Individuals vary in their innate behaviours, even when they have the same genome and have been reared in the same environment. The extent of individuality in plastic behaviours, like learning, is less well characterized. Also unknown is the extent to which intragenotypic differences in learning generalize: if an individual performs well in one assay, will it perform well in other assays? We investigated this using the fruit fly , an organism long-used to study the mechanistic basis of learning and memory. We found that isogenic flies, reared in identical laboratory conditions, and subject to classical conditioning that associated odorants with electric shock, exhibit clear individuality in their learning responses. Flies that performed well when an odour was paired with shock tended to perform well when the odour was paired with bitter taste or when other odours were paired with shock. Thus, individuality in learning performance appears to be prominent in isogenic animals reared identically, and individual differences in learning performance generalize across some aversive sensory modalities. Establishing these results in flies opens up the possibility of studying the genetic and neural circuit basis of individual differences in learning in a highly suitable model organism.

Innate behavioral biases and preferences can vary significantly among individuals of the same genotype. Though individuality is a fundamental property of behavior, it is not currently understood how individual differences in brain structure and physiology produce idiosyncratic behaviors. Here we present evidence for idiosyncrasy in olfactory behavior and neural responses in We show that individual female from a highly inbred laboratory strain exhibit idiosyncratic odor preferences that persist for days. We used in vivo calcium imaging of neural responses to compare projection neuron (second-order neurons that convey odor information from the sensory periphery to the central brain) responses to the same odors across animals. We found that, while odor responses appear grossly stereotyped, upon closer inspection, many individual differences are apparent across antennal lobe (AL) glomeruli (compact microcircuits corresponding to different odor channels). Moreover, we show that neuromodulation, environmental stress in the form of altered nutrition, and activity of certain AL local interneurons affect the magnitude of interfly behavioral variability. Taken together, this work demonstrates that individual exhibit idiosyncratic olfactory preferences and idiosyncratic neural responses to odors, and that behavioral idiosyncrasies are subject to neuromodulation and regulation by neurons in the AL.

The ability to discriminate sensory stimuli with overlapping features is thought to arise in brain structures called expansion layers, where neurons carrying information about sensory features make combinatorial connections onto a much larger set of cells. For 50 years, expansion coding has been a prime topic of theoretical neuroscience, which seeks to explain how quantitative parameters of the expansion circuit influence sensory sensitivity, discrimination, and generalization. Here, we investigate the developmental events that produce the quantitative parameters of the arthropod expansion layer, called the mushroom body. Using Drosophila melanogaster as a model, we employ genetic and chemical tools to engineer changes to circuit development. These allow us to produce living animals with hypothesis-driven variations on natural expansion layer wiring parameters. We then test the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input density, but not cell number, tunes neuronal odor selectivity. Simple odor discrimination behavior is maintained when the Kenyon cell number is reduced and augmented by Kenyon cell number expansion. Animals with increased input density to each Kenyon cell show increased overlap in Kenyon cell odor responses and become worse at odor discrimination tasks.

The ability to discriminate sensory stimuli with overlapping features is thought to arise in brain structures called expansion layers, where neurons carrying information about sensory features make combinatorial connections onto a much larger set of cells. For 50 years, expansion coding has been a prime topic of theoretical neuroscience, which seeks to explain how quantitative parameters of the expansion circuit influence sensory sensitivity, discrimination, and generalization. Here, we investigate the developmental events that produce the quantitative parameters of the arthropod expansion layer, called the mushroom body. Using Drosophila melanogaster as a model, we employ genetic and chemical tools to engineer changes to circuit development. These allow us to produce living animals with hypothesis-driven variations on natural expansion layer wiring parameters. We then test the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input density, but not cell number, tunes neuronal odor selectivity. Simple odor discrimination behavior is maintained when the Kenyon cell number is reduced and augmented by Kenyon cell number expansion. Animals with increased input density to each Kenyon cell show increased overlap in Kenyon cell odor responses and become worse at odor discrimination tasks.

Understanding learning through synaptic plasticity rules in the brain is a grand challenge for neuroscience. Here we introduce a novel computational framework for inferring plasticity rules from experimental data on neural activity trajectories and behavioral learning dynamics. Our methodology parameterizes the plasticity function to provide theoretical interpretability and facilitate gradient-based optimization. For instance, we use Taylor series expansions or multilayer perceptrons to approximate plasticity rules, and we adjust their parameters via gradient descent over entire trajectories to closely match observed neural activity and behavioral data. Notably, our approach can learn intricate rules that induce long nonlinear time-dependencies, such as those incorporating postsynaptic activity and current synaptic weights. We validate our method through simulations, accurately recovering established rules, like Oja’s, as well as more complex hypothetical rules incorporating reward-modulated terms. We assess the resilience of our technique to noise and, as a tangible application, apply it to behavioral data from Drosophila during a probabilistic reward-learning experiment. Remarkably, we identify an active forgetting component of reward learning in flies that enhances the predictive accuracy of previous models. Overall, our modeling framework provides an exciting new avenue to elucidate the computational principles governing synaptic plasticity and learning in the brain.

