The intrinsic electrophysiological characteristics of fly lobula plate tangential cells: I. Passive membrane properties

Morphology and synapse topography optimize linear encoding of synapse numbers in Drosophila looming responsive descending neurons

Synapses are often precisely organized on dendritic arbors, yet the role of synaptic topography in dendritic integration remains poorly understood. Utilizing electron microscopy (EM) connectomics we investigate synaptic topography in Drosophila melanogaster looming circuits, focusing on retinotopically tuned visual projection neurons (VPNs) that synapse onto descending neurons (DNs). Synapses of a given VPN type project to non-overlapping regions on DN dendrites. Within these spatially constrained clusters, synapses are not retinotopically organized, but instead adopt near random distributions. To investigate how this organization strategy impacts DN integration, we developed multicompartment models of DNs fitted to experimental data and using precise EM morphologies and synapse locations. We find that DN dendrite morphologies normalize EPSP amplitudes of individual synaptic inputs and that near random distributions of synapses ensure linear encoding of synapse numbers from individual VPNs. These findings illuminate how synaptic topography influences dendritic integration and suggest that linear encoding of synapse numbers may be a default strategy established through connectivity and passive neuron properties, upon which active properties and plasticity can then tune as needed.

Bioinspired figure-ground discrimination via visual motion smoothing

Flies detect and track moving targets among visual clutter, and this process mainly relies on visual motion. Visual motion is analyzed or computed with the pathway from the retina to T4/T5 cells. The computation of local directional motion was formulated as an elementary movement detector (EMD) model more than half a century ago. Solving target detection or figure-ground discrimination problems can be equivalent to extracting boundaries between a target and the background based on the motion discontinuities in the output of a retinotopic array of EMDs. Individual EMDs cannot measure true velocities, however, due to their sensitivity to pattern properties such as luminance contrast and spatial frequency content. It remains unclear how local directional motion signals are further integrated to enable figure-ground discrimination. Here, we present a computational model inspired by fly motion vision. Simulations suggest that the heavily fluctuating output of an EMD array is naturally surmounted by a lobula network, which is hypothesized to be downstream of the local motion detectors and have parallel pathways with distinct directional selectivity. The lobula network carries out a spatiotemporal smoothing operation for visual motion, especially across time, enabling the segmentation of moving figures from the background. The model qualitatively reproduces experimental observations in the visually evoked response characteristics of one type of lobula columnar (LC) cell. The model is further shown to be robust to natural scene variability. Our results suggest that the lobula is involved in local motion-based target detection.

The cellular architecture of memory modules in Drosophila supports stochastic input integration

The ability to associate neutral stimuli with valence information and to store these associations as memories forms the basis for decision making. To determine the underlying computational principles, we build a realistic computational model of a central decision module within the Drosophila mushroom body (MB), the fly's center for learning and memory. Our model combines the electron microscopy-based architecture of one MB output neuron (MBON-α3), the synaptic connectivity of its 948 presynaptic Kenyon cells (KCs), and its membrane properties obtained from patch-clamp recordings. We show that this neuron is electrotonically compact and that synaptic input corresponding to simulated odor input robustly drives its spiking behavior. Therefore, sparse innervation by KCs can efficiently control and modulate MBON activity in response to learning with minimal requirements on the specificity of synaptic localization. This architecture allows efficient storage of large numbers of memories using the flexible stochastic connectivity of the circuit.

Connectomic features underlying diverse synaptic connection strengths and subcellular computation

Connectomes generated from electron microscopy images of neural tissue unveil the complex morphology of every neuron and the locations of every synapse interconnecting them. These wiring diagrams may also enable inference of synaptic and neuronal biophysics, such as the functional weights of synaptic connections, but this requires integration with physiological data to properly parameterize. Working with a stereotyped olfactory network in the Drosophila brain, we make direct comparisons of the anatomy and physiology of diverse neurons and synapses with subcellular and subthreshold resolution. We find that synapse density and location jointly predict the amplitude of the somatic postsynaptic potential evoked by a single presynaptic spike. Biophysical models fit to data predict that electrical compartmentalization allows axon and dendrite arbors to balance independent and interacting computations. These findings begin to fill the gap between connectivity maps and activity maps, which should enable new hypotheses about how network structure constrains network function.

A general principle of dendritic constancy: A neuron's size- and shape-invariant excitability

Reducing neuronal size results in less membrane and therefore lower input conductance. Smaller neurons are thus more excitable, as seen in their responses to somatic current injections. However, the impact of a neuron's size and shape on its voltage responses to dendritic synaptic activation is much less understood. Here we use analytical cable theory to predict voltage responses to distributed synaptic inputs in unbranched cables, showing that these are entirely independent of dendritic length. For a given synaptic density, neuronal responses depend only on the average dendritic diameter and intrinsic conductivity. This remains valid for a wide range of morphologies irrespective of their arborization complexity. Spiking models indicate that morphology-invariant numbers of spikes approximate the percentage of active synapses. In contrast to spike rate, spike times do depend on dendrite morphology. In summary, neuronal excitability in response to distributed synaptic inputs is largely unaffected by dendrite length or complexity.

