Biology subjects

Lazar, A. A.

Publications and source records attributed to Lazar, A. A..

15 recordsLinked to original sources

bioRxiv2026

NeuroGraphBench: Interacting with Drosophila Connectomes at Scale for Exploring the Functional Logic of Neural Circuits

Drosophila connectomic datasets provide increasingly comprehensive maps of neuronal morphology and synaptic connectivity, offering an unprecedented opportunity to explore the structural organization of its neural circuits. This calls for designing automated tools to interact with connectomic datasets at scale for efficiently exploring structural features embedded in the vast amount of data. Yet the central challenge remains the understanding of the functional logic of neural circuits. In order to understand how elements of the functional logic may emerge from this structural organization, it is critical to (i) characterize the objects in the natural environment in which brain circuits operate, and (ii) formulate how brain circuits represent and process the defined objects in the natural environment. To develop and demonstrate a methodology for these requirements, we focus on the Drosophila looming-evoked escape pathway. We modeled the trajectory of looming objects that are on a collision course (direct-hits) or pass-by the fly (near-misses): their projected images on the retina can be characterized by the solid angle (angular size) and elevation. We then analyzed the pathway's morphology across the OpticLobe, Hemibrain, and FlyWire connectome datasets. By abstracting their sub-neuronal structure and retinotopic organization, we constructed an executable circuit model that maps each structural element to a processing block. We demonstrate that this model separates direct hits from near misses well before the angular size could tell them apart. To accelerate the connectomic analysis step, we developed a Python toolset with an agentic, code-free workspace interface called NeuroGraphBench (NGB). NGB provides four composable morphology-analysis primitives and an AI agent that composes them to interactively respond to natural-language queries aided by visualization on an interactive 3D canvas. Thus, NGB automates tedious and repetitive tasks to enable faster and scalable connectomic exploration, keeping human reasoning, instead of writing code, at the center of an open-ended research inquiry.

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bioRxiv2026

Elements of Olfactory Intelligence in Drosophila

The ability to make the world of odorants intelligible is a key capability of the Drosophila olfactory system that we shall call olfactory intelligence. Characterizing the functional logic of the Drosophila early olfactory system that makes the natural world of odorants intelligible is a major challenge in neuroscience. Starting by modeling the space of odorants using constructs of both semantic and syntactic information, we establish that the Antenna Lobe and Mushroom Body Calyx first decompose the confounding representation of the Antenna into a concentration independent odorant semantics information and the ON-OFF timing of the syntactic information. Subsequently the two streams of information are integrated and produce a novel time and rank-based representation of Kenyon Cell outputs, called the marked first spike sequence code. In conjunction with a novel distance measure for the spike sequence code, we demonstrate that the rank-based representation supports accurate classification of ON-OFF odorant semantics. Computationally, these elements of olfactory intelligence are realized by a class of differential divisive normalization processors modeling the feedback circuits in a causal chain of stages including the Antenna, Antennal Lobe and the Mushroom Body Calyx. Consequently, the early olfactory system makes the natural world of odorants intelligible.

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bioRxiv2025

Synaptic high-frequency jumping synchronises vision to high-speed behaviour

During high-speed behaviour, animals must synchronise perception and action despite rapid environmental and self-generated motion. How neural systems achieve such precision remains unclear. Here we show how the housefly (Musca domestica) maintains visual accuracy during fast motion. Using intracellular and photomechanical recordings during saccade-like stimulation, we traced information flow from photoreceptors to large monopolar cells (LMCs). Visual neurons achieved record-high information sampling ([~]2,500 bits{middle dot}s-1) and synaptic transmission ([~]4,100 bits{middle dot}s-1), far exceeding previous estimates. We identify a previously unknown mechanism - synaptic high-frequency jumping - in which photoreceptor-LMC synapses dynamically shift transmission toward higher frequencies during saccades, extending visual bandwidth to [~]1,000 Hz, effectively eliminating synaptic delays, and quadrupling classical flicker-fusion limits ([~]230 Hz). Behavioural experiments show flies respond synchronously within [~]13-20 ms, even before photoreceptor responses peak. A biophysically realistic model reveals how photomechanical-stochastic-refractory quantal sampling co-adapts with saccadic behaviour: through self-motion, flies efficiently translate image motion into temporally-precise, predictive high-speed vision.