Memory guides the choices an animal makes across widely varying conditions in dynamic environments. Consequently, the most adaptive choice depends on the options available. How can a single memory support optimal behavior across different sets of choice options? We address this using olfactory learning in Drosophila. Even when we restrict an odor-punishment association to a single set of synapses using optogenetics, we find that flies still show choice behavior that depends on the options it encounters. Here we show that how the odor choices are presented to the animal influences memory recall itself. Presenting two similar odors in sequence enabled flies to not only discriminate them behaviorally but also at the level of neural activity. However, when the same odors were encountered as solitary stimuli, no such differences were detectable. These results show that memory recall is not simply a comparison to a static learned template, but can be adaptively modulated by stimulus dynamics.

Although all sensory circuits ascend to higher brain areas where stimuli are represented in sparse, stimulus-specific activity patterns, relatively little is known about sensory coding on the descending side of neural circuits, as a network converges. In insects, mushroom bodies have been an important model system for studying sparse coding in the olfactory system, where this format is important for accurate memory formation. In Drosophila, it has recently been shown that the 2,000 Kenyon cells of the mushroom body converge onto a population of only 34 mushroom body output neurons (MBONs), which fall into 21 anatomically distinct cell types. Here we provide the first, to our knowledge, comprehensive view of olfactory representations at the fourth layer of the circuit, where we find a clear transition in the principles of sensory coding. We show that MBON tuning curves are highly correlated with one another. This is in sharp contrast to the process of progressive decorrelation of tuning in the earlier layers of the circuit. Instead, at the population level, odour representations are reformatted so that positive and negative correlations arise between representations of different odours. At the single-cell level, we show that uniquely identifiable MBONs display profoundly different tuning across different animals, but that tuning of the same neuron across the two hemispheres of an individual fly was nearly identical. Thus, individualized coordination of tuning arises at this level of the olfactory circuit. Furthermore, we find that this individualization is an active process that requires a learning-related gene, rutabaga. Ultimately, neural circuits have to flexibly map highly stimulus-specific information in sparse layers onto a limited number of different motor outputs. The reformatting of sensory representations we observe here may mark the beginning of this sensory-motor transition in the olfactory system.

Foraging animals must use decision-making strategies that dynamically adapt to the changing availability of rewards in the environment. A wide diversity of animals do this by distributing their choices in proportion to the rewards received from each option, Herrnstein’s operant matching law. Theoretical work suggests an elegant mechanistic explanation for this ubiquitous behavior, as operant matching follows automatically from simple synaptic plasticity rules acting within behaviorally relevant neural circuits. However, no past work has mapped operant matching onto plasticity mechanisms in the brain, leaving the biological relevance of the theory unclear. Here we discovered operant matching in Drosophila and showed that it requires synaptic plasticity that acts in the mushroom body and incorporates the expectation of reward. We began by developing a novel behavioral paradigm to measure choices from individual flies as they learn to associate odor cues with probabilistic rewards. We then built a model of the fly mushroom body to explain each fly’s sequential choice behavior using a family of biologically-realistic synaptic plasticity rules. As predicted by past theoretical work, we found that synaptic plasticity rules could explain fly matching behavior by incorporating stimulus expectations, reward expectations, or both. However, by optogenetically bypassing the representation of reward expectation, we abolished matching behavior and showed that the plasticity rule must specifically incorporate reward expectations. Altogether, these results reveal the first synaptic level mechanisms of operant matching and provide compelling evidence for the role of reward expectation signals in the fly brain.

The ability to optically image cellular transmembrane voltages at millisecond-timescale resolutions can offer unprecedented insight into the function of living brains in behaving animals. Here, we present a point mutation that increases the sensitivity of Ace2 opsin-based voltage indicators. We use the mutation to develop Voltron2, an improved chemigeneic voltage indicator that has a 65% higher sensitivity to single APs and 3-fold higher sensitivity to subthreshold potentials than Voltron. Voltron2 retained the sub-millisecond kinetics and photostability of its predecessor, although with lower baseline fluorescence. In multiple in vitro and in vivo comparisons with its predecessor across multiple species, we found Voltron2 to be more sensitive to APs and subthreshold fluctuations. Finally, we used Voltron2 to study and evaluate the possible mechanisms of interneuron synchronization in the mouse hippocampus. Overall, we have discovered a generalizable mutation that significantly increases the sensitivity of Ace2 rhodopsin-based sensors, improving their voltage reporting capability.

Sparse coding is thought to improve discrimination of sensory stimuli by reducing overlap between their representations. Two factors, however, can offset sparse coding's advantages. Similar sensory stimuli have significant overlap, and responses vary across trials. To elucidate the effect of these two factors, we analyzed odor responses in the fly and mouse olfactory regions implicated in learning and discrimination --- the Mushroom Body (MB) and the Piriform Cortex (PCx). In both species, we show that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic. Computationally, we show that the range of observed variability arises from probabilistic synapses in inhibitory feedback connections within central circuits rather than sensory noise, as is traditionally assumed. We propose this coding scheme to be advantageous for coarse- and fine-odor discrimination. More reliable cells enable quick discrimination between dissimilar odors. For similar odors, however, these cells overlap, and do not provide distinguishing information. By contrast, more unreliable cells are decorrelated for similar odors, providing distinguishing information, though this requires extended training with more trials. Overall, we have uncovered a stochastic coding scheme that is conserved in vertebrates and invertebrates, and we identify a candidate mechanism, based on variability in a winner-take-all inhibitory circuit, that improves discrimination with training.