Maximally efficient prediction in the early fly visual system may support evasive flight maneuvers

The visual system must make predictions to compensate for inherent delays in its processing. Yet little is known, mechanistically, about how prediction aids natural behaviors. Here, we show that despite a 20-30ms intrinsic processing delay, the vertical motion sensitive (VS) network of the blowfly achieves maximally efficient prediction. This prediction enables the fly to fine-tune its complex, yet brief, evasive flight maneuvers according to its initial ego-rotation at the time of detection of the visual threat. Combining a rich database of behavioral recordings with detailed compartmental modeling of the VS network, we further show that the VS network has axonal gap junctions that are critical for optimal prediction. During evasive maneuvers, a VS subpopulation that directly innervates the neck motor center can convey predictive information about the fly’s future ego-rotation, potentially crucial for ongoing flight control. These results suggest a novel sensory-motor pathway that links sensory prediction to behavior.

Survival-critical behaviors shape neural circuits to translate sensory information into strikingly fast predictions, e.g. in escaping from a predator faster than the system’s processing delay. We show that the fly visual system implements fast and accurate prediction of its visual experience. This provides crucial information for directing fast evasive maneuvers that unfold over just 40ms. Our work shows how this fast prediction is implemented, mechanistically, and suggests the existence of a novel sensory-motor pathway from the fly visual system to a wing steering motor neuron. Echoing and amplifying previous work in the retina, our work hypothesizes that the efficient encoding of predictive information is a universal design principle supporting fast, natural behaviors.

Localized inhibition in the Drosophila mushroom body

Many neurons show compartmentalized activity, in which activity does not spread readily across the cell, allowing input and output to occur locally. However, the functional implications of compartmentalized activity for the wider neural circuit are often unclear. We addressed this problem in the Drosophila mushroom body, whose principal neurons, Kenyon cells, receive feedback inhibition from a non-spiking interneuron called the anterior paired lateral (APL) neuron. We used local stimulation and volumetric calcium imaging to show that APL inhibits Kenyon cells’ dendrites and axons, and that both activity in APL and APL’s inhibitory effect on Kenyon cells are spatially localized (the latter somewhat less so), allowing APL to differentially inhibit different mushroom body compartments. Applying these results to the Drosophila hemibrain connectome predicts that individual Kenyon cells inhibit themselves via APL more strongly than they inhibit other individual Kenyon cells. These findings reveal how cellular physiology and detailed network anatomy can combine to influence circuit function.

A connectome and analysis of the adult Drosophila central brain

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain.

Axonal Computations

Axons functionally link the somato-dendritic compartment to synaptic terminals. Structurally and functionally diverse, they accomplish a central role in determining the delays and reliability with which neuronal ensembles communicate. By combining their active and passive biophysical properties, they ensure a plethora of physiological computations. In this review, we revisit the biophysics of generation and propagation of electrical signals in the axon and their dynamics. We further place the computational abilities of axons in the context of intracellular and intercellular coupling. We discuss how, by means of sophisticated biophysical mechanisms, axons expand the repertoire of axonal computation, and thereby, of neural computation.

Universal features of dendrites through centripetal branch ordering

Dendrites form predominantly binary trees that are exquisitely embedded in the networks of the brain. While neuronal computation is known to depend on the morphology of dendrites, their underlying topological blueprint remains unknown. Here, we used a centripetal branch ordering scheme originally developed to describe river networks—the Horton-Strahler order (SO)–to examine hierarchical relationships of branching statistics in reconstructed and model dendritic trees. We report on a number of universal topological relationships with SO that are true for all binary trees and distinguish those from SO-sorted metric measures that appear to be cell type-specific. The latter are therefore potential new candidates for categorising dendritic tree structures. Interestingly, we find a faithful correlation of branch diameters with centripetal branch orders, indicating a possible functional importance of SO for dendritic morphology and growth. Also, simulated local voltage responses to synaptic inputs are strongly correlated with SO. In summary, our study identifies important SO-dependent measures in dendritic morphology that are relevant for neural function while at the same time it describes other relationships that are universal for all dendrites.

Similarly to river beds, dendritic trees of nerve cells form elaborate networks that branch out to cover extensive areas. In the 1940s, ecologist Robert E. Horton developed an ordering system for branches in river networks that was refined in the 1950s by geoscientist Arthur N. Strahler, the Horton-Strahler order (SO). Branches at the tips start with order 1 and increase their order in a systematic way when encountering new branches on the way to the root. SO relationships have recently become popular for quantifying dendritic morphologies. Various branching statistics can be studied as a function of SO. Here we describe that topological measures such as the number of branches, the branch bifurcation ratio and the size of subtrees exhibit stereotypical relations with SO in dendritic trees independently of cell type, mirroring universal features of binary trees. Other functionally more relevant features such as mean branch lengths, local diameters and simulated voltage responses to synaptic inputs directly correlate with SO in a cell type-specific manner, indicating the importance of SO for understanding dendrite growth as well as neural computation.

Subcellular Imaging of Voltage and Calcium Signals Reveals Neural Processing In Vivo

A mechanistic understanding of neural computation requires determining how information is processed as it passes through neurons and across synapses. However, it has been challenging to measure membrane potential changes in axons and dendrites in vivo. We use in vivo, two-photon imaging of novel genetically encoded voltage indicators, as well as calcium imaging, to measure sensory stimulus-evoked signals in the Drosophila visual system with subcellular resolution. Across synapses, we find major transformations in the kinetics, amplitude, and sign of voltage responses to light. We also describe distinct relationships between voltage and calcium signals in different neuronal compartments, a substrate for local computation. Finally, we demonstrate that ON and OFF selectivity, a key feature of visual processing across species, emerges through the transformation of membrane potential into intracellular calcium concentration. By imaging voltage and calcium signals to map information flow with subcellular resolution, we illuminate where and how critical computations arise.

