The physiological basis for contrast opponency in motion computation in Drosophila

In Drosophila, direction-selective neurons implement a mechanism of motion computation similar to cortical neurons, using contrast-opponent receptive fields with ON and OFF subfields. It is not clear how the presynaptic circuitry of direction-selective neurons in the OFF pathway supports this computation if all major inputs are OFF-rectified neurons. Here, we reveal the biological substrate for motion computation in the OFF pathway. Three interneurons, Tm2, Tm9 and CT1, provide information about ON stimuli to the OFF direction-selective neuron T5 across its receptive field, supporting a contrast-opponent receptive field organization. Consistent with its prominent role in motion detection, variability in Tm9 receptive field properties transfers to T5, and calcium decrements in Tm9 in response to ON stimuli persist across behavioral states, while spatial tuning is sharpened by active behavior. Together, our work shows how a key neuronal computation is implemented by its constituent neuronal circuit elements to ensure direction selectivity.

Intrinsic temporal tuning of neurons in the optic tectum is shaped by multisensory experience

For a biological neural network to be functional, its neurons need to be connected with synapses of appropriate strength, and each neuron needs to appropriately respond to its synaptic inputs. This second aspect of network tuning is maintained by intrinsic plasticity; yet it is often considered secondary to changes in connectivity and mostly limited to adjustments of overall excitability of each neuron. Here we argue that even nonoscillatory neurons can be tuned to inputs of different temporal dynamics and that they can routinely adjust this tuning to match the statistics of their synaptic activation. Using the dynamic clamp technique, we show that, in the tectum of Xenopus tadpole, neurons become selective for faster inputs when animals are exposed to fast visual stimuli but remain responsive to longer inputs in animals exposed to slower, looming, or multisensory stimulation. We also report a homeostatic cotuning between synaptic and intrinsic temporal properties of individual tectal cells. These results expand our understanding of intrinsic plasticity in the brain and suggest that there may exist an additional dimension of network tuning that has been so far overlooked.NEW & NOTEWORTHY We use dynamic clamp to show that individual neurons in the tectum of Xenopus tadpoles are selectively tuned to either shorter (more synchronous) or longer (less synchronous) synaptic inputs. We also demonstrate that this intrinsic temporal tuning is strongly shaped by sensory experiences. This new phenomenon, which is likely to be mediated by changes in sodium channel inactivation, is bound to have important consequences for signal processing and the development of local recurrent connections.

Recent Advances in the Genetic Dissection of Neural Circuits in Drosophila

Nervous systems endow animals with cognition and behavior. To understand how nervous systems control behavior, neural circuits mediating distinct functions need to be identified and characterized. With superior genetic manipulability, Drosophila is a model organism at the leading edge of neural circuit analysis. We briefly introduce the state-of-the-art genetic tools that permit precise labeling of neurons and their interconnectivity and investigating what is happening in the brain of a behaving animal and manipulating neurons to determine how behaviors are affected. Brain-wide wiring diagrams, created by light and electron microscopy, bring neural circuit analysis to a new level and scale. Studies enabled by these tools advances our understanding of the nervous system in relation to cognition and behavior.

A guide to analysis and reconstruction of serial block face scanning electron microscopy data

Serial block face scanning electron microscopy (SBF-SEM) is a relatively new technique that allows the acquisition of serially sectioned, imaged and digitally aligned ultrastructural data. There is a wealth of information that can be obtained from the resulting image stacks but this presents a new challenge for researchers - how to computationally analyse and make best use of the large datasets produced. One approach is to reconstruct structures and features of interest in 3D. However, the software programmes can appear overwhelming, time-consuming and not intuitive for those new to image analysis. There are a limited number of published articles that provide sufficient detail on how to do this type of reconstruction. Therefore, the aim of this paper is to provide a detailed step-by-step protocol, accompanied by tutorial videos, for several types of analysis programmes that can be used on raw SBF-SEM data, although there are more options available than can be covered here. To showcase the programmes, datasets of skeletal muscle from foetal and adult guinea pigs are initially used with procedures subsequently applied to guinea pig cardiac tissue and locust brain. The tissue is processed using the heavy metal protocol developed specifically for SBF-SEM. Trimmed resin blocks are placed into a Zeiss Sigma SEM incorporating the Gatan 3View and the resulting image stacks are analysed in three different programmes, Fiji, Amira and MIB, using a range of tools available for segmentation. The results from the image analysis comparison show that the analysis tools are often more suited to a particular type of structure. For example, larger structures, such as nuclei and cells, can be segmented using interpolation, which speeds up analysis; single contrast structures, such as the nucleolus, can be segmented using the contrast-based thresholding tools. Knowing the nature of the tissue and its specific structures (complexity, contrast, if there are distinct membranes, size) will help to determine the best method for reconstruction and thus maximize informative output from valuable tissue.

