Summary Although associative learning has been localized to specific brain areas in many animals, identifying the underlying synaptic processes in vivo has been difficult. Here, we provide the first demonstration of long-term synaptic plasticity at the output site of the Drosophila mushroom body. Pairing an odor with activation of specific dopamine neurons induces both learning and odor-specific synaptic depression. The plasticity induction strictly depends on the temporal order of the two stimuli, replicating the logical requirement for associative learning. Furthermore, we reveal that dopamine action is confined to and distinct across different anatomical compartments of the mushroom body lobes. Finally, we find that overlap between sparse representations of different odors defines both stimulus-specificity of the plasticity and generalizability of associative memories across odors. Thus, the plasticity we find here not only manifests important features of associative learning but also provides general insights into how a sparse sensory code is read out.

The Drosophila brain contains a relatively simple circuit for forming Pavlovian associations, yet it achieves many operations common across memory systems. Recent advances have established a clear framework for Drosophila learning and revealed the following key operations: a) pattern separation, whereby dense combinatorial representations of odors are preprocessed to generate highly specific, nonoverlapping odor patterns used for learning; b) convergence, in which sensory information is funneled to a small set of output neurons that guide behavioral actions; c) plasticity, where changing the mapping of sensory input to behavioral output requires a strong reinforcement signal, which is also modulated by internal state and environmental context; and d) modularization, in which a memory consists of multiple parallel traces, which are distinct in stability and flexibility and exist in anatomically well-defined modules within the network. Cross-module interactions allow for higher-order effects where past experience influences future learning. Many of these operations have parallels with processes of memory formation and action selection in more complex brains.

Animals can learn causal relationships between pairs of stimuli separated in time and this ability depends on the hippocampus. Such learning is believed to emerge from alterations in network connectivity, but large-scale connectivity is difficult to measure directly, especially during learning. Here, we show that area CA1 cells converge to time-locked firing sequences that bridge the two stimuli paired during training, and this phenomenon is coupled to a reorganization of network correlations. Using two-photon calcium imaging of mouse hippocampal neurons we find that co-time-tuned neurons exhibit enhanced spontaneous activity correlations that increase just prior to learning. While time-tuned cells are not spatially organized, spontaneously correlated cells do fall into distinct spatial clusters that change as a result of learning. We propose that the spatial re-organization of correlation clusters reflects global network connectivity changes that are responsible for the emergence of the sequentially-timed activity of cell-groups underlying the learned behavior.DOI: http://dx.doi.org/10.7554/eLife.01982.001

No abstract

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.

Silicon photomultipliers (SiPMs) are a class of inexpensive and robust single-pixel detectors with applications similar to photomultiplier tubes (PMTs). We performed side-by-side comparisons of recently-developed SiPMs and a GaAsP PMT for two-photon fluorescence imaging of neural activity. Despite higher dark counts, which limit their performance at low photon rates (<1/μs), SiPMs matched the signal-to-noise ratio of the GaAsP PMT at photon rates encountered in typical calcium imaging experiments due to their much lower pulse height variability. At higher photon rates and dynamic ranges encountered during high-speed two-photon voltage imaging, SiPMs significantly outperformed the GaAsP PMT.Photomultiplier tubes (PMTs) detect light using a photocathode, which emits electrons upon photon absorption 2 . Emitted electrons are accelerated under high voltage in vacuum to strike a series of dynodes coated in a secondary emission material 3 . Collisions with the dynodes release additional electrons, amplifying the photon signal into a detectable current pulse. The highestsensitivity PMTs available for visible imaging use Gallium-Arsenide-Phosphide (GaAsP) photocathodes with high quantum efficiencies (QE; >40%), although these photocathodes degrade with light exposure. Alkali photocathodes are also used, which do not degrade substantially but are less sensitive. PMT output pulses have highly variable amplitudes 4 because they rely on a series of low-gain stochastic amplification steps. PMTs are the most commonly used single-pixel light detectors in microscopy.Avalanche photodiodes (APDs) are light-sensitive semiconductor diodes to which a bias voltage is applied. Photon absorption generates electron-hole pairs, which are accelerated by the bias voltage, producing additional electron-hole pairs via impact ionization. At lower bias voltages, APDs operate in 'linear mode', in which each photon produces a pulse of current. At higher bias voltages, APDs operate in Geiger mode, in which runaway ionization produces a saturating signal, causing the APD to act as a photon-gated binary switch. In Geiger mode, additional simultaneously-arriving photons do not produce additional signal. The Geiger mode avalanche is quenched by active or passive circuits 5 , which cause the bias voltage to drop below breakdown then restore it, allowing another photon to be detected. Geiger mode APDs (also called SPADs) have excellent quantum efficiency (>80%) and lower pulse height variability than PMTs, but have small sensitive areas, making single SPADs inefficient for collecting the large etendue of emission light from scattering specimens.

Memory guides behavior across widely varying environments and must therefore be both sufficiently specific and general. A memory too specific will be useless in even a slightly different environment, while an overly general memory may lead to suboptimal choices. Animals successfully learn to both distinguish between very similar stimuli and generalize across cues. Rather than forming memories that strike a balance between specificity and generality, Drosophila can flexibly categorize a given stimulus into different groups depending on the options available. We asked how this flexibility manifests itself in the well-characterized learning and memory pathways of the fruit fly. We show that flexible categorization in neuronal activity as well as behavior depends on the order and identity of the perceived stimuli. Our results identify the neural correlates of flexible stimulus-categorization in the fruit fly.

No abstract