Firing-rate resonances in the peripheral auditory system of the cricket, Gryllus bimaculatus

In many communication systems, information is encoded in the temporal pattern of signals. For rhythmic signals that carry information in specific frequency bands, a neuronal system may profit from tuning its inherent filtering properties towards a peak sensitivity in the respective frequency range. The cricket Gryllus bimaculatus evaluates acoustic communication signals of both conspecifics and predators. The song signals of conspecifics exhibit a characteristic pulse pattern that contains only a narrow range of modulation frequencies. We examined individual neurons (AN1, AN2, ON1) in the peripheral auditory system of the cricket for tuning towards specific modulation frequencies by assessing their firing-rate resonance. Acoustic stimuli with a swept-frequency envelope allowed an efficient characterization of the cells' modulation transfer functions. Some of the examined cells exhibited tuned band-pass properties. Using simple computational models, we demonstrate how different, cell-intrinsic or network-based mechanisms such as subthreshold resonances, spike-triggered adaptation, as well as an interplay of excitation and inhibition can account for the experimentally observed firing-rate resonances. Therefore, basic neuronal mechanisms that share negative feedback as a common theme may contribute to selectivity in the peripheral auditory pathway of crickets that is designed towards mate recognition and predator avoidance.

Topological characterization of neuronal arbor morphology via sequence representation: I--motif analysis

BACKGROUND

The morphology of neurons offers many insights into developmental processes and signal processing. Numerous reports have focused on metrics at the level of individual branches or whole arbors; however, no studies have attempted to quantify repeated morphological patterns within neuronal trees. We introduce a novel sequential encoding of neurite branching suitable to explore topological patterns.

RESULTS

Using all possible branching topologies for comparison we show that the relative abundance of short patterns of up to three bifurcations, together with overall tree size, effectively capture the local branching patterns of neurons. Dendrites and axons display broadly similar topological motifs (over-represented patterns) and anti-motifs (under-represented patterns), differing most in their proportions of bifurcations with one terminal branch and in select sub-sequences of three bifurcations. In addition, pyramidal apical dendrites reveal a distinct motif profile.

CONCLUSIONS

The quantitative characterization of topological motifs in neuronal arbors provides a thorough description of local features and detailed boundaries for growth mechanisms and hypothesized computational functions.

SPIN: a method of skeleton-based polarity identification for neurons

Directional signal transmission is essential for neural circuit function and thus for connectomic analysis. The directions of signal flow can be obtained by experimentally identifying neuronal polarity (axons or dendrites). However, the experimental techniques are not applicable to existing neuronal databases in which polarity information is not available. To address the issue, we proposed SPIN: a method of Skeleton-based Polarity Identification for Neurons. SPIN was designed to work with large-scale neuronal databases in which tracing-line data are available. In SPIN, a classifier is first trained by neurons with known polarity in two steps: 1) identifying morphological features that most correlate with the polarity and 2) constructing a linear classifier by determining a discriminant axis (a specific combination of the features) and decision boundaries. Each polarity-undefined neuron is then divided into several morphological substructures (domains) and the corresponding polarities are determined using the classifier. Finally, the result is evaluated and warnings for potential errors are returned. We tested this method on fruitfly (Drosophila melanogaster) and blowfly (Calliphora vicina and Calliphora erythrocephala) unipolar neurons using data obtained from the Flycircuit and Neuromorpho databases, respectively. On average, the polarity of 84-92 % of the terminal points in each neuron could be correctly identified. An ideal performance with an accuracy between 93 and 98 % can be achieved if we fed SPIN with relatively "clean" data without artificial branches. Our result demonstrates that SPIN, as a computer-based semi-automatic method, provides quick and accurate polarity identification and is particularly suitable for analyzing large-scale data. We implemented SPIN in Matlab and released the codes under the GPLv3 license.

Preserving neural function under extreme scaling

Important brain functions need to be conserved throughout organisms of extremely varying sizes. Here we study the scaling properties of an essential component of computation in the brain: the single neuron. We compare morphology and signal propagation of a uniquely identifiable interneuron, the HS cell, in the blowfly (Calliphora) with its exact counterpart in the fruit fly (Drosophila) which is about four times smaller in each dimension. Anatomical features of the HS cell scale isometrically and minimise wiring costs but, by themselves, do not scale to preserve the electrotonic behaviour. However, the membrane properties are set to conserve dendritic as well as axonal delays and attenuation as well as dendritic integration of visual information. In conclusion, the electrotonic structure of a neuron, the HS cell in this case, is surprisingly stable over a wide range of morphological scales.

Optic glomeruli and their inputs in Drosophila share an organizational ground pattern with the antennal lobes

Studying the insect visual system provides important data on the basic neural mechanisms underlying visual processing. As in vertebrates, the first step in visual processing in insects is through a series of retinotopic neurons. Recent studies on flies have found that these converge onto assemblies of columnar neurons in the lobula, the axons of which segregate to project to discrete optic glomeruli in the lateral protocerebrum. This arrangement is much like the fly's olfactory system, in which afferents target uniquely identifiable olfactory glomeruli. Here, whole-cell patch recordings show that even though visual primitives are unreliably encoded by single lobula output neurons because of high synaptic noise, they are reliably encoded by the ensemble of outputs. At a glomerulus, local interneurons reliably code visual primitives, as do projection neurons conveying information centrally from the glomerulus. These observations demonstrate that in Drosophila, as in other dipterans, optic glomeruli are involved in further reconstructing the fly's visual world. Optic glomeruli and antennal lobe glomeruli share the same ancestral anatomical and functional ground pattern, enabling reliable responses to be extracted from converging sensory inputs.

