Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era

The era of connectomics/synaptomics ushered in the advent of large-scale availability of highly complex fruit fly brain data (Chiang et al., 2011; Berck et al., 2016; Takemura et al., 2017a; Scheffer et al., 2020), while simultaneously highlighting the dearth of computational tools with the speed and scale that can be effectively deployed to uncover the functional logic of fly brain circuits. In the early 2000’s, automation tools introduced in computational genomics significantly accelerated the pace of gene discovery from the large amounts of genomic data. Likewise, there is a need to develop tightly integrated computing tools that automate the process of 3D exploration and visualization of fruit fly brain data with the interactive exploration of executable circuits. The fruit fly brain data considered here includes neuroanatomy, genetics, and neurophysiology datasets. Due to space limitations, we mostly focus here on exploring, analyzing, comparing, and evaluating executable circuits informed by wiring diagrams derived from neuroanatomy datasets currently available in public domain.

To meet this challenge, we have built an open-source interactive computing platform called FlyBrainLab. FlyBrainLab is uniquely positioned to accelerate the discovery of the functional logic of the Drosophila brain. It is designed with three main capabilities in mind: (1) 3D exploration and visualization of fruit fly brain data, (2) creation of executable circuits directly from the explored and visualized fly brain data in step (1), and (3) interactive exploration of the functional logic of the executable circuits devised in step (2) (see Figure 1).

Figure 1

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To achieve tight integration of the three main capabilities sketched in Figure 1 into a single working environment, FlyBrainLab integrates fly brain data in the NeuroArch Database (Givon et al., 2015) and provides circuit execution with the Neurokernel Execution Engine (Givon and Lazar, 2016) (see Figure 2a). The NeuroArch Database stores neuroanatomy datasets provided by for example, FlyCircuit (Chiang et al., 2011), Larva L1EM (Ohyama et al., 2015), the Medulla 7 Column (Takemura et al., 2017a) and Hemibrain (Scheffer et al., 2020), genetics datasets published by for example, FlightLight (Jenett et al., 2012) and FlyCircuit (Chiang et al., 2011), and neurophysiology datasets including the DoOR (Münch and Galizia, 2016) and our own in vivo recordings (Lazar and Yeh, 2020; Kim et al., 2011; Kim et al., 2015). The Neurokernel Execution Engine (see Figure 2a) supports the execution of fruit fly brain circuits on GPUs. Finally, the NeuroMynerva front-end exhibits an integrated 3D graphics user interface (GUI) and provides the user a unified view of data integration and computation (see Figure 2a (top) and Figure 2b). The FlyBrainLab software architecture is depicted in the Appendix 1—figure 1.

Figure 2

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To accelerate the generation of executable circuits from fruit fly brain data, NeuroMynerva supports the following workflow.

First, the 3D GUI, called the NeuroNLP window (see Figure 2b, top middle-left), supports the visual exploration of fly brain data, including neuron morphology, synaptome, and connectome from all available data sources, stored in the NeuroArch Database (Givon et al., 2015). With plain English queries (see Figure 2b, top middle-left), a layperson can perform sophisticated database queries with only knowledge of fly brain neuroanatomy (Ukani et al., 2019).

Second, the circuit diagram GUI, called the NeuroGFX window (see Figure 2b, top middle-right) enables the interactive exploration of executable circuits stored in the NeuroArch Database. By retrieving tightly integrated biological data and executable circuit models from the NeuroArch Database, NeuroMynerva supports the interaction and interoperability between the biological circuit (or pathway for short) built for morphological visualization and the executable circuit created and represented as an interactive circuit diagram, and allows them to build on each other. This helps circuit developers to more readily identify the modeling assumptions and the relationship between neuroanatomy, neurocircuitry, and neurocomputation.

