Animals exhibit a behavioral response to novel sensory stimuli about which they have no prior knowledge. We have examined the neural and behavioral correlates of novelty and familiarity in the olfactory system of Drosophila. Novel odors elicit strong activity in output neurons (MBONs) of the α'3 compartment of the mushroom body that is rapidly suppressed upon repeated exposure to the same odor. This transition in neural activity upon familiarization requires odor-evoked activity in the dopaminergic neuron innervating this compartment. Moreover, exposure of a fly to novel odors evokes an alerting response that can also be elicited by optogenetic activation of α'3 MBONs. Silencing these MBONs eliminates the alerting behavior. These data suggest that the α'3 compartment plays a causal role in the behavioral response to novel and familiar stimuli as a consequence of dopamine-mediated plasticity at the Kenyon cell-MBONα'3 synapse.

We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussian mixture modeling (GMM) in the adjacency spectral embedding (ASE) representation space, we introduce the latent structure model (LSM) for network modeling and inference. LSM is a generalization of the stochastic block model (SBM) and a special case of the random dot product graph (RDPG) latent position model, and is amenable to semiparametric GMM in the ASE representation space. The resulting connectome code derived via semiparametric GMM composed with ASE captures latent connectome structure and elucidates biologically relevant neuronal properties.

We describe new detachable floating glass micropipette electrode devices that provide targeted action potential recordings in active moving organs without requiring constant mechanical constraint or pharmacological inhibition of tissue motion. The technology is based on the concept of a glass micropipette electrode that is held firmly during cell targeting and intracellular insertion, after which a 100µg glass microelectrode, a "microdevice", is gently released to remain within the moving organ. The microdevices provide long-term recordings of action potentials, even during millimeter-scale movement of tissue in which the device is embedded. We demonstrate two different glass micropipette electrode holding and detachment designs appropriate for the heart (sharp glass microdevices for cardiac myocytes in rats, guinea pigs and humans) and the brain (patch glass microdevices for neurons in rats). We explain how microdevices enable measurements of multiple cells within a moving organ that are typically difficult with other technologies. Using sharp microdevices, action potential duration (APD) was monitored continuously for 15 minutes in unconstrained perfused hearts during global ischemia-reperfusion, providing beat-to-beat measurements of changes in APD. Action potentials from neurons in the hippocampus of anaesthetized rats were measured with patch microdevices, which provided stable base potentials during long-term recordings. Our results demonstrate that detachable microdevices are an elegant and robust tool to record electrical activity with high temporal resolution and cellular level localization without disturbing the physiological working conditions of the organ.

Nervous systems have evolved to translate external stimuli into appropriate behavioral responses. In an ever-changing environment, flexible adjustment of behavioral choice by experience-dependent learning is essential for the animal's survival. Associative learning is a simple form of learning that is widely observed from worms to humans. To understand the whole process of learning, we need to know how sensory information is represented and transformed in the brain, how it is changed by experience, and how the changes are reflected on motor output. To tackle these questions, studying numerically simple invertebrate nervous systems has a great advantage. In this review, I will feature the Pavlovian olfactory learning in the fruit fly, Drosophila melanogaster. The mushroom body is a key brain area for the olfactory learning in this organism. Recently, comprehensive anatomical information and the genetic tool sets were made available for the mushroom body circuit. This greatly accelerated the physiological understanding of the learning process. One of the key findings was dopamine-induced long-term synaptic plasticity that can alter the representations of stimulus valence. I will mostly focus on the new studies within these few years and discuss what we can possibly learn about the vertebrate systems from this model organism.

Ring attractors are a class of recurrent networks hypothesized to underlie the representation of heading direction. Such network structures, schematized as a ring of neurons whose connectivity depends on their heading preferences, can sustain a bump-like activity pattern whose location can be updated by continuous shifts along either turn direction. We recently reported that a population of fly neurons represents the animal's heading via bump-like activity dynamics. We combined two-photon calcium imaging in head-fixed flying flies with optogenetics to overwrite the existing population representation with an artificial one, which was then maintained by the circuit with naturalistic dynamics. A network with local excitation and global inhibition enforces this unique and persistent heading representation. Ring attractor networks have long been invoked in theoretical work; our study provides physiological evidence of their existence and functional architecture.

Persistent neural activity maintains information that connects past and future events. Models of persistent activity often invoke reverberations within local cortical circuits, but long-range circuits could also contribute. Neurons in the mouse anterior lateral motor cortex (ALM) have been shown to have selective persistent activity that instructs future actions. The ALM is connected bidirectionally with parts of the thalamus, including the ventral medial and ventral anterior-lateral nuclei. We recorded spikes from the ALM and thalamus during tactile discrimination with a delayed directional response. Here we show that, similar to ALM neurons, thalamic neurons exhibited selective persistent delay activity that predicted movement direction. Unilateral photoinhibition of delay activity in the ALM or thalamus produced contralesional neglect. Photoinhibition of the thalamus caused a short-latency and near-complete collapse of ALM activity. Similarly, photoinhibition of the ALM diminished thalamic activity. Our results show that the thalamus is a circuit hub in motor preparation and suggest that persistent activity requires reciprocal excitation across multiple brain areas.

We present an approach to study macromolecular assemblies by detecting component proteins' characteristic high-resolution projection patterns, calculated from their known 3D structures, in single electron cryo-micrographs. Our method detects single apoferritin molecules in vitreous ice with high specificity and determines their orientation and location precisely. Simulations show that high spatial-frequency information and-in the presence of protein background-a whitening filter are essential for optimal detection, in particular for images taken far from focus. Experimentally, we could detect small viral RNA polymerase molecules, distributed randomly among binding locations, inside rotavirus particles. Based on the currently attainable image quality, we estimate a threshold for detection that is 150 kDa in ice and 300 kDa in 100 nm thick samples of dense biological material.

Animal development is orchestrated by spatio-temporal gene expression programmes that drive precise lineage commitment, proliferation and migration events at the single-cell level, collectively leading to large-scale morphological change and functional specification in the whole organism. Efforts over decades have uncovered two 'seemingly contradictory' mechanisms in gene regulation governing these intricate processes: (i) stochasticity at individual gene regulatory steps in single cells and (ii) highly coordinated gene expression dynamics in the embryo. Here we discuss how these two layers of regulation arise from the molecular and the systems level, and how they might interplay to determine cell fate and to control the complex body plan. We also review recent technological advancements that enable quantitative analysis of gene regulation dynamics at single-cell, single-molecule resolution. These approaches outline next-generation experiments to decipher general principles bridging gaps between molecular dynamics in single cells and robust gene regulations in the embryo.

Insect nervous systems are proven and powerful model systems for neuroscience research with wide relevance in biology and medicine. However, descriptions of insect brains have suffered from a lack of a complete and uniform nomenclature. Recognising this problem the Insect Brain Name Working Group produced the first agreed hierarchical nomenclature system for the adult insect brain, using Drosophila melanogaster as the reference framework, with other insect taxa considered to ensure greater consistency and expandability (Ito et al., 2014). Ito et al. (2014) purposely focused on the gnathal regions that account for approximately 50% of the adult CNS. We extend this nomenclature system to the sub-gnathal regions of the adult Drosophila nervous system to provide a nomenclature of the so-called ventral nervous system (VNS), which includes the thoracic and abdominal neuromeres that was not included in the original work and contains the neurons that play critical roles underpinning most fly behaviours.