Digital reconstructions of neuronal morphology: three decades of research trends

The importance of neuronal morphology has been recognized from the early days of neuroscience. Elucidating the functional roles of axonal and dendritic arbors in synaptic integration, signal transmission, network connectivity, and circuit dynamics requires quantitative analyses of digital three-dimensional reconstructions. We extensively searched the scientific literature for all original reports describing reconstructions of neuronal morphology since the advent of this technique three decades ago. From almost 50,000 titles, 30,000 abstracts, and more than 10,000 full-text articles, we identified 902 publications describing ∼44,000 digital reconstructions. Reviewing the growth of this field exposed general research trends on specific animal species, brain regions, neuron types, and experimental approaches. The entire bibliography, annotated with relevant metadata and (wherever available) direct links to the underlying digital data, is accessible at NeuroMorpho.Org.

Candidate glutamatergic neurons in the visual system of Drosophila

The visual system of Drosophila contains approximately 60,000 neurons that are organized in parallel, retinotopically arranged columns. A large number of these neurons have been characterized in great anatomical detail. However, studies providing direct evidence for synaptic signaling and the neurotransmitter used by individual neurons are relatively sparse. Here we present a first layout of neurons in the Drosophila visual system that likely release glutamate as their major neurotransmitter. We identified 33 different types of neurons of the lamina, medulla, lobula and lobula plate. Based on the previous Golgi-staining analysis, the identified neurons are further classified into 16 major subgroups representing lamina monopolar (L), transmedullary (Tm), transmedullary Y (TmY), Y, medulla intrinsic (Mi, Mt, Pm, Dm, Mi Am), bushy T (T), translobula plate (Tlp), lobula intrinsic (Lcn, Lt, Li), lobula plate tangential (LPTCs) and lobula plate intrinsic (LPi) cell types. In addition, we found 11 cell types that were not described by the previous Golgi analysis. This classification of candidate glutamatergic neurons fosters the future neurogenetic dissection of information processing in circuits of the fly visual system.

Neurons with cholinergic phenotype in the visual system of Drosophila

The optic lobe of Drosophila houses about 60,000 neurons that are organized in parallel, retinotopically arranged columns. Based on the Golgi-staining method, Fischbach and Dittrich ([1989] Cell Tissue Res 258:441-475) determined that each column contains about 90 identified cells. Each of these cells is supposed to release one or two different neurotransmitters. However, for most cells the released neurotransmitter is not known. Here we characterize the vast majority of the neurons in the Drosophila optic lobe that release acetylcholine (Ach), the major excitatory neurotransmitter of the insect central nervous system. We employed a promoter specific for cholinergic neurons and restricted its activity to single or a few cells using the MARCM technique. This approach allowed us to establish an anatomical map of neurons with a cholinergic phenotype based on their branching pattern. We identified 43 different types of neurons with a cholinergic phenotype. Thirty-one of them match previously described members of nine different subgroups: Transmedullary (Tm), Transmedullary Y (TmY), Medulla intrinsic (Mi, Mt, and Pm), Bushy T (T), Translobula Plate (Tlp), and Lobula intrinsic (Lcn and Lt) neurons (Fischbach and Dittrich [1989]). Intriguingly, 12 newly identified cell types suggest that previous Golgi studies were not saturating and that the actual number of different neurons per column is higher than previously thought. This study and similar ones on other neurotransmitter systems will contribute towards a columnar wiring diagram and foster the functional dissection of the visual circuitry in Drosophila.

Neural action fields for optic flow based navigation: a simulation study of the fly lobula plate network

Optic flow based navigation is a fundamental way of visual course control described in many different species including man. In the fly, an essential part of optic flow analysis is performed in the lobula plate, a retinotopic map of motion in the environment. There, the so-called lobula plate tangential cells possess large receptive fields with different preferred directions in different parts of the visual field. Previous studies demonstrated an extensive connectivity between different tangential cells, providing, in principle, the structural basis for their large and complex receptive fields. We present a network simulation of the tangential cells, comprising most of the neurons studied so far (22 on each hemisphere) with all the known connectivity between them. On their dendrite, model neurons receive input from a retinotopic array of Reichardt-type motion detectors. Model neurons exhibit receptive fields much like their natural counterparts, demonstrating that the connectivity between the lobula plate tangential cells indeed can account for their complex receptive field structure. We describe the tuning of a model neuron to particular types of ego-motion (rotation as well as translation around/along a given body axis) by its ‘action field’. As we show for model neurons of the vertical system (VS-cells), each of them displays a different type of action field, i.e., responds maximally when the fly is rotating around a particular body axis. However, the tuning width of the rotational action fields is relatively broad, comparable to the one with dendritic input only. The additional intra-lobula-plate connectivity mainly reduces their translational action field amplitude, i.e., their sensitivity to translational movements along any body axis of the fly.