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bioRxiv2022

Odorant Mixture Separation in Drosophila Early Olfactory System

Natural odorant scenes are complex landscapes comprising mixtures of volatile compounds. It was previously proposed that the Antennal Lobe circuit recovers the odorant identity in a concentration-invariant manner via divisive normalization of Local Neurons. It remains unclear, however, how identities of odorant components in a mixture is represented or recovered in the fruit fly early olfactory pathway. In the current work, we take a different approach from the traditional steady-state analyses that classify odorant mixture encoding into configural vs. elemental schemes. Instead, we focus on the spatio-temporal responses of the early olfactory pathway at the levels of the Antennal Lobe and the Mushroom Body, and formulate the odorant demixing problem as a blind source separation problem - where the identities of each individual odorant component and their corresponding concentration waveforms are recovered from the spatio-temporal PSTH of Olfactory Sensory Neurons (OSNs), Projection Neurons (PNs), and Kenyon Cells (KCs) respectively. Building upon previous models of the Antenna and the Antennal Lobe, we advanced a feedback divisive normalization architecture of the Mushroom Body Calyx circuit comprised of PN, KC and the giant Anterior Paired Lateral (APL) neuron. We demonstrate that the PN-KC-APL circuit produces a high dimensional representation of odorant mixture with robust sparsity, and results in greater odorant demixing performance than the PN responses.

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bioRxiv2022

Divisive Normalization Circuits Faithfully Represent Auditory and Visual Stimuli

Divisive normalization is a canonical neural computation employed for sensory adaptation in vision, olfaction and attention modulation. While Divisive Normalization has been proposed to be an efficient coding algorithm, it remains unclear whether such transformation results in information loss in a dynamic setting. Leveraging a previously proposed general mathematical framework called the Divisive Normalization Processor (DNP), we first show that the DNP circuit describes a wide class of neural circuits as well as phenomenological models including the Linear-Nonlinear cascade model. We then demonstrate both theoretically and computationally that the DNP is an invertible operator that could faithfully represent input information given sufficient output samples.

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bioRxiv2022

A Programmable Model for Exploring the Functional Logic of the Drosophila Antennal Lobe

Recent progress on connectomics has resulted in huge datasets of brain structures in single-synapse scale. This calls for the modeling of executable circuits to discover the functional logic of these neural circuits in different scales. To provide an approach to standardize analyses of neuropils with multiple-input-multi-output channels, we put forward a programmable model focusing on the antennal lobe (AL), a circuit at the periphery of the olfactory system with nonlinear computation. We present an approach for constructing and analyzing antennal lobe circuits using models of glomeruli and local feedback loops. We establish the composability of the connectivity of glomeruli with local neuron feedback loops by combining pairs of glomeruli, and then by combining all glomeruli together to characterize the I/O of the entire AL. We thus provide a methodology for the quantitative characterization of the I/O of the AL as a function of feedback loop motifs.

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bioRxiv2022

A Programmable Model for Exploring the Functional Logic of the Drosophila Mushroom Body

We advance a model of the Mushroom Body of the Drosophila brain based on the recently published fruit fly brain connectome. We quantify the effect of the connectivity from antennal lobe projection neurons to Kenyon Cells (KCs) and the effect of feedback between KCs and the anterior paired lateral (APL) neuron on the representation of odorants at the KC level. We then characterize odorant representation in the mushroom body output neurons (MBONs) as a function of semantics represented by the dopaminergic neurons (DANs). Finally, we evaluate the performance of associative learning in the MB that endows pure odorants with DAN semantics.

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bioRxiv2022

Connectomic Analysis of the Drosophila Lateral Neuron Clock Cells Reveals the Synaptic Basis of Functional Pacemaker Classes.