Connectome studies on Drosophila: a short perspective on a tiny brain

The brain is a network of neurons, one that generates behaviour, and knowing the former is crucial to understanding the latter. Identifying the exact network of synaptic connections, or connectome, of the fly's central nervous system is now a major objective in Drosophila neurobiology, one that has been initiated in several laboratories, especially the Janelia Research Campus of the Howard Hughes Medical Institute. Progress is most advanced in the optic neuropiles of the visual system. The effort to derive a connectome from these and other neuropile regions is proceeding by various methods of electron microscopy, especially focused-ion beam milling scanning electron microscopy, and relies upon - but is to be carefully distinguished from - published light microscopic methods that reveal the projections of genetically labelled cell types. The latter reveal those neurons that come into close proximity and are therefore candidate synaptic partners. Synaptic partnerships are not in fact reliably revealed by such candidate pairs, anatomical connections often revealing unexpected pathways. Synaptic partnerships identified from ultrastructural features provide a strong heuristic basis to interpret not only functional interactions between identified neurons, but also a powerful means to predict such interactions, and suggest functional pathways not readily predicted from existing experimental evidence. The analysis of circuit function may proceed cell by cell, by examining the behavioural outcome of either interrupting or restoring function to any one element in an anatomically defined circuit, but can be foiled by degeneracy in pathway elements. Circuit information can also be used to identify and analyse circuit motifs, and their role in higher-order network properties. These attempts in Drosophila anticipate parallel attempts in other systems, notably the inner plexiform layer of the vertebrate retina, and augment the one complete connectome already available to us, that available for 30 years in the nematode Caenorhabditis elegans.

Generating Executable Models of the Drosophila Central Complex

The central complex (CX) is a set of neuropils in the center of the fly brain that have been implicated as playing an important role in vision-mediated behavior and integration of spatial information with locomotor control. In contrast to currently available data regarding the neural circuitry of neuropils in the fly's vision and olfactory systems, comparable data for the CX neuropils is relatively incomplete; many categories of neurons remain only partly characterized, and the synaptic connectivity between CX neurons has yet to be fully determined. Successful modeling of the information processing functions of the CX neuropils therefore requires a means of easily constructing and testing a range of hypotheses regarding both the high-level structure of their neural circuitry and the properties of their constituent neurons and synapses. To this end, we have created a web application that enables simultaneous graphical querying and construction of executable models of the CX neural circuitry based upon currently available information regarding the geometry and polarity of the arborizations of identified local and projection neurons in the CX. The application's novel functionality is made possible by the Fruit Fly Brain Observatory, a platform for collaborative study and development of fruit fly brain models.

Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy

BACKGROUND

Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern.

NEW METHOD

The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation.

RESULTS

For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result.

COMPARISON WITH EXISTING METHODS

Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity.

CONCLUSION

Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons.

Recent advancement in the challenges to connectomics

Advancement of microscopic technologies established significant progress in our understanding of the brain. In the recent effort to elucidate the complete wiring map of the brain circuitry termed 'connectome', the different modalities of imaging technology, including those of light and electron microscopy, have started providing essential contribution in multiple organisms. The contribution would be impossible without the recent innovation in both acquisition and analyses of the big connectomic data. The current data demonstrated complicated networks with unidirectional and reciprocal connections of the cerebral circuits at the macroscopic and light microscopic ('mesoscopic') levels, and the unimaginable complexity of synaptic connections between axons and dendrites at the electron microscopic ('microscopic') level. At the same time, the data highlighted the necessity to make substantial advancement in methodology of the connectomic studies, including efficient handling and automated analyses of the acquired dataset. Further understanding about structural and functional connectome seems to be facilitated by combinations of the different imaging modalities. Such multidisciplinary approaches will give us the clues to address whether the complete connectome can elucidate fundamental mechanisms processing the basic and higher functions of human brains.

Functional Specialization of Neural Input Elements to the Drosophila ON Motion Detector

Detecting the direction of visual movement is fundamental for every sighted animal in order to navigate, avoid predators, or detect conspecifics. Algorithmic models of correlation-type motion detectors describe the underlying computation remarkably well. They consist of two spatially separated input lines that are asymmetrically filtered in time and then interact in a nonlinear way. However, the cellular implementation of this computation remains elusive. Recent connectomic data of the Drosophila optic lobe has suggested a neural circuit for the detection of moving bright edges (ON motion) with medulla cells Mi1 and Tm3 providing spatially offset input to direction-selective T4 cells, thereby forming the two input lines of a motion detector. Electrophysiological characterization of Mi1 and Tm3 revealed different temporal filtering properties and proposed them to correspond to the delayed and direct input, respectively. Here, we test this hypothesis by silencing either Mi1 or Tm3 cells and using electrophysiological recordings and behavioral responses of flies as a readout. We show that Mi1 is a necessary element of the ON pathway under all stimulus conditions. In contrast, Tm3 is specifically required only for the detection of fast ON motion in the preferred direction. We thereby provide first functional evidence that Mi1 and Tm3 are key elements of the ON pathway and uncover an unexpected functional specialization of these two cell types. Our results thus require an elaboration of the currently prevailing model for ON motion detection and highlight the importance of functional studies for neural circuit breaking.