An inverse approach for elucidating dendritic function

We outline an inverse approach for investigating dendritic function-structure relationships by optimizing dendritic trees for a priori chosen computational functions. The inverse approach can be applied in two different ways. First, we can use it as a "hypothesis generator" in which we optimize dendrites for a function of general interest. The optimization yields an artificial dendrite that is subsequently compared to real neurons. This comparison potentially allows us to propose hypotheses about the function of real neurons. In this way, we investigated dendrites that optimally perform input-order detection. Second, we can use it as a "function confirmation" by optimizing dendrites for functions hypothesized to be performed by classes of neurons. If the optimized, artificial, dendrites resemble the dendrites of real neurons the artificial dendrites corroborate the hypothesized function of the real neuron. Moreover, properties of the artificial dendrites can lead to predictions about yet unmeasured properties. In this way, we investigated wide-field motion integration performed by the VS cells of the fly visual system. In outlining the inverse approach and two applications, we also elaborate on the nature of dendritic function. We furthermore discuss the role of optimality in assigning functions to dendrites and point out interesting future directions.

Processing of horizontal optic flow in three visual interneurons of the Drosophila brain

Motion vision is essential for navigating through the environment. Due to its genetic amenability, the fruit fly Drosophila has been serving for a lengthy period as a model organism for studying optomotor behavior as elicited by large-field horizontal motion. However, the neurons underlying the control of this behavior have not been studied in Drosophila so far. Here we report the first whole cell recordings from three cells of the horizontal system (HSN, HSE, and HSS) in the lobula plate of Drosophila. All three HS cells are tuned to large-field horizontal motion in a direction-selective way; they become excited by front-to-back motion and inhibited by back-to-front motion in the ipsilateral field of view. The response properties of HS cells such as contrast and velocity dependence are in accordance with the correlation-type model of motion detection. Neurobiotin injection suggests extensive coupling among ipsilateral HS cells and additional coupling to tangential cells that have their dendrites in the contralateral hemisphere of the brain. This connectivity scheme accounts for the complex layout of their receptive fields and explains their sensitivity both to ipsilateral and to contralateral motion. Thus the main response properties of Drosophila HS cells are strikingly similar to the responses of their counterparts in the blowfly Calliphora, although we found substantial differences with respect to their dendritic structure and connectivity. This long-awaited functional characterization of HS cells in Drosophila provides the basis for the future dissection of optomotor behavior and the underlying neural circuitry by combining genetics, physiology, and behavior.

Neural simulations on multi-core architectures

Neuroscience is witnessing increasing knowledge about the anatomy and electrophysiological properties of neurons and their connectivity, leading to an ever increasing computational complexity of neural simulations. At the same time, a rather radical change in personal computer technology emerges with the establishment of multi-cores: high-density, explicitly parallel processor architectures for both high performance as well as standard desktop computers. This work introduces strategies for the parallelization of biophysically realistic neural simulations based on the compartmental modeling technique and results of such an implementation, with a strong focus on multi-core architectures and automation, i.e. user-transparent load balancing.

Signal propagation in Drosophila central neurons

Drosophila is an important model organism for investigating neural development, neural morphology, neurophysiology, and neural correlates of behaviors. However, almost nothing is known about how electrical signals propagate in Drosophila neurons. Here, we address these issues in antennal lobe projection neurons, one of the most well studied classes of Drosophila neurons. We use morphological and electrophysiological data to deduce the passive membrane properties of these neurons and to build a compartmental model of their electrotonic structure. We find that these neurons are electrotonically extensive and that a somatic recording electrode can only imperfectly control the voltage in the rest of the cell. Simulations predict that action potentials initiate at a location distant from the soma, in the proximal portion of the axon. Simulated synaptic input to a single dendritic branch propagates poorly to the rest of the cell and cannot match the size of real unitary synaptic events, but we can obtain a good fit to data when we model unitary input synapses as dozens of release sites distributed across many dendritic branches. We also show that the true resting potential of these neurons is more hyperpolarized than previously thought, attributable to the experimental error introduced by the electrode seal conductance. A leak sodium conductance also contributes to the resting potential. Together, these findings have fundamental implications for how these neurons integrate their synaptic inputs. Our results also have important consequences for the design and interpretation of experiments aimed at understanding Drosophila neurons and neural circuits.

From form to function: the ways to know a neuron

The shape of a neuron, its morphological signature, dictates the neuron's function by establishing its synaptic partnerships. Here, we review various anatomical methods used to reveal neuron shape and the contributions these have made to our current understanding of neural function in the Drosophila brain, especially the optic lobe. These methods, including Golgi impregnation, genetic reporters, and electron microscopy (EM), necessarily incorporate biases of various sorts that are easy to overlook, but that filter the morphological signatures we see. Nonetheless, the application of these methods to the optic lobe has led to reassuringly congruent findings on the number and shapes of neurons and their connection patterns, indicating that morphological classes are actually genetic classes. Genetic methods using, especially, GAL4 drivers and associated reporters have largely superceded classical Golgi methods for cellular analyses and, moreover, allow the manipulation of neuronal activity, thus enabling us to establish a bridge between morphological studies and functional ones. While serial-EM reconstruction remains the only reliable, albeit labor-intensive, method to determine actual synaptic connections, genetic approaches in combination with EM or high-resolution light microscopic techniques are promising methods for the rapid determination of synaptic circuit function.

