function remains largely unknown. To investigate this structure–function relationship, we developed a realistic computational model of an empirically very well-characterized cerebellum-like structure, the Drosophila melanogaster mushroom body. We show how well-defined connectivity patterns between the Kenyon cells, the constituent neurons of the mushroom body, and their input projection neurons enable different functions. First, biases in the likelihoods at which individual projection neurons connect to Kenyon cells allow the mushroom body to prioritize the learning of particular, ethologically meaningful odors. Second, groups of projection neurons connecting preferentially to the same Kenyon cells facilitate the mushroom body generalizing across similar odors. Altogether, our results demonstrate how different connectivity patterns shape the representation space of a cerebellum-like network and impact its learning outcomes.

Compensatory variability in network parameters enhances memory performance in the Drosophila mushroom body

Neural circuits use homeostatic compensation to achieve consistent behaviour despite variability in underlying intrinsic and network parameters. However, it remains unclear how compensation regulates variability across a population of the same type of neurons within an individual, and what computational benefits might result from such compensation. We address these questions in the Drosophila mushroom body, the fly’s olfactory memory center. In a computational model, we show that memory performance is degraded when the mushroom body’s principal neurons, Kenyon cells (KCs), vary realistically in key parameters governing their excitability, because the resulting inter-KC variability in average activity levels makes odor representations less separable. However, memory performance is rescued while maintaining realistic variability if parameters compensate for each other to equalize KC average activity. Such compensation can be achieved through both activity-dependent and activity-independent mechanisms. Finally, we show that correlations predicted by our model’s compensatory mechanisms appear in the Drosophila hemibrain connectome. These findings reveal compensatory variability in the mushroom body and describe its computational benefits for associative memory.Significance statementHow does variability between neurons affect neural circuit function? How might neurons behave similarly despite having different underlying features? We addressed these questions in neurons called Kenyon cells, which store olfactory memories in flies. Kenyon cells differ among themselves in key features that affect how active they are, and in a model of the fly’s memory circuit, adding this inter-neuronal variability made the model fly worse at learning the values of multiple odors. However, memory performance was rescued if compensation between the variable underlying features allowed Kenyon cells to be equally active on average, and we found the hypothesized compensatory variability in real Kenyon cells’ anatomy. This work reveals the existence and computational benefits of compensatory variability in neural networks.