Memory formation is achieved by genetically tightly controlled molecular pathways that result in a change of synaptic strength and synapse organization. While for short-term memory traces, rapidly acting biochemical pathways are in place, the formation of long-lasting memories requires changes in the transcriptional program of a cell. Although many genes involved in learning and memory formation have been identified, little is known about the genetic mechanisms required for changing the transcriptional program during different phases of long-term memory (LTM) formation. With Drosophila melanogaster as a model system, we profiled transcriptomic changes in the mushroom body—a memory center in the fly brain—at distinct time intervals during appetitive olfactory LTM formation using the targeted DamID technique. We describe the gene expression profiles during these phases and tested 33 selected candidate genes for deficits in LTM formation using RNAi knockdown. We identified 10 genes that enhance or decrease memory when knocked-down in the mushroom body. For vajk-1 and hacd1—the two strongest hits—we gained further support for their crucial role in appetitive learning and forgetting. These findings show that profiling gene expression changes in specific cell-types harboring memory traces provides a powerful entry point to identify new genes involved in learning and memory. The presented transcriptomic data may further be used as resource to study genes acting at different memory phases.

Lasting changes in gene expression are critical for the formation of long-term memories (LTMs), depending on the conserved CrebB transcriptional activator. While requirement of distinct neurons in defined circuits for different learning and memory phases have been studied in detail, only little is known regarding the gene regulatory changes that occur within these neurons. We here use the fruit fly as powerful model system to study the neural circuits of CrebB-dependent appetitive olfactory LTM. We edited the CrebB locus to create a GFP-tagged CrebB conditional knockout allele, allowing us to generate mutant, post-mitotic neurons with high spatial and temporal precision. Investigating CrebB-dependence within the mushroom body (MB) circuit we show that MB α/β and α’/β’ neurons as well as MBON α3, but not in dopaminergic neurons require CrebB for LTM. Thus, transcriptional memory traces occur in different neurons within the same neural circuit.

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.

Table of contentsA1 Functional advantages of cell-type heterogeneity in neural circuitsTatyana O. SharpeeA2 Mesoscopic modeling of propagating waves in visual cortexAlain DestexheA3 Dynamics and biomarkers of mental disordersMitsuo KawatoF1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneuronsVladislav Sekulić, Frances K. SkinnerF2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brainsDaniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán SomogyváriF3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks.Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir JosićO1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generatorsIrene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo VaronaO2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrainEunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun ChoiO3 Modeling auditory stream segregation, build-up and bistabilityJames Rankin, Pamela Osborn Popp, John RinzelO4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fieldsAlejandro Tabas, André Rupp, Emili Balaguer-BallesterO5 A simple model of retinal response to multi-electrode stimulationMatias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish MeffinO6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination taskVeronika Koren, Timm Lochmann, Valentin Dragoi, Klaus ObermayerO7 Input-location dependent gain modulation in cerebellar nucleus neuronsMaria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen, Volker SteuberO8 Analytic solution of cable energy function for cortical axons and dendritesHuiwen Ju, Jiao Yu, Michael L. Hines, Liang Chen, Yuguo YuO9 C. elegans interactome: interactive visualization of Caenorhabditis elegans worm neuronal networkJimin Kim, Will Leahy, Eli ShlizermanO10 Is the model any good? Objective criteria for computational neuroscience model selectionJustas Birgiolas, Richard C. Gerkin, Sharon M. CrookO11 Cooperation and competition of gamma oscillation mechanismsAtthaphon Viriyopase, Raoul-Martin Memmesheimer, Stan GielenO12 A discrete structure of the brain wavesYuri Dabaghian, Justin DeVito, Luca PerottiO13 Direction-specific silencing of the Drosophila gaze stabilization systemAnmo J. Kim, Lisa M. Fenk, Cheng Lyu, Gaby MaimonO14 What does the fruit fly think about values? A model of olfactory associative learningChang Zhao, Yves Widmer, Simon Sprecher,Walter SennO15 Effects of ionic diffusion on power spectra of local field potentials (LFP)Geir Halnes, Tuomo Mäki-Marttunen, Daniel Keller, Klas H. Pettersen,Ole A. Andreassen...

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 KCto-MBON synapses can also explain trace-conditioning, the valence-dependent sign switch in plasticity, and the observed novelty-familiarity representation. The model offer a framework to dissect MBON circuits and interpret DAN activity during olfactory learning.