The morphological identity of insect dendrites

Dendrite morphology, a neuron's anatomical fingerprint, is a neuroscientist's asset in unveiling organizational principles in the brain. However, the genetic program encoding the morphological identity of a single dendrite remains a mystery. In order to obtain a formal understanding of dendritic branching, we studied distributions of morphological parameters in a group of four individually identifiable neurons of the fly visual system. We found that parameters relating to the branching topology were similar throughout all cells. Only parameters relating to the area covered by the dendrite were cell type specific. With these areas, artificial dendrites were grown based on optimization principles minimizing the amount of wiring and maximizing synaptic democracy. Although the same branching rule was used for all cells, this yielded dendritic structures virtually indistinguishable from their real counterparts. From these principles we derived a fully-automated model-based neuron reconstruction procedure validating the artificial branching rule. In conclusion, we suggest that the genetic program implementing neuronal branching could be constant in all cells whereas the one responsible for the dendrite spanning field should be cell specific.

Neural computation has been shown to be heavily dependent not only on the connectivity of single neurons but also on their specific dendritic shape—often used as a key feature for their classification. Still, very little is known about the constraints determining a neuron's morphological identity. In particular, one would like to understand what cells with the same or similar function share anatomically, what renders them different from others, and whether one can formalize this difference objectively. A large number of approaches have been proposed, trying to put dendritic morphology in a parametric frame. A central problem lies in the wide variety and variability of dendritic branching and function even within one narrow cell class. We addressed this problem by investigating functionally and anatomically highly conserved neurons in the fly brain, where each neuron can easily be individually identified in different animals. Our analysis shows that the pattern of dendritic branching is not unique in any particular cell, only the features of the area that the dendrites cover allow a clear classification. This leads to the conclusion that all fly dendrites share the same growth program but a neuron's dendritic field shape, its “anatomical receptive field”, is key to its specific identity.

Optimization principles of dendritic structure

Background

Dendrites are the most conspicuous feature of neurons. However, the principles determining their structure are poorly understood. By employing cable theory and, for the first time, graph theory, we describe dendritic anatomy solely on the basis of optimizing synaptic efficacy with minimal resources.

Results

We show that dendritic branching topology can be well described by minimizing the path length from the neuron's dendritic root to each of its synaptic inputs while constraining the total length of wiring. Tapering of diameter toward the dendrite tip – a feature of many neurons – optimizes charge transfer from all dendritic synapses to the dendritic root while housekeeping the amount of dendrite volume. As an example, we show how dendrites of fly neurons can be closely reconstructed based on these two principles alone.

Robust coding of flow-field parameters by axo-axonal gap junctions between fly visual interneurons

Complex flight maneuvers require a sophisticated system to exploit the optic flow resulting from moving images of the environment projected onto the retina. In the fly's visual course control center, the lobula plate, 10 so-called vertical system (VS) cells are thought to match, with their complex receptive fields, the optic flow resulting from rotation around different body axes. However, signals of single VS cells are unreliable indicators of such optic flow parameters in the context of their noisy, texture-dependent input from local motion measurements. Here we propose an alternative encoding scheme based on network simulations of biophysically realistic compartmental models of VS cells. The simulations incorporate recent data about the highly selective connectivity between VS cells consisting of an electrical axo-axonal coupling between adjacent cells and a reciprocal inhibition between the most distant cells. We find that this particular wiring performs a linear interpolation between the output signals of VS cells, leading to a robust representation of the axis of rotation even in the presence of textureless patches of the visual surround.

Direction-selective adaptation in fly visual motion-sensitive neurons is generated by an intrinsic conductance-based mechanism

Motion-sensitive neurons in the blowfly brain present an ideal model system to study the cellular mechanisms and functional significance of adaptation to visual motion. Various adaptation processes have been described, but it is still largely unknown which of these processes are generated in the motion-sensitive neurons themselves and which originate at more peripheral processing stages. By input resistance measurements I demonstrate that direction-selective adaptation is generated by an activity-dependent conductance increase in the motion-sensitive neurons. Based on correlations between dendritic Ca(2+) accumulation and slow hyperpolarizing after-potentials following excitatory stimulation, a regulation of direction-selective adaptation by Ca(2+) has previously been suggested. In the present study, however, adaptation phenomena are not evoked when the cytosolic Ca(2+) concentration is elevated by ultraviolet photolysis of caged Ca(2+) in single neurons rather than by motion stimulation. This result renders it unlikely, that adaptation in fly motion-sensitive neurons is regulated by bulk cytosolic Ca(2+).

Integration of lobula plate output signals by DNOVS1, an identified premotor descending neuron

Many motion-sensitive tangential cells of the lobula plate in blowflies are well described with respect to their visual response properties and the connectivity among them. They have large and complex receptive fields with different preferred directions in different parts of their receptive fields matching the optic flow that occurs during various flight maneuvers. However, much less is known about how tangential cells connect to postsynaptic neurons descending to the motor circuits in the thoracic ganglion and how optic flow is represented in these downstream neurons. Here we describe the physiology and the connectivity of a prominent descending neuron called DNOVS1 (for descending neurons of the ocellar and vertical system). We find that DNOVS1 is electrically coupled to a subset of vertical system cells. The specific wiring leads to a preference of DNOVS1 for rotational flow fields around a particular body axis. In addition, DNOVS1 receives input from interneurons connected to the ocelli.

