The claustrum is a brain region that has been investigated for over 200 years, yet its precise function remains unknown. In the final posthumously released article of Francis Crick, written with Christof Koch, the claustrum was suggested to be critically linked to consciousness. Though the claustrum remained relatively obscure throughout the last half century, it has enjoyed a renewed interest in the last 15 years since Crick and Koch's article. During this time, the claustrum, like many other brain regions, has been studied with the myriad of modern systems neuroscience tools that have been made available by the intersection of genetic and viral technologies. This has uncovered new information about its anatomical connectivity and physiological properties and begun to reveal aspects of its function. From these studies, one clear consensus has emerged which supports Crick and Koch's primary interest in the claustrum: the claustrum has widespread extensive connectivity with the entire cerebral cortex, suggesting a prominent role in 'higher order processes'.

Thermosensation is an indispensable sensory modality. Here, we study temperature coding in Drosophila, and show that temperature is represented by a spatial map of activity in the brain. First, we identify TRP channels that function in the fly antenna to mediate the detection of cold stimuli. Next, we identify the hot-sensing neurons and show that hot and cold antennal receptors project onto distinct, but adjacent glomeruli in the Proximal-Antennal-Protocerebrum (PAP) forming a thermotopic map in the brain. We use two-photon imaging to reveal the functional segregation of hot and cold responses in the PAP, and show that silencing the hot- or cold-sensing neurons produces animals with distinct and discrete deficits in their behavioral responses to thermal stimuli. Together, these results demonstrate that dedicated populations of cells orchestrate behavioral responses to different temperature stimuli, and reveal a labeled-line logic for the coding of temperature information in the brain.

A number of atomic-resolution structures of membrane proteins (better than 3Å resolution) have been determined recently by electron crystallography. While this technique was established more than 40 years ago, it is still in its infancy with regard to the two-dimensional (2D) crystallization, data collection, data analysis, and protein structure determination. In terms of data collection, electron crystallography encompasses both image acquisition and electron diffraction data collection. Other chapters in this volume outline protocols for image collection and analysis. This chapter, however, outlines detailed protocols for data collection by electron diffraction. These include microscope setup, electron diffraction data collection, and troubleshooting.

The formation of large, well-ordered crystals for crystallographic experiments remains a crucial bottleneck to the structural understanding of many important biological systems. To help alleviate this problem in crystallography, we have developed the MicroED method for the collection of electron diffraction data from 3D microcrystals and nanocrystals of radiation-sensitive biological material. In this approach, liquid solutions containing protein microcrystals are deposited on carbon-coated electron microscopy grids and are vitrified by plunging them into liquid ethane. MicroED data are collected for each selected crystal using cryo-electron microscopy, in which the crystal is diffracted using very few electrons as the stage is continuously rotated. This protocol gives advice on how to identify microcrystals by light microscopy or by negative-stain electron microscopy in samples obtained from standard protein crystallization experiments. The protocol also includes information about custom-designed equipment for controlling crystal rotation and software for recording experimental parameters in diffraction image metadata. Identifying microcrystals, preparing samples and setting up the microscope for diffraction data collection take approximately half an hour for each step. Screening microcrystals for quality diffraction takes roughly an hour, and the collection of a single data set is ∼10 min in duration. Complete data sets and resulting high-resolution structures can be obtained from a single crystal or by merging data from multiple crystals.

Associating stimuli with positive or negative reinforcement is essential for survival, but a complete wiring diagram of a higher-order circuit supporting associative memory has not been previously available. Here we reconstruct one such circuit at synaptic resolution, the Drosophila larval mushroom body. We find that most Kenyon cells integrate random combinations of inputs but that a subset receives stereotyped inputs from single projection neurons. This organization maximizes performance of a model output neuron on a stimulus discrimination task. We also report a novel canonical circuit in each mushroom body compartment with previously unidentified connections: reciprocal Kenyon cell to modulatory neuron connections, modulatory neuron to output neuron connections, and a surprisingly high number of recurrent connections between Kenyon cells. Stereotyped connections found between output neurons could enhance the selection of learned behaviours. The complete circuit map of the mushroom body should guide future functional studies of this learning and memory centre.

