How many neurons drosophila

An efficient method for estimating the total number of neurons in rat brain cortex. J Neurosci Methods. Napper RMA. Front Neuroanat. The reliability of the isotropic fractionator method for counting total cells and neurons. Front Cell Neurosci. How to count cells: the advantages and disadvantages of the isotropic fractionator compared with stereology. Cell Tissue Res.

Herculano-Houzel S, Lent R. J Neurosci. Three counting methods agree on cell and neuron number in chimpanzee primary visual cortex. Use of flow cytometry for high-throughput cell population estimates in brain tissue. Cellular scaling rules of insectivore brains. Cellular scaling rules for rodent brains. Proc Natl Acad Sci. Neuron numbers in sensory cortices of five delphinids compared to a physeterid, the pygmy sperm whale.

Brain Res Bull. Artificial selection on brain size leads to matching changes in overall number of neurons. Gorilla and orangutan brains conform to the primate cellular scaling rules: implications for human evolution. Brain Behav Evol. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Freeman MR, Doherty J. Glial cell biology in Drosophila and vertebrates.

Trends Neurosci. Klowden MJ. Chapter 11—Nervous Systems. In: Klowden MJ, editor. Physiological Systems in Insects Third Edition. San Diego: Academic Press; Single cell transcriptome atlas of the Drosophila larval brain. VijayRaghavan K, editor. The glia of the adult Drosophila nervous system. Mol Biol Evol.

Complete mtDNA genomes of Anopheles darlingi and an approach to anopheline divergence time. Malar J. Zador AM. The basic unit of computation. Nat Neurosci. Martinez P, Sprecher SG. Front Ecol Evol. View Article Google Scholar Dicke U, Roth G. Neuronal factors determining high intelligence. Herculano-Houzel S. The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost. The elephant brain in numbers. Fruitless specifies sexually dimorphic neural circuitry in the Drosophila brain.

Organization of olfactory centres in the malaria mosquito Anopheles gambiae. Nat Commun. Fruitless mutant male mosquitoes gain attraction to human odor. Int J Mol Sci. The non-neuronal population of the brain, likely consisting mainly of glia, is approximately 18, cells in Drosophila and 31, cells in mosquitoes. Although the Culicine Aedes and Culex species and Anopheline Anopheles species mosquitoes diverged about million years ago [ 27 , 28 ], their brain cell population is not statistically different by IF.

The similarities in the brain cell population and cell types suggest that Ae. However, there were approximately 20, more cells in the mosquito whole brain compared to Drosophila. Future studies might be able to determine if particular neuronal regions in the mosquito brain have expanded in comparison to Drosophila , which might implicate neuronal specialization for certain behavioral tasks.

What can we derive from estimating the brain neuron numbers of these dipterans? The total number of neurons in an insect brain represents one measure of its computational power [ 29 ]. Neuronal cell population is crucial for brain functionality but is not the only determinant of brain information processing capacity. Other factors, including the number of neuronal connections and packing density, axonal conduction velocity, diversification of neuronal types and their properties [ 30 , 31 ], all contribute to the broad behavioral repertoire seen in insects.

In mammals, neuronal and non-neuronal cell population varies across every hierarchical order and follows a predictable scaling equation [ 32 ]. In mammalian brains, enrichment of neuronal populations correlate with the complex behaviors the brains can process [ 32 ]. From an evolutionary perspective, body size and brain size are not sufficient to explain the functional complexity of every species [ 1 ]. However, the number of neurons estimated in a human cerebral cortex is triple that of an elephant [ 33 ].

This suggests that the number of brain neurons, but not necessarily brain volume or size, is a key determinant of brain functional complexity. Sex difference in the number of neurons and non-neuronal cells has been reported in the human olfactory bulb, with females having significantly greater number of neurons and glial cells than males [ 5 ]. Sexually dimorphic neural circuitry has also been identified in the olfactory system of Drosophila [ 34 ] and mosquitoes [ 35 , 36 ], which attests to the existence of this phenomenon in the sensory system of insects.