Third, the GUIs can operate in tandem with command execution in Jupyter notebooks (see also Figure 2b, bottom center). Consequently, fly brain pathways and circuit diagrams can be equivalently processed using API calls from Python, thereby ensuring the reproducibility of the exploration of similar datasets with minimal modifications. The Neurokernel Execution Engine (Givon and Lazar, 2016) provides circuit execution on multiple computing nodes/GPUs. The tight integration in the database also allows the execution engine to fetch executable circuits directly from the NeuroArch Database. The tight integration between NeuroArch and Neurokernel is reinforced and made user transparent by NeuroMynerva.

Exploration, analysis, execution, comparison, and evaluation of circuit models, either among versions developed by one’s own, or among those published in literature, are often critical steps toward discovering the functional logic of brain circuits. Six types of explorations, analyses, comparisons, and evaluations are of particular interest. First, build and explore the structure of fly brain circuits with English queries (Use Case 1). Second, explore the structure and function of yet to be discovered brain circuits (Use Case 2). Third, interactively explore executable circuit models (Use Case 3). Fourth, starting from a given dataset and after implementing a number of circuit models published in the literature, analyze and compare these under the same evaluation criteria (Use Case 4). Fifth, automate the construction of executable circuit models from datasets gathered by different labs and analyze, compare, and evaluate the different circuit realizations (Use Case 5). Sixth, analyze, compare, and evaluate fruit fly brain circuit models at different developmental stages (Use Case 6).

In what follows, we present results, supported by the FlyBrainLab Circuits Libraries (see Materials and methods), demonstrating the comprehensive exploration, execution, analysis, comparison, and evaluation capability of FlyBrainLab. While our emphasis here is on building executable circuit models informed by the connectome/synaptome of the fruit fly brain, these libraries together with sensory neuron activity data serve as entry points for an in-depth exploration, execution, analysis, comparison, and evaluation of the functional logic of the fruit fly brain.

Historically, a large number of visualization and computational tools have been developed primarily designed for either neurobiological studies (see Figure 1 (left)) or computational studies (see Figure 1 (right)). These are briefly discussed below.

The computational neuroscience community has invested a significant amount of effort in developing tools for analyzing and evaluating model neural circuits. A number of simulation engines have been developed, including general simulators such as NEURON (Hines and Carnevale, 1997), NEST (Gewaltig and Diesmann, 2007), Brian (Stimberg et al., 2019), Nengo (Bekolay et al., 2014), Neurokernel (Givon and Lazar, 2016), DynaSim (Sherfey et al., 2018), and the ones that specialize in multi-scale simulation, for example MOOSE (Ray and Bhalla, 2008), in compartmental models, for example ARBOR (Akar et al., 2019), and in fMRI-scale simulation for example The Virtual Brain (Sanz Leon et al., 2013; Melozzi et al., 2017). Other tools improve the accessibility to these simulators by (i) facilitating the creation of large-scale neural networks, for example BMTK (Dai et al., 2020a) and NetPyNE (Dura-Bernal et al., 2019), and by (ii) providing a common interface, simplifying the simulation workflow and streamlining parallelization of simulation, for example PyNN (Davison et al., 2008), Arachne (Aleksin et al., 2017), and NeuroManager (Stockton and Santamaria, 2015). To facilitate access and exchange of neurobiological data worldwide, a number of model specification standards have been worked upon in parallel including MorphML (Crook et al., 2007), NeuroML (Gleeson et al., 2010), SpineML (Tomkins et al., 2016), and SONATA (Dai et al., 2020b).

Even with the help of these computational tools, it still takes a substantial amount of manual effort to build executable circuits from real data provided, for example, by model databases such as ModelDB/NeuronDB (Hines et al., 2004) and NeuroArch (Givon et al., 2015). Moreover, with the ever expanding size of the fruit fly brain datasets, it has become more difficult to meet the demand of creating executable circuits that can be evaluated with different datasets. In addition, with very few exceptions, comparisons of circuit models, a standard process in the computer science community, are rarely available in the computational neuroscience literature.