Synapses in the fly motion-vision pathway: evidence for a broad range of signal amplitudes and dynamics

Synapses are generally considered to operate efficiently only when their signaling range matches the spectrum of prevailing presynaptic signals in terms of both amplitudes and dynamics. However, the prerequisites for optimally matching the signaling ranges may differ between spike-mediated and graded synaptic transmission. This poses a problem for synapses that convey both graded and spike signals at the same time. We addressed this issue by tracing transmission systematically in vivo in the blowfly's visual-motion pathway by recording from single neurons that receive mixed potential signals consisting of rather slow graded fluctuations superimposed with highly variable spikes from a small number of presynaptic elements. Both pre- and postsynaptic neurons were previously shown to represent preferred-direction motion velocity reliably and linearly at low fluctuation frequencies. To selectively assess the performance of individual synapses and to precisely control presynaptic signals, we voltage clamped one of the presynaptic neurons. Results showed that synapses can effectively convey signals over a much larger amplitude and frequency range than is normally used during graded transmission of visual signals. An explanation for this unexpected finding might lie in the transmission of the spike component that reaches larger amplitudes and contains higher frequencies than graded signals.

Influence of electrotonic structure and synaptic mapping on the receptive field properties of a collision-detecting neuron

The lobula giant movement detector (LGMD) is a visual interneuron of Orthopteran insects involved in collision avoidance and escape behavior. The LGMD possesses a large dendritic field thought to receive excitatory, retinotopic projections from the entire compound eye. We investigated whether the LGMD's receptive field for local motion stimuli can be explained by its electrotonic structure and the eye's anisotropic sampling of visual space. Five locust (Schistocerca americana) LGMD neurons were stained and reconstructed. We show that the excitatory dendritic field and eye can be fitted by ellipsoids having similar geometries. A passive compartmental model fit to electrophysiological data was used to demonstrate that the LGMD is not electrotonically compact. We derived a spike rate to membrane potential transform using intracellular recordings under visual stimulation, allowing direct comparison between experimental and simulated receptive field properties. By assuming a retinotopic mapping giving equal weight to each ommatidium and equally spaced synapses, the model reproduced the experimental data along the eye equator, though it failed to reproduce the receptive field along the ventral-dorsal axis. Our results illustrate how interactions between the distribution of synaptic inputs and the electrotonic properties of neurons contribute to shaping their receptive fields.

Nonlinear, binocular interactions underlying flow field selectivity of a motion-sensitive neuron

Neurons in many species have large receptive fields that are selective for specific optic flow fields. Here, we studied the neural mechanisms underlying flow field selectivity in lobula plate tangential cells (LPTCs) of the blowfly. Among these cells, the H2 cell responds preferentially to visual stimuli approximating rotational optic flow. Through double recordings from H2 and many other LPTCs, we characterized a bidirectional commissural pathway that allows visual information to be shared between the hemispheres. This pathway is mediated by axo-axonal electrical coupling of H2 and the horizontal system equatorial (HSE) cell located in the opposite hemisphere. Using single-cell ablations, we found that this pathway is sufficient to allow H2 to amplify and attenuate dendritic input during binocular visual stimuli. This is accomplished through a modulation of H2's membrane potential by input from the contralateral HSE cell, which scales the firing rate of H2 during visual stimulation but is not sufficient to induce action potentials.

Implications of functionally different synaptic inputs for neuronal gain and computational properties of fly visual interneurons

Neurons embedded in networks are thought to receive synaptic inputs that do not drive them on their own, but modulate the responsiveness to driving input. Although studies on brain slices have led to detailed knowledge of how nondriving input affects dendritic integration, its origin and functional implications remain unclear. We tackle this issue using an ensemble of fly wide-field visual interneurons. These neurons offer the opportunity not only to combine in vivo recording techniques and natural sensory stimulation but also to interpret electrophysiological results in a behavioral context. By targeted manipulation of the animal's visual input we find a pronounced modulating impact of nondriving input, whereas functionally important cellular properties like direction tuning and the coding of pattern velocity are left almost unaffected. We propose that the integration of functionally different synaptic inputs is a mechanism that immanently equalizes the ensemble's sensitivity irrespective of the specific stimulus conditions.

Spike-frequency adaptation and intrinsic properties of an identified, looming-sensitive neuron

We investigated in vivo the characteristics of spike-frequency adaptation and the intrinsic membrane properties of an identified, looming-sensitive interneuron of the locust optic lobe, the lobula giant movement detector (LGMD). The LGMD had an input resistance of 4-5 MOmega, a membrane time constant of about 8 ms, and exhibited inward rectification and rebound spiking after hyperpolarizing current pulses. Responses to depolarizing current pulses revealed the neuron's intrinsic bursting properties and pronounced spike-frequency adaptation. The characteristics of adaptation, including its time course, the attenuation of the firing rate, the mutual dependency of these two variables, and their dependency on injected current, closely followed the predictions of a model first proposed to describe the adaptation of cat visual cortex pyramidal neurons in vivo. Our results thus validate the model in an entirely different context and suggest that it might be applicable to a wide variety of neurons across species. Spike-frequency adaptation is likely to play an important role in tuning the LGMD and in shaping the variability of its responses to visual looming stimuli.