The circadian clock orchestrates daily changes in physiology and behavior to ensure internal temporal order and optimal timing across the day. In animals, a central brain clock coordinates circadian rhythms throughout the body and is characterized by a remarkable robustness that depends on synaptic connections between constituent neurons. The clock neuron network of Drosophila, which shares network motifs with clock networks in the mammalian brain yet is built of many fewer neurons, offers a powerful model for understanding the network properties of circadian timekeeping. Here we report an assessment of synaptic connectivity within a clock network, focusing on the critical lateral neuron (LN) clock neuron classes. Our results reveal that previously identified anatomical and functional subclasses of LNs represent distinct connectomic types. Moreover, we identify a small number of clock cell subtypes representing highly synaptically coupled nodes within the clock neuron network. This suggests that neurons lacking molecular timekeeping likely play integral roles within the circadian timekeeping network. To our knowledge, this represents the first comprehensive connectomic analysis of a circadian neuronal network.

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bioRxiv2021

A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain

The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice and humans. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the function of fruit fly brain circuits at this scale posits significant challenges. To address these challenges, we present here a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain. The programmable ontology provides a language not only for modeling circuit motifs but also for programmatically exploring their functional logic. To achieve this goal, we tightly integrated the programmable ontology with the workflow of the interactive FlyBrainLab computing platform. As part of the programmable ontology, we developed NeuroNLP++, a web application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets, and the published worldwide literature. In addition, we present a methodology for including a model of the space of odorants into the programmable ontology, and for modeling olfactory sensory circuits of the antenna of the fruit fly brain that detect odorant sources. Furthermore, we describe a methodology for modeling the functional logic of the antennal lobe circuit consisting of massive local feedback loops, a characteristic feature observed across Drosophila brain regions. Finally, using a circuit library, we demonstrate the power of our methodology for interactively exploring the functional logic of the massive number of feedback loops in the antennal lobe.

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bioRxiv2021

The Functional Logic of Odor Information Processing in the Drosophila Antennal Lobe

The early olfactory system of the fruit fly, while sensing a complex odorant landscape, encodes the odorant object identity (semantic information) and the odorant concentration waveform (syntactic information) into a combinatorial neural code. Single-channel physiology recordings at the output of the Antenna Lobe (AL) exhibit concentration-invariance and contrast-boosting properties, indicating a decoupling of the odorant object identity from the concentration waveform in steady-state while responding strongly to odorant concentration onset and offset in transient states. Through exhaustive computational explorations of the AL circuits, we show that the steady-state and transient response features of the AL are, respectively, due to presynaptic and postsynaptic Local Neurons (LNs). Theoretical analysis reveals that the LN pathways can be modeled as parallel differential Divisive Normalization Processors (DNPs). Differential DNPs robustly extract odorant identity (semantic information) and ON/OFF odorant event-timing (syntactic information), thereby providing for the AL the functional logic of ON-OFF odorant identity recovery.

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bioRxiv2020

FlyBrainLab: Accelerating the Discovery of the Functional Logic of the Fruit Fly Brain in the Connectomic/Synaptomic Era

In recent years, a wealth of Drosophila neuroscience data have become available. These include cell type, connectome and synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fly brain, we developed an interactive computing environment called FlyBrainLab. FlyBrainLab is uniquely positioned to accelerate the discovery of the functional logic of the Drosophila brain. Its interactive open source architecture seamlessly integrates and brings together computational models with neuroanatomical, neurogenetic and electrophysiological data, changing the organization of neuroscientific fly brain data from a group of seemingly disparate databases, arrays and tables, to a well structured data and executable circuit repository. The FlyBrainLab User Interface supports a highly intuitive and automated work-flow 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 explored and visualized fly brain data. Furthermore, efficient comparisons of circuit models are supported, across models developed by different researchers, across different developmental stages of the fruit fly and across different datasets. The FlyBrainLab Utility Libraries help untangle the graph structure of neural circuits from raw connectome and synaptome data. The Circuit Libraries 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 systems. Seeking to transcend the limitations of the connectome, FlyBrainLab provides additional libraries for molecular transduction arising in sensory coding in vision and olfaction. Together with sensory neuron activity data, these libraries serve as entry points for discovering circuit function in the sensory systems of the fruit fly brain. They also enable the biological validation of developed executable circuits within the same platform.