Substantial efforts by the system neuroscience community went into developing tools for visualizing the anatomy of the brain. A number of tools have been developed to provide interactive, web-based interfaces for exploring, visualizing and analyzing fruit fly brain and ventral nerve cord datasets, for both the adult (Chiang et al., 2011; Scheffer et al., 2020) and the larva (Ohyama et al., 2015). These include the FlyCircuit (Chiang et al., 2011), the Fruit Fly Brain Observatory (FFBO/NeuroNLP) (Ukani et al., 2019), Virtual Fly Brain (Milyaev et al., 2012), neuPrintExplorer (Clements et al., 2020), FlyWire (Dorkenwald et al., 2020), and CATMAID (Saalfeld et al., 2009). Similar tools have been developed for other model organisms, such as the Allen Mouse Brain Connectivity Atlas (Oh et al., 2014), the WormAtlas for C. elegans (https://www.wormatlas.org) and the Z Brain for zebra fish (Randlett et al., 2015). A number of projects, for example (Bates et al., 2020), offer a more specialized capability for visualizing and analyzing neuroanatomy data.

While these tools have significantly improved the access to and exploration of brain data a number of recent efforts started to bridge the gap between neurobiological data and computational modeling including the Geppetto (Cantarelli et al., 2018), the OpenWorm (Szigeti et al., 2014) and the Open Source Brain (Gleeson et al., 2019) initiatives and the Brain Simulation Platform of the Human Brain Project (Einevoll et al., 2019). However, without information linking circuit activity/computation to the structure of the underlying neuronal circuits, understanding the function of brain circuits remains elusive. Lacking a systematic method of automating the process of creating and exploring the function of executable circuits at the brain or system scale levels hinders the application of these tools when composing more complex circuits. Furthermore, these tools fall short of offering the capability of generating static circuit diagrams, let alone interactive ones. The experience of VLSI design, analysis, and evaluation of computer circuits might be instructive here. An electronic circuit engineer reads a circuit diagram of a chip, rather than the 3D structure of the tape-out, to understand its function, although the latter ultimately realizes it. Similarly, visualization of a biological circuit alone, while powerful and intuitive for building a neural circuit, provides little insights into the function of the circuit. While simulations can be done without a circuit diagram, understanding how an executable circuit leads to its function remains elusive.

The tools discussed above all fall short of offering an integrated infrastructure that can effectively leverage the ever expanding neuroanatomy, genetic and neurophysiology data for creating and exploring executable fly brain circuits. Creating circuit simulations from visualized data remains a major challenge and requires extraordinary effort in practice as amply demonstrated by the Allen Brain Observatory (de Vries et al., 2020). The need to accelerate the pace of discovery of the functional logic of the brain of model organisms has entered a center stage in brain research.

FlyBrainLab is uniquely positioned to accelerate the discovery of the functional logic of the Drosophila brain. Its interactive architecture seamlessly integrates and brings together computational models with neuroanatomical, neurogenetic, and neurophysiological data, changing the organization of fruit fly brain data from a group of independently created datasets, arrays, and tables, into a well-structured data and executable circuit repository, with a simple API for accessing data in different datasets. Current data integration extensively focuses on connectomics/synaptomics datasets that, as demonstrated, strongly inform the construction of executable circuit models. We will continue to expand the capabilities of the NeuroArch database with genetic Gal4 lines (https://gene.neuronlp.fruitflybrain.org) and neurophysiology recordings including our own (http://antenna.neuronlp.fruitflybrain.org/). How to construct executable models of brain circuits using genetic and neurophysiology data sets is not the object of this publication and will be discussed elsewhere. Pointers to our initial work are given below.

As detailed here, the FlyBrainLab UI supports a highly intuitive and automated workflow that streamlines the 3D exploration and visualization of fly brain circuits, and the interactive exploration of the functional logic of executable circuits created directly from the analyzed fly brain data. In conjunction with the capability of visually constructing circuits, speeding up the process of creating interactive executable circuit diagrams can substantially reduce the exploratory development cycle.