On the computations analyzing natural optic flow: quantitative model analysis of the blowfly motion vision pathway

For many animals, including humans, the optic flow generated on the eyes during locomotion is an important source of information about self-motion and the structure of the environment. The blowfly has been used frequently as a model system for experimental analysis of optic flow processing at the microcircuit level. Here, we describe a model of the computational mechanisms implemented by these circuits in the blowfly motion vision pathway. Although this model was originally proposed based on simple experimenter-designed stimuli, we show that it is also capable to quantitatively predict the responses to the complex dynamic stimuli a blowfly encounters in free flight. In particular, the model visual system exploits the active saccadic gaze and flight strategy of blowflies in a similar way, as does its neuronal counterpart. The model circuit extracts information about translation velocity in the intersaccadic intervals and thus, indirectly, about the three-dimensional layout of the environment. By stepwise dissection of the model circuit, we determine which of its components are essential for these remarkable features. When accounting for the responses to complex natural stimuli, the model is much more robust against parameter changes than when explaining the neuronal responses to simple experimenter-defined stimuli. In contrast to conclusions drawn from experiments with simple stimuli, optimization of the parameter set for different segments of natural optic flow stimuli do not indicate pronounced adaptational changes of these parameters during long-lasting stimulation.

Dye-coupling visualizes networks of large-field motion-sensitive neurons in the fly

In the fly, visually guided course control is accomplished by a set of 60 large-field motion-sensitive neurons in each brain hemisphere. These neurons have been shown to receive retinotopic motion information from local motion detectors on their dendrites. In addition, recent experiments revealed extensive coupling between the large-field neurons through electrical synapses. These two processes together give rise to their broad and elaborate receptive fields significantly surpassing the extent of their dendritic fields. Here, we demonstrate that the electrical connections between different large-field neurons can be visualized using Neurobiotin dye injection into a single one of them. When combined with a fluorescent dye which does not cross electrical synapses, the injected cell can be identified unambiguously. The Neurobiotin staining corroborates the electrical coupling postulated amongst the cells of the vertical system (VS-cells) and between cells of the horizontal system (HS-cells and CH-cells). In addition, connections between some cells are revealed that have so far not been considered as electrically coupled.

New methods for the computer-assisted 3-D reconstruction of neurons from confocal image stacks

Exact geometrical reconstructions of neuronal architecture are indispensable for the investigation of neuronal function. Neuronal shape is important for the wiring of networks, and dendritic architecture strongly affects neuronal integration and firing properties as demonstrated by modeling approaches. Confocal microscopy allows to scan neurons with submicron resolution. However, it is still a tedious task to reconstruct complex dendritic trees with fine structures just above voxel resolution. We present a framework assisting the reconstruction. User time investment is strongly reduced by automatic methods, which fit a skeleton and a surface to the data, while the user can interact and thus keeps full control to ensure a high quality reconstruction. The reconstruction process composes a successive gain of metric parameters. First, a structural description of the neuron is built, including the topology and the exact dendritic lengths and diameters. We use generalized cylinders with circular cross sections. The user provides a rough initialization by marking the branching points. The axes and radii are fitted to the data by minimizing an energy functional, which is regularized by a smoothness constraint. The investigation of proximity to other structures throughout dendritic trees requires a precise surface reconstruction. In order to achieve accuracy of 0.1 microm and below, we additionally implemented a segmentation algorithm based on geodesic active contours that allow for arbitrary cross sections and uses locally adapted thresholds. In summary, this new reconstruction tool saves time and increases quality as compared to other methods, which have previously been applied to real neurons.

Sensorimotor control of navigation in arthropod and artificial systems

Arthropods exhibit highly efficient solutions to sensorimotor navigation problems. They thus provide a source of inspiration and ideas to robotics researchers. At the same time, attempting to re-engineer these mechanisms in robot hardware and software provides useful insights into how the natural systems might work. This paper reviews three examples of arthropod sensorimotor control systems that have been implemented and tested on robots. First we discuss visual control mechanisms of flies, such as the optomotor reflex and collision avoidance, that have been replicated in analog VLSI (very large scale integration) hardware and used to produce corrective behavior in robot vehicles. Then, we present a robot model of auditory localization in the cricket; and discuss integration of this behavior with the optomotor behavior previously described. Finally we present a model of olfactory search in the moth, which makes use of several sensory cues, and has also been tested using robot hardware. We discuss some of the similarities and differences of the solutions obtained.

Input organization of multifunctional motion-sensitive neurons in the blowfly

Flies rely heavily on visual motion cues for course control. This is mediated by a small set of motion-sensitive neurons called lobula plate tangential cells. A single class of these, the centrifugal horizontal (CH) neurons, play an important role in two pathways: figure-ground discrimination and flow-field selectivity. As was recently found, the dendrites of CH cells are electrically coupled with the dendritic tree of another class of neurons sensitive to horizontal image motion, the horizontal system (HS) cells. However, whether motion information arrives independently at both of these cells or is passed from one to the other is not known. Here, we examine the ipsilateral input circuitry to HS and CH neurons by selective laser ablation of individual interneurons. We find that the response of CH neurons to motion presented in front of the ipsilateral eye is entirely abolished after ablation of HS cells. In contrast, the motion response of HS cells persists after the ablation of CH cells. We conclude that HS cells receive direct motion input from local motion elements, whereas CH cells do not; their motion response is driven by HS cells. This connection scheme is discussed with reference to how the dendritic networks involved in figure-ground detection and flow-field selectivity might operate.

Neural image processing by dendritic networks

Convolution is one of the most common operations in image processing. Based on experimental findings on motion-sensitive visual interneurons of the fly, we show by realistic compartmental modeling that a dendritic network can implement this operation. In a first step, dendritic electrical coupling between two cells spatially blurs the original motion input. The blurred motion image is then passed onto a third cell via inhibitory dendritic synapses resulting in a sharpening of the signal. This enhancement of motion contrast may be the central element of figure-ground discrimination based on relative motion in the fly.