However, the IF method could not identify sexual dimorphism in the brain cell counts of these insects. This does not necessarily rule out sexual dimorphism between brains, yet, does suggest that any sexual differences present would be at small populations or in neuronal processes or synaptic connections undetectable by this method. The emergence of new tools and methods to label smaller brain populations will aid in the identification of cellular differences, if any, in the insect brain.

IF is a simple method that can be used on any dissected tissue [ 13 ]. In this study, we optimized IF for estimating brain cell counts in insects with small brains. The required step of homogenization makes it impossible to reveal information about the structural arrangements of cells within the brain. However, it is a appropriate method to estimate cell populations. The IF method could be applied to a range of insect brains, allowing for better experimental comparison among a variety of organisms.

For example, cellular differences could be investigated in the brain of social insects that display sexual dimorphism such as honeybees. In addition, IF could be combined with immune or genetic labeling of cells to estimate the number of brain subpopulations. The relative ease of the method might even allow IF experiments to be incorporated into lab classroom curriculums. IF could also be applied in insect developmental studies to compare cellular differences in the developmental stages.

For example, an estimate of neuronal proliferation occurring along developmental time points can be investigated. Studies using the Drosophila model to investigate human neurodegenerative diseases [ 37 , 38 ] may adopt IF to estimate neuronal population changes in the nervous system of aging Drosophila.

An accurate estimate of neuron numbers can set the expectations in complexity for full brain reconstruction [ 7 , 39 ]. Unlike the EM-reconstruction performed on a single Drosophila female brain [ 7 , 39 ], the IF approach allows the averaging of many brains to reduce variation among individuals.

The future completion of the Drosophila whole brain EM-reconstruction project will provide an exact count of the number of cells in a single fly brain, and serve as an independent measure for the accuracy of IF in estimating cell numbers as reported in this study.

Data in Fig 2 were re-plotted to allow side-by-side comparisons of neuronal counts in A whole brain, B central brain, and C optic lobe amongst the four insect species. We nonetheless recommend that you implement the minor suggestions made by the reviewers to improve the manuscript. In particular, including a figure panel that compares neuron numbers across species would be a great addition to the work. An invoice for payment will follow shortly after the formal acceptance.

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Reviewer 1: In this paper, Raji et al. These are species with drastically different behavioral response that are governed by the brain. Even though anecdotal reports on the number of neurons in the fruit fly brain, the experiments determining the total number of cells in the brains of these 4 insects are lacking.

Using antibody stainings for neuronal anti-elav and non-neuronal anti-repo cells followed by IF, the authors determine the number of neuronal and nonneuronal cell populations. These experiments identified approximately , cells in fruit fly brain. They also find that the number of cells is increased in mosquito populations as approximately ,, ,, and , for A. This paper is well written and addresses a critical gap in experimental demonstration of number of neurons in insect species commonly used in laboratory research.

The paper is suitable as is for publication in PlosONE. A few minor points:. This reads as if it is referring to a different study maybe unpublished , not the findings in this paper. Where is this number coming from? Is there a way to cite it? Everyone mentions this number but I have always wondered where it came from as well. I think it was even present before the hemibrain connectome, but I also remember people in the field mentioning , neurons in the central brain.

Is there any information on the total number of cells or at least the volume of the body to brain in flies and mosquitoes? As the authors indicate, there might be methodological limitations that allow for the detection of small differences in the number of neurons between males and females. These are definitely known for the Drosophila brain where certain neuronal clusters in the courtship circuits show male and female specific differences in the number of neurons.

Are there known sexual dimorphism in the brains of the mosquito species analyzed? It would be good to add this information to the discussion if it exists.

Consequently, a subset of cubes available at that time was used to train a new classifier focused on addressing recall in the problematic regions.

This new classifier was used in an incremental cascaded fashion, primarily by adding additional predictions to the existing initial set. This gave better performance than complete replacement using only the new classifier, with the resulting predictions able to improve recall while largely maintaining precision. As an independent check on synapse quality, we also trained a separate classifier Buhmann et al. Both synapse predictors give a confidence value associated with each synapse, a measure of how firmly the classifier believes the prediction to be a true synapse.

Figure 5A and B show the results, illustrating the precision and recall obtained in each brain region. T-bar identification is better than PSD identification since this organelle is both more distinct and typically occurs in larger neurites. Each dot is one brain region.