The FlyBrainLab Utility and Circuit Libraries accelerate the creation of models of executable circuits. The Utility Libraries (detailed in the Appendix 2) help untangle the graph structure of neural circuits from raw connectome and synaptome data. The Circuit Libraries (detailed in the Appendix 3) facilitate the exploration of neural circuits of the neuropils of the central complex and, the development and implementation of models of the adult and larva fruit fly early olfactory system.

Importantly, to transcend the limitations of the connectome, FlyBrainLab is providing Circuit Libraries for molecular transduction in sensory coding (detailed in the Appendix 3), including models of sensory transduction and neuron activity data (Lazar et al., 2015a; Lazar et al., 2015b; Lazar and Yeh, 2020). These libraries serve as entry points for discovery of circuit function in the sensory systems of the fruit fly (Lazar and Yeh, 2019; Lazar et al., 2020a). They also enable the biological validation of developed executable circuits within the same platform.

The modular software architecture underlying FlyBrainLab provides substantial flexibility and scalability for the study of the larva and adult fruit fly brain. As more data becomes available, we envision that the entire central nervous system of the fruit fly can be readily explored with FlyBrainLab. Furthermore, the core of the software and the workflow enabled by the FlyBrainLab for accelerating discovery of Drosophila brain functions can be adapted in the near term to other model organisms including the zebrafish and bee.

The FlyBrainLab interactive computing platform tightly integrates tools enabling the morphological visualization and exploration of large connectomics/synaptomics datasets, interactive circuit construction and visualization and multi-GPU execution of neural circuit models for in silico experimentation. The tight integration is achieved with a comprehensive open software architecture and libraries to aid data analysis, creation of executable circuits and exploration of their functional logic.

# query for LPTC

res1 = client.executeNLPquery("show LPTC")

# color the neurons in the previous query
_ = client.executeNLPquery('color orange')

# query for GLN on the right hemisphere using regular expression 
res2 = client.executeNLPquery("add /rGLN(.*)R(.*)/r")

# color the neurons in the previous query
_ = client.executeNLPquery("color cyan") 

# get the unique names of the GLNs
gln = [v['uname'] for v in res2.neurons.values()]

# query using NeuroArch JSON format 
task = {"query": [
          {"action": {"method": {"path_to_neurons": {
              "pass_through": gln,
              "max_hops": 2,
              "synapse_threshold": 5
          }}},
          "object": {"state": 1}}],
        "format": "morphology",
        "verb": "add"
}

res3 = client.executeNAquery(task)
_ = client.executeNLPquery("color red")

Use Case 4: analyzing, evaluating, and comparing circuit models of the fruit fly central complex

Request a detailed protocol

Model A (Givon et al., 2017; Figure 6a), Model B (Kakaria and de Bivort, 2017; Figure 6b) and Model C (Su et al., 2017; Figure 6c) were implemented using the CXcircuits Library (see also Appendix 3). A wild-type fruit fly CX circuit diagram based on model A was created in the SVG format and made interactive in the NeuroGFX window. The neurons modeled in the circuit diagram were obtained by querying all the neurons of the CX neuropils in the FlyCircuit dataset. The innervation pattern of each neuron was obtained from Lin et al., 2013 and visually examined in the NeuroNLP window. Based on the assumptions made for each model, a standard name was assigned to the model of the neuron according to the naming scheme adopted in the CXcircuits Library. The neurons with missing morphologies in the FlyCircuit dataset were augmented with the data available in the literature (Wolff et al., 2015; Lin et al., 2013).

This all encompassing circuit diagram was then adapted to the other models. Since the assumptions about the subregions that neurons receive inputs from and output to are different in each circuit model, slightly different names may be assigned to neurons in different circuit models. The complete list of modeled neurons of the three models are provided in Supplementary file 1 ‘CX_models.xlsx’, along with their corresponding neurons in the FlyCircuit dataset. The CXcircuits Library also uses this list to enable synchronization of the neurons visualized in the NeuroNLP window with the neurons represented in the NeuroGFX window.