Neurotransmitter release is mediated by proteins that drive synaptic vesicle fusion with the presynaptic plasma membrane. While soluble N-ethylmaleimide sensitive factor attachment protein receptors (SNAREs) form the core of the fusion apparatus, additional proteins play key roles in the fusion pathway. Here, we report that the C-terminal amphipathic helix of the mammalian accessory protein, complexin (Cpx), exerts profound effects on membranes, including the formation of pores and the efficient budding and fission of vesicles. Using nanodisc-black lipid membrane electrophysiology, we demonstrate that the membrane remodeling activity of Cpx modulates the structure and stability of recombinant exocytic fusion pores. Cpx had particularly strong effects on pores formed by small numbers of SNAREs. Under these conditions, Cpx increased the current through individual pores 3.5-fold, and increased the open time fraction from roughly 0.1 to 1.0. We propose that the membrane sculpting activity of Cpx contributes to the phospholipid rearrangements that underlie fusion by stabilizing highly curved membrane fusion intermediates.

Analysing computations in neural circuits often uses simplified models because the actual neuronal implementation is not known. For example, a problem in vision, how the eye detects image motion, has long been analysed using Hassenstein-Reichardt (HR) detector or Barlow-Levick (BL) models. These both simulate motion detection well, but the exact neuronal circuits undertaking these tasks remain elusive. We reconstructed a comprehensive connectome of the circuits of Drosophila's motion-sensing T4 cells using a novel EM technique. We uncover complex T4 inputs and reveal that putative excitatory inputs cluster at T4's dendrite shafts, while inhibitory inputs localize to the bases. Consistent with our previous study, we reveal that Mi1 and Tm3 cells provide most synaptic contacts onto T4. We are, however, unable to reproduce the spatial offset between these cells reported previously. Our comprehensive connectome reveals complex circuits that include candidate anatomical substrates for both HR and BL types of motion detectors.

In flies, the direction of moving ON and OFF features is computed separately. T4 (ON) and T5 (OFF) are the first neurons in their respective pathways to extract a directionally selective response from their non-selective inputs. Our recent study of T4 found that the integration of offset depolarizing and hyperpolarizing inputs is critical for the generation of directional selectivity. However, T5s lack small-field inhibitory inputs, suggesting they may use a different mechanism. Here we used whole-cell recordings of T5 neurons and found a similar receptive field structure: fast depolarization and persistent, spatially offset hyperpolarization. By assaying pairwise interactions of local stimulation across the receptive field, we found no amplifying responses, only suppressive responses to the non-preferred motion direction. We then evaluated passive, biophysical models and found that a model using direct inhibition, but not the removal of excitation, can accurately predict T5 responses to a range of moving stimuli.

Insects process and learn information flexibly to adapt to their environment. The honeybee Apis mellifera constitutes a traditional model for studying learning and memory at behavioural, cellular and molecular levels. Earlier studies focused on elementary associative and non-associative forms of learning determined by either olfactory conditioning of the proboscis extension reflex or the learning of visual stimuli in an operant context. However, research has indicated that bees are capable of cognitive performances that were thought to occur only in some vertebrate species. For example, honeybees can interpolate visual information, exhibit associative recall, categorize visual information and learn contextual information. Here we show that honeybees can form ’sameness’ and ’difference’ concepts. They learn to solve ’delayed matching-to-sample’ tasks, in which they are required to respond to a matching stimulus, and ’delayed non-matching-to-sample’ tasks, in which they are required to respond to a different stimulus; they can also transfer the learned rules to new stimuli of the same or a different sensory modality. Thus, not only can bees learn specific objects and their physical parameters, but they can also master abstract inter-relationships, such as sameness and difference.

Brains contain networks of interconnected neurons, so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an insect brain (Drosophila larva) with rich behavior, including learning, value-computation, and action-selection, comprising 3,013 neurons and 544,000 synapses. We characterized neuron-types, hubs, feedforward and feedback pathways, and cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain’s most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled powerful machine learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.