The size of the dot is proportional to the volume of the region. Humans proofreaders typically achieve 0. Data available in Figure 5—source datas 1 — 2. Since machine segmentation is not perfect, we made a concerted effort to fix the errors remaining at this stage by several passes of human proofreading. Enabled by advances in visualization and semi-automated proofreading using our Neu3 tool Hubbard et al.

A human examined each putative neuron and determined if it had an unusual morphology suggesting that a merge might have occurred, a task still much easier for humans than machines. If judged to be a false merger, the operator identified discrete points that should be on separate neurons. The shape was then resegmented in real time allowing users to explore other potential corrections.

Neurons with more complex problems were then scheduled to be re-checked, and the process repeated until few false mergers remained. In the next phase, the largest remaining pieces were merged into neuron shapes using a combination of machine-suggested edits Plaza, and manual intuition, until the main shape of each neuron emerged.

This requires relatively few proofreading decisions and has the advantage of producing an almost complete neuron catalog early in the process. These procedures which focused on higher-level proofreading produced a reasonably accurate library of the main branches of each neuron, and a connectome of the stronger neuronal pathways.

At this point, there was still considerable variations among the brain regions, with greater completeness achieved in regions where the initial segmentation performed better. Finally, to achieve the highest reconstruction completeness possible in the time allotted, and to enable confidence in weaker neuronal pathways, proofreaders connected remaining isolated fragments segments to already constructed neurons, using NeuTu Zhao et al.

The fragments that would result in largest connectivity changes were considered first, exploiting automatic guesses through focused proofreading where possible. Since proofreading every small segment is still prohibitive, we tried to ensure a basic level of completeness throughout the brain with special focus in regions of particular biological interest such as the central complex and mushroom body. In a parallel effort to proofreading, the sample was annotated with discrete brain regions.

For the hemibrain dataset, the regions are based on the brain atlas in Ito et al. The dataset covers most of the right hemisphere of the brain, except the optic lobe OL , periesophageal neuropils PENP and gnathal ganglia GNG , as well as part of the left hemisphere Table 2.

We examined innervation patterns, synapse distribution, and connectivity of reconstructed neurons to define the neuropils as well as their boundaries on the dataset.

We also made necessary, but relatively minor, revisions to some boundaries by considering anatomical features that had not been known during the creation of previous brain maps, while following the existing structural definitions Ito et al.

We also used information from synapse point clouds, a predicted glial mask, and a predicted fiber bundle mask to determine boundaries of the neuropils Figure 6A. The brain regions of the fruit fly Figure 6 , B and C include synaptic neuropils and non-synaptic fiber bundles.

The non-synaptic cell body layer on the brain surface, which contains cell bodies of the neurons and some glia, surrounds these structures.

The synaptic neuropils can be further categorized into two groups: delineated and diffuse neuropils. The delineated neuropils have distinct boundaries throughout their surfaces, often accompanied by glial processes, and have clear internal structures in many cases. Remaining are the diffuse neuropils, sometimes referred to as terra incognita , since most have been less investigated than the delineated neuropils. A A vertical section of the hemibrain dataset with synapse point clouds white , predicted glial tissue green , and predicted fiber bundles magenta.

B Grayscale image overlaid with segmented neuropils at the same level as A. C A frontal view of the reconstructed neuropils. In the previous brain atlas of , boundaries of some terra incognita neuropils were somewhat arbitrarily determined, due to a lack of precise information of the landmark neuronal structures used for the boundary definition.

In the hemibrain data, we adjusted these boundaries to trace more faithfully the contours of the structures that are much better clarified by the EM-reconstructed data.

The boundary between the LH and its surrounding neuropils is barely visible with synaptic immunolabeling such as nc82 or predicted synapse point clouds, as the synaptic contrast in these regions is minimal. Multiglomerular PNs, on the other hand, project to much broader regions, including the volumes around the core LH Figure 7B. These regions include areas which are currently considered parts of the superior lateral protocerebrum SLP and posterior lateral protocerebrum PLP.