All three models include the same core subcircuits for modeling the Protocerebral Bridge - Ellipsoid Body (PB-EB) interaction. The core subcircuits include three cell types, namely, the PB-EB-LAL, PB-EB-NO, and EB-LAL-PB neurons (NO - Noduli, LAL - Lateral Accessory Lobe, see also Givon et al., 2017 for a list of synonyms of each neuron class). These cells innervate three neuropils, either PB, EB and LAL or PB, EB, and NO. Note that only synapses within PB and EB are considered. For model A, this is achieved by removing all neurons that do not belong to the core PB-EB circuit. This can be directly performed on the circuit diagram in the NeuroGFX window or by using the CXcircuits API. Model B includes an additional cell type, the PB local neurons that introduce global inhibition to the PB-EB circuit. Model C does not include PB local neurons, but models 3 types of ring neurons that innervate the EB. Both PB local neurons and ring neurons are present in model A. However, except for their receptive fields, all ring neurons in model A are the same (see below). Figure 9 depicts the correspondence established between the morphology of example neurons and their respective representation in the overall circuit diagram.

Figure 9

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In Model C, the subcircuit consisting of the PB-EB-LAL and EB-LAL-PB neurons was claimed to give rise to the persistent bump activity while the interconnect between PB-EB-NO and EB-LAL-PB allowed the bump to shift in darkness. To better compare with the other two models that did not model the shift in darkness, PB-EB-NO neurons were interactively disabled from the diagram.

For a single vertical bar presented to the fly at the position shown in Figure 6 (d1) (see also the flattened visual input in Figure 6 (d2), the PB glomeruli or the EB wedges innervated by neurons of each of the three circuit models that receive injected current or external spike inputs are, respectively, highlighted in Figure 6 (a3, b3, c3). The CXcircuit Library generates a set of visual stimuli and computes the spike train and/or the injected current inputs to each of the three models.

For model A (Givon et al., 2017), each PB glomerulus is endowed with a rectangular receptive field that covers 20° in azimuth and the entire elevation. Together, the receptive fields of all PB glomeruli tile the 360° azimuth. All PB neurons with dendrites in a glomerulus, including the PB-EB-LAL and PB-EB-NO neurons, receive the visual stimulus filtered by the receptive field as an injected current. Additionally, each Bulb (BU) microglomerulus is endowed with a Gaussian receptive field with a standard deviation of 9° in both azimuth and elevation. The ring neuron innervating a microglomerulus receives the filtered visual stimulus as an injected current (see also the arrows in Figure 6 (a3)). Neuron dynamics follow the Leaky Integrate-and-Fire (LIF) neuron model

(1) $C^i\frac{d{V}^i}{dt}=-\frac{V^i-V_0^i}{R^i}+I^i,$

where $V^i$ is the membrane voltage of the $i$ th neuron, $C^i$ is the membrane capacitance, $V_0^i$ is the resting potential, $R^i$ is the resistance, and $I^i$ is the synaptic current. Upon reaching the threshold voltage $V_{th}^i$, each neuron’s, membrane voltage is reset to $V_r^i$. Synapses are modeled as $\alpha$-synapses with dynamics given by the differential equations

(2) ${\displaystyle \begin{array}{ll}{\displaystyle g^i(t)}& {\displaystyle ={\overline{g}}^i{s}^i(t)}\\ {\displaystyle \frac{d{s}^i}{dt}(t)}& {\displaystyle =h^i(t)1_{[t\ge 0]}(t)}\\ {\displaystyle \frac{d{h}^i}{dt}(t)}& {\displaystyle =-(a_r^i+a_d^i)h(t)-a_r^i{a}_d^i{s}^i+a_r^i{a}_d^i\sum _k\delta (t-t_k^i),}\end{array}}$

where ${\overline{g}}^i$ is a scaling factor, $a_r^i$ and $a_d^i$ are, respectively, the rise and decay time of the synapse, $1_{[t\ge 0]}(t)$ is the Heaviside function and $\delta (t)$ is the Dirac function. $\delta (t-t_k^i)$ indicates an input spike to the $i$ th synapse at time $t_k^i$.