Of course, the multiglomerular PNs convey olfactory information as well, and therefore the neighboring parts of the SLP and PLP to some extent also receive inputs from the antennal lobe. These regions might be functionally distinct from the remaining parts of the SLP or PLP, but they are not explicitly separated from those neuropils in this study.

C, D Columnar visual projection neurons. Each subtype of cells is color coded. G Zones in the ellipsoid body EB defined by the innervation patterns of different types of ring neurons. In this horizontal section of the EB, the left side shows the original grayscale data, and the seven ring neuron zones see Table 1 are color-coded. The right side displays the seven segmented zones based on the innervation pattern, in a slightly different section. The VLNP is located in the lateral part of the central brain and receives extensive inputs from the optic lobe through various types of the visual projection neurons VPNs.

In the previous paper Ito et al. Most of them turned out to be projection targets of several classes of central complex neurons, implying the ventral CRE and dorsal LAL are closely related in their function. We re-determined the boundary so that each of the glomerular structures would not be divided into two, while keeping the overall architecture and definition of the CRE and LAL. Substructures of the delineated neuropils have also been added to the brain region map in the hemibrain.

The AB is a small synaptic volume adjacent to the ventral surface of the fan-shaped body FB that has historically been included in the FB Ito et al. These anatomical observations imply that the AB is a ventralmost annexed part of the FB, although this possibility is neither developmentally nor phylogenetically proven.

It is a glomerulus-like structure and one of the foci of the CX output neurons, including the PFR protocerebral bridge — fan-shaped body — round body neurons.

It is classified as a substructure of the CRE along with other less-defined glomerular regions in the neuropil, many of which also receive signals from the CX. Among these, the most prominent one is the rubus RUB. While the ROB and GA have relatively clear boundaries separating them from the surrounding regions, they may not qualify as independent neuropils because of their small size and the structural similarities with the glomerulus-like terminals around them. They may be comparable with other glomerular structures such as the AL glomeruli and the optic glomeruli in the lateral protocerebrum, both of which are considered as substructures of the surrounding neuropils.

Substructures of independent neuropils are also defined using neuronal innervations. Our compartment boundaries were defined by approximating the innervation of these neurons. Although the innervating regions of the MBONs and DANs do not perfectly tile the entire lobes, the compartments have been defined to tile the lobes, so that every synapse in the lobes belongs to one of the 15 compartments.

Here, we summarize the division of its neuropils into compartments. The layer boundaries in our dataset were determined by the pattern of innervation of FB tangential cells, which form nine groups depending on the dorsoventral levels they innervate in the FB. Since tangential cells overlap somewhat, and do not entirely respect the layer boundaries, these boundaries were chosen to maximize the containment of the tangential arbors within their respective layers.

The EB is likewise subdivided into zones by the innervating patterns of the EB ring neurons, the most prominent class of neurons innervating the EB. Among them, the regions innervated by ER2 and ER4 are mutually exclusive but highly intermingled, so these regions are grouped together into a single zone EBr2r4.

ER3 has the most neurons among the ring neuron subtypes and is further grouped into five subclasses ER3a, d, m, p, and w. Unlike other zones, EBr6 is innervated only sparsely by the ER6 cells, with the space filled primarily by synaptic terminals of other neuron types, including the extrinsic ring neurons ExR.

Omoto et al. Our results show that the innervation pattern of each ring neuron subtype is highly compartmentalized at the EM level and the entire neuropil can be sufficiently subdivided into zones based purely on the neuronal morphologies. The neuropil may be subdivided differently if other neuron types, such as the extrinsic ring neurons ExR Omoto et al. Since many of the terra incognita neuropils are not clearly partitioned from each other by solid boundaries such as glial walls, it is important to evaluate if the current boundaries reflect anatomical and functional compartments of the brain.

To check our definitions, which are mostly based on morphology, we compute metrics for each boundary between any two adjacent neuropil regions. The first is the area of each boundary, in square microns, as shown in Figure 8A. By restricting our analysis to the right part of the hemibrain, we hopefully minimize the effect of smaller, traced-but-truncated neuron fragments on our metric.

A Areas of the boundaries in square microns between adjacent neuropils, indicated on a log scale. B The number of excess crossings normalized by the area of neuropil boundary. Larger dots indicate a more uncertain boundary. Data available in Figure 8—source data 1. Column A: index number; column B: first ROI name; column C: second ROI name; column D: boundary area in square microns; column E: number of neurons crossings; column F: number of distinct neurons that cross; column G: crossings - number of neurons per area.

There is no contribution to the metric from neurons that cross a boundary once, since most such crossings are inevitable no matter where the boundary is placed.

Figure 8B shows the number of excess crossings normalized by the area of boundary. A bigger dot indicates a potentially less well-defined boundary. We spot checked many of the instances and in general note that the brain regions with high excess crossings per area, such as those in SNP, INP and VLNP, tend to have less well-defined boundaries. These brain regions were defined based on the arborization patterns of characteristic neuron types, but because neurons in the terra incognita neuropils tend to be rather heterogeneous, there are many other neuron types that do not follow these boundaries.

The current brain regions based on Ito et al. In this study, we tried to solidify the ambiguous boundaries as much as possible using the information from the reconstructed neurons. However, large parts of the left hemisphere and the subesophageal zone SEZ are missing from the hemibrain dataset, and neurons innervating these regions are not sufficiently reconstructed.

This incompleteness of the dataset is the main reason that we did not alter the previous map drastically and kept all the existing brain regions even if their anatomical and functional significance is not obvious. Once a complete EM volume of the whole fly brain is imaged and most of its , neurons are reconstructed, the entire brain can be re-segmented from scratch with more comprehensive anatomical information.

Arbitrary or artificial neuropil boundaries will thereby be minimized, if not avoided, in a new brain map. Anatomy-based neuron segmentation strategies such as NBLAST may be used as neutral methods to revise the neuropils and their boundaries. Any single method, however, is not likely to produce consistent boundaries throughout the brain, especially in the terra incognita regions. It may be necessary to use different methods and criteria to segment the entire brain into reasonable brain regions.

Such a new map would need discussion in a working group, and approval from the community in advance as did the previous map [ Ito et al. Defining cell types for groups of similar neurons is a time-honored means to help to understand the anatomical and functional properties of a circuit. Presumably, neurons of the same type have similar circuit roles.

Therefore, despite our best efforts, we recognize that our typing of cells may not be identical to that proposed by other experts. We expect future revisions to cell type classification, especially as additional dense connectome data become available.

One common method of cell type classification, used in flies, exploits the GAL4 system to highlight the morphology of neurons having similar gene expression Jenett et al. Where they exist and are sufficiently sparse, light lines provide a key method for identifying types by grouping morphologically similar neurons together.

However, there are no guarantees of coverage, and it is difficult to distinguish between neurons of very similar morphology but different connectivity. We enhanced the classic view of morphologically distinct cell types by defining distinct cell types or sub-types based on both morphology and connectivity. Connectivity-based clustering often reveals clear cell type distinctions, even when genetic markers have yet to be found, or when the neuronal morphologies of different types are hardly distinguishable in optical images.

For example, the two PEN protocerebral bridge - ellipsoid body - noduli neurons have very similar forms but quite distinct inputs Figure 9 ; Turner-Evans et al. Based on our previous definition of cell type, many neurons exhibit a unique morphology or connectivity pattern at least within one hemisphere of the brain with a matching type in the other hemisphere in most cases.

Because our hemibrain volume covers only the right-side examples of ipsilaterally-projecting neurons, and the contralateral arborizations of bilaterally-projecting neurons arising from the left side of the brain were in practice very difficult to match to neurons in the right side, many partial neurons were therefore left uncategorized. As a result, many neuron types consisting of a distinct morphology and connectivity have only a single example in our reconstruction. It is possible to provide coarser groupings of neurons.

For instance, most cell types are grouped by their cell body fiber representing a distinct clonal unit, which we discuss in more detail below. Furthermore, each neuron can be grouped with neurons that innervate similar brain regions. In this paper, we do not explicitly formalize this higher level grouping, but data on the innervating brain regions can be readily mined from the dataset.

Assigning types and names to the more than 20, reconstructed cells was a difficult undertaking. Adding to the complexity, prior work focused on morphological similarities and differences, but here we have, for the first time, connectivity information to assist in cell typing as well. Many cell types in well-explored regions have already been described and named in the literature, but existing names can be both inconsistent and ambiguous. The same cell type is often given differing names in different publications, and conversely, the same name, such as PN for projection neuron, is used for many different cell types.