For Model B (Kakaria and de Bivort, 2017), the receptive field of each of the 16 EB wedges covers 22.5° in azimuth. All EB-LAL-PB neurons that innervate a wedge receive a spike train input whose rate is proportional to the filtered visual stimulus (see also arrow in Figure 6 (b3)). The maximum input spike rate is 120 Hz when the visual stimulus is a bar of width 20° at maximum brightness 1. A 5 Hz background firing is always added even in darkness. Neurons are modeled as LIF with a refractory period of 2.2 ms as suggested in Kakaria and de Bivort, 2017. For synapses, instead of using the postsynaptic current (PSC)-based model described in Kakaria and de Bivort, 2017, the $\alpha$-synapse described above was used and its parameters were chosen such that the time-to-peak and peak value approximately matched that of the PSC-based synapse.

For Model C (Su et al., 2017), the receptive field of each of the 16 EB wedges covers 22.5° in azimuth. Two PB-EB-LAL neurons project to a wedge each from a different PB glomerulus. Input to the Model C circuit is presented to pairs of PB glomeruli (see also arrows in Figure 6 (c3)), and all neurons with dendrites in these two PB glomeruli receive a spike train input at a rate proportional to the filtered visual stimuli, with a maximum 50 Hz when the bar is at maximum brightness 1. Neurons are modeled as LIF neurons with a refractory period of 2 ms (as suggested in Su et al., 2017). Synapses are either modeled by the AMPA/GABAA receptor dynamics as

(3) ${\displaystyle \begin{array}{ll}{\displaystyle g^i(t)}& {\displaystyle ={\overline{g}}^i{s}^i(t)}\\ {\displaystyle \frac{d{s}^i}{dt}(t)}& {\displaystyle =-\frac{s^i(t)}{{\tau }^i}+\sum _k\delta (t-t_k^i),}\end{array}}$

where $g^i(t)$ is the synaptic conductance, ${\tau }^i$ is the time constant, and $s^i(t)$ is the state variable of the $i$ th synapse, or modeled by the NMDA receptor dynamics (Su et al., 2017)

(4) ${\displaystyle \begin{array}{rl}{\displaystyle g^i(t)}& {\displaystyle =\frac{{\overline{g}}^i{s}^i(t)}{1+[M{g}^{2+}{]}^i{e}^{-\frac{0.062V}{3.56}}}}\\ {\displaystyle \frac{d{s}^i}{dt}(t)}& {\displaystyle =-\frac{s^i(t)}{{\tau }^i}+{\alpha }^i\left(1-s^i(t)\right)\sum _k\delta (t-t_k^i),}\end{array}}$

where $g^i(t)$ is the synaptic conductance, ${\overline{g}}^i$ is the maximum conductance, $s^i(t)$ is the state variable, ${\tau }^i$ is the time constant, ${\alpha }^i>0$ is a constant, ${[M{g}^{2+}]}^i$ is the extracellular concentration of $M{g}^{2+}$, respectively, of the $i$ th synapse and $V$ is the membrane voltage of the postsynaptic neuron.

Parameters of the above models can be found in Givon et al., 2017; Kakaria and de Bivort, 2017; Su et al., 2017 and in the CXcircuit Library.

Commonly used models, such as the LIF neuron and the α-synapse, are built-into the Neurokernel Execution Engine. Only the model parameters residing in the NeuroArch Database need to be specified via NeuroArch API. The Neurokernel automatically retrieves the circuit models and their parameters from the NeuroArch Database based on the last queried circuit model. For models that are not yet built-into the Neurokernel Execution Engine, such as the PSC-based model in Model B, users must provide an implementation supported by the Neurodriver API.