In a few cases, using existing names created conflicts, which we have had to resolve. The names of the antennal lobe local neuron are always preceded by lowercase letters for their cell body locations to differentiate them from the clock neuron names, for example, lLN1 versus LNd.

DN1 and letters for descending neurons e. We classified these neurons in several steps. The first step classified all cells by their lineage, grouping neurons according to their bundle of cell body fibers CBFs. Neuronal cell bodies are located in the cell body layer that surrounds the brain, and each neuron projects a single CBF towards synaptic neuropils. In the central brain, cell bodies of clonally related neurons deriving from a single stem cell called a neuroblast in the insect brain tend to form clusters, from each of which arises one or several bundles of CBFs.

Comparing the location, trajectory, and the combined arborization patterns of all the neurons that arise from a particular CBF with the light microscopy LM image data of the neuronal progeny that derive from single neuroblasts Ito et al.

We carefully examined the trajectory and origins of CBFs of the 15, neurons on the right central brain and identified distinct CBF bundles. Neurons arising from four specific CBF bundles arborize primarily in the contralateral brain side, which is not fully covered in the hemibrain volume.

We characterized these neurons using the arborization patterns in the right-side brain that are formed by the neurons arising from the left-side CBFs. Among the bundles, matched the CBF bundles of 92 known and six newly identified clonal units Ito et al.

The remaining 37 CBF bundles are minor populations and most likely of embryonic origin. In addition, we found 80 segregated cell body fiber bundles SCB, totalling cells with only one or two neurons per bundle.

Many of them are also likely of embryonic origin. We were able to identify another neurons that were not traced up to their cell bodies. For the neurons that arise from the contralateral side, we gave matching neuron names and associated CBF information, provided their specific arborization patterns gave us convincing identity information by comparison with cells that we identified in the right side of the brain.

For the neurons arising from the ventralmost part of the brain outside of the hemibrain volume, we identified and gave them names if we could find convincingly specific arborization patterns, even if the CBF and cell body location data were missing.

Sensory neurons that project to the specific primary sensory centers were also identified insofar as possible. In total, we typed and named 22, neurons. Different stem cells sometimes give rise to neurons with very similar morphologies.

We classified these as different types because of their distinct developmental origin and slightly different locations of their cell bodies and CBFs. Thus, the next step in neuron typing was to cluster neurons within each CBF group. This process consisted of three further steps, as shown in Figure This step is an iterative process, using neuron morphology as a template, regrouping neurons after more careful examination of neuron projection patterns and their connections. Neurons with similar connectivity characteristics but with distinguishable shapes were categorized into different morphology types.

Those with practically indistinguishable shapes but with different connectivity characteristics were categorized into connectivity types within a morphology type. Finally, we validated the cell typing with extensive manual review and visual inspection.

This review allowed us both to confirm cell type identity and help ensure neuron reconstruction accuracy. In total we identified morphology types and connectivity types in the hemibrain dataset. See Table 3 for the detailed numbers and Appendix 1—table 6 for naming schemes for various neuron categories. Brain regions with repetitive array architecture tend to have higher average numbers of cells per type see Figure For example, the central complex includes neurons on both sides of the brain, the mushroom body neurons are identified mostly on the right side, and many left-side antennal lobe sensory neurons are included as they tend to terminate bilaterally.

Because of these differences, the figures shown above do not indicate the number of cells or cell number per type per brain side. In spite of this general rule, we assigned the same neuron type name for the neurons of different lineages in the following four cases. Mushroom body intrinsic neurons called Kenyon cells, which are formed by a set of four near-identical neuroblasts Ito et al. Columnar neurons of the central complex, where neurons arising from different stem cells form repetitive column-like arrangement and are near identical in terms of connectivity with tangential neurons Hanesch et al.

The PAM cluster of the dopaminergic neurons, where one of the hemilineages of the two clonal units forms near identical set of neurons Lee et al. Cell body fiber groupings for neurons of the lateral horn, where systematic neuron names have already been given based on the light microscopy analysis Frechter et al.