The 35 s visual stimulus, depicted in Figure 6 (d3), was presented to all three models. A bright vertical bar moves back and forth across the entire visual field while a second bar with lower brightness is presented at a fixed position. Figure 6 (d3) (bottom) depicts the time evolution of the visual input.

To visualize the response of the three executable circuits, the mean firing rate $r^j(t)$ of all EB-LAL-PB neurons that innervate the $j$ th EB wedge was calculated following Su et al., 2017.

(5) ${\displaystyle r^j(t)=\frac{1}{N^j}\sum _{i\in {\mathbb{I}}^j}\left(\sum _k\delta (t-t_k^i)\ast e^{-\frac{t}{0.7215}}\right),}$

where ∗ denotes the convolution operator, ${?}^j$ is the index set of EB-LAL-PB neurons that innervate the $j$ th EB wedge, whose cardinality is $N^j$, and $t_k^i$ is the time of kth spike generated by ith neuron. CXcircuit Library provides utilities to visualize the circuit response.

Jupyter notebooks for Models A, B and C used to generate Video 1 are available at https://github.com/FlyBrainLab/CXcircuits/tree/master/notebooks/elife20.

General information about the FlyBrainLab is available at https://www.fruitflybrain.org. Stable and tested FlyBrainLab installation instructions are available at https://github.com/FlyBrainLab/FlyBrainLab. An overview of the FlyBrainLab resources can be found at the FlyBrainLab Resource wiki page at https://github.com/FlyBrainLab/FlyBrainLab/wiki/FlyBrainLab-Resources. It includes links to individual code repositories for components, libraries and tutorials. The NeuroArch Database hosting publicly available FlyCircuit, Hemibrain, Medulla 7-column and Larva L1EM datasets can be downloaded from https://github.com/FlyBrainLab/datasets. The same repository provides Jupyter notebooks for loading publicly available datasets, such as the FlyCircuit dataset with inferred connectivity, the Hemibrain dataset, the Medulla 7-column dataset and the Larva L1 EM dataset.

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Author details

1. Aurel A Lazar

   Department of Electrical Engineering, Columbia University, New York, United States

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing, Conceived the study and FlyBrainLab software architecture. Developed comparative models of the central complex. Developed comparative models of the early olfactory system.

   For correspondence

   aurel@ee.columbia.edu

   Competing interests

   No competing interests declared

   Additional information

   The authors’ names are listed in alphabetical order.

   "This ORCID iD identifies the author of this article:" 0000-0003-4261-8709

2. Tingkai Liu

   Department of Electrical Engineering, Columbia University, New York, United States

   Contribution

   Conceptualization, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing, Conceived the study and FlyBrainLab software architecture. Developed the FlyBrainLab platform. Developed user-side libraries. Developed comparative models of the early olfactory system.

   Competing interests

   No competing interests declared

   Additional information

   The authors’ names are listed in alphabetical order.

   "This ORCID iD identifies the author of this article:" 0000-0003-3075-7648

3. Mehmet Kerem Turkcan

   Department of Electrical Engineering, Columbia University, New York, United States

   Contribution

   Conceptualization, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing, Conceived the study and FlyBrainLab software architecture. Developed the FlyBrainLab platform. Developed user-side libraries and utility libraries. Developed comparative models of the central complex. Developed comparative models of the early olfactory system.

   Competing interests

   No competing interests declared

   Additional information

   The authors’ names are listed in alphabetical order.

   "This ORCID iD identifies the author of this article:" 0000-0001-9273-7293

4. Yiyin Zhou

   Department of Electrical Engineering, Columbia University, New York, United States

   Contribution

   Conceptualization, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing, Conceived the study and FlyBrainLab software architecture. Developed the FlyBrainLab platform. Updated the server-side components of the existing FFBO architecture. Developed comparative models of the central complex.

   Competing interests

   No competing interests declared

   Additional information

   The authors’ names are listed in alphabetical order.

   "This ORCID iD identifies the author of this article:" 0000-0003-4618-4039

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