Individual cell types exist within the same lineage, however. Following the experiences of taxonomy, we opted for splitting when we could not obtain convincing identity information, a decision designed to ease the task of future researchers.

If we split two similar neuron types into Type 1 and Type 2, then there is a chance future studies might conclude that they are actually subsets of a common cell type. If so, then at that time we can simply merge the two types as Type 1, and leave the other type name unused, and publish a lookup table of the lumping process to keep track of the names that have been merged.

The preceding studies can then be re-interpreted as the analyses on the particular subsets of a common neuron type. If, on the contrary, we lump the two similar neurons into a common type, then a later study finds they are actually a mixture of two neuron types, then it would not be possible to determine which of the two neuron types, or a mixture of them, was analyzed in preceding studies.

In the hemibrain, using the defined brain regions neuropils and reference to known expression driver strains, we were able to assign a cell type to many cells. Even though most of the cell types in the MB and CX were already known, we still found new cell types in these regions, an important vindication of our methods. In these cases, we tried to name them using the existing schemes for these regions, and further refined these morphological groupings with relevant information on connectivity.

To give names to neuron types, we categorized neurons that share certain characteristics into groups and distinguished individual types by adding identifiers IDs with numbers, uppercase letters, or combinations of these. See Appendix 1—table 6 for the summary of the naming schemes of all the neuron types.

Different types of neurons that arborize in each layer were further distinguished by uppercase letters. Thus, for example the FB7B neurons are the second type of tangential neurons that arborize in the seventh layer of FB. We also used uppercase letters to subdivide the neuron types that have previously been reported as a single type to keep naming consistency.

For example, a population of antennal lobe local neurons that has been known as LN2L was divided into five morphology subtypes as lLN2F, 2P, 2R, 2S, and 2T for their full, patchy, regional, star-like and tortuous arborization patterns while still indicating that they are part of the LN2 population. Neuron types that are known to exist were sometimes not identified in the particular brain sample used for the hemibrain EM dataset.

In such cases, the corresponding ID numbers were kept blank. For example, the MBON08 neurons were not identified in the current sample and the number was therefore skipped.

Although the morphology type names generally end with either numbers or uppercase letters, in a few cases lower case letters were used for distinguishing morphological subtypes to keep the naming convention of that cell group consistent. For example, subtypes of the neurons in the optic lobes were distinguished as, for example LC28a and LC28b, because such subtypes of the optic lobe neurons have historically been distinguished by lowercase letters.

A neuron type without such a suffix consists of a single connectivity type. Across the brain, we looked for neurons that correspond to already known cell types, and as far as possible gave them consistent names. These include: olfactory projection neurons and local neurons associated with the antennal lobe Tanaka et al. In some cases, we found candidate neurons that do not precisely match previously identified neurons.

We also found that the FB2B neurons share the same cell body location and appear to match another type of tdc2-Gal4 expressing neurons in the FlyCircuit database. Due to similar considerations, the number of candidate neurons may not match the actual known numbers for many neuron types.

For the multiglomerular olfactory projection neurons and local interneurons of the antennal lobe, we devised new naming schemes by expanding the naming scheme of uniglomerular projection neurons, which consists of the contributing antennal lobe glomerulus and the location of the cell body cluster Bates et al. Because the list of contributing glomeruli is not a useful designator for the multiglomerular projection neurons, we used information about the antennal lobe tract ALT projection pathways instead.

Unique type ID numbers were then added at the end of the names of the multiglomerular projection neurons and local neurons For the local neurons LN the numbers were kept consistent with the published neuron names Tanaka et al. The neuron types that have been defined in the lateral horn sometimes contain slightly larger morphological varieties of neurons than would be categorized as different types in the hemibrain volume. 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.

Keywords: D. Animal brains of all sizes, from the smallest to the largest, work in broadly similar ways. Studying the brain of any one animal in depth can thus reveal the general principles behind the workings of all brains. The fruit fly Drosophila is a popular choice for such research. With about , neurons — compared to some 86 billion in humans — the fly brain is small enough to study at the level of individual cells. But it nevertheless supports a range of complex behaviors, including navigation, courtship and learning.