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bioRxiv2019

The Fruit Fly Brain Observatory: From Structure to Function

The fruit fly is a key model organism for studying the activity of interconnected brain circuits. A large scattered global research community of neurobiologists and neurogeneticists, computational and theoretical neuroscientists, and computer scientists and engineers has been developing a vast trove of experimental and modeling data that has yet to be distilled into new knowledge and understanding of the functional logic of the brain. Developing open shared models, modelling tools and data repositories that can be accessed from anywhere in the world is the necessary engine for accelerating our understanding of how the brain works.\n\nTo that end we developed the Fruit Fly Brain Observatory (FFBO), the next generation open-source platform to support open, collaborative Drosophila neuroscience research. FFBO provides a (i) hub for storing and integrating fruit fly brain research data from multiple data sources worldwide, (ii) unified repository of tools and methods to build, emulate and compare fruit fly brain models in health and disease, and (iii) an open framework for fruit fly brain data processing and model execution. FFBO provides access to application tools for visualizing, configuring, simulating and analyzing computational models of brain circuits of the (i) cell type map, (ii) connectome, (iii) synaptome, and (iv) activity map using intuitive queries in plain English. Tools are provided to extract the function inherent in these structural maps. All applications can be accessed with any modern browser.

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bioRxiv2016

The Fruit Fly Brain Observatory: from structure to function

The Fruit Fly Brain Observatory (FFBO) is a collaborative effort between experimentalists, theorists and computational neuroscientists at Columbia University, National Tsing Hua University and Sheffield University with the goal to (i) create an open platform for the emulation and biological validation of fruit fly brain models in health and disease, (ii) standardize tools and methods for graphical rendering, representation and manipulation of brain circuits, (iii) standardize tools for representation of fruit fly brain data and its abstractions and support for natural language queries, (iv) create a focus for the neuroscience community with interests in the fruit fly brain and encourage the sharing of fruit fly brain structural data and executable code worldwide. NeuroNLP and NeuroGFX, two key FFBO applications, aim to address two major challenges, respectively: i) seamlessly integrate structural and genetic data from multiple sources that can be intuitively queried, effectively visualized and extensively manipulated, ii) devise executable brain circuit models anchored in structural data for understanding and developing novel hypotheses about brain function. NeuroNLP enables researchers to use plain English (or other languages) to probe biological data that are integrated into a novel database system, called NeuroArch, that we developed for integrating biological and abstract data models of the fruit fly brain. With powerful 3D graphical visualization, NeuroNLP presents a highly accessible portal for the fruit fly brain data. NeuroGFX provides users highly intuitive tools to execute neural circuit models with Neurokernel, an open-source platform for emulating the fruit fly brain, with full data support from the NeuroArch database and visualization support from an interactive graphical interface. Brain circuits can be configured with high flexibility and investigated on multiple levels, e.g., whole brain, neuropil, and local circuit levels. The FFBO is publicly available and accessible at http://fruitflybrain.org from any modern web browsers, including those running on smartphones.

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bioRxiv2016

NeuroNLP: a natural language portal for aggregated fruit fly brain data

NeuroNLP, is a key application on the Fruit Fly Brain Observatory platform (FFBO, http://fruitflybrain.org), that provides a modern web-based portal for navigating fruit fly brain circuit data. Increases in the availability and scale of fruit fly connectome data, demand new, scalable and accessible methods to facilitate investigation into the functions of the latest complex circuits being uncovered. NeuroNLP enables in-depth exploration and investigation of the structure of brain circuits, using intuitive natural language queries that are capable of revealing the latent structure and information, obscured due to expansive yet independent data sources. NeuroNLP is built on top of a database system call NeuroArch that codifies knowledge about the fruit fly brain circuits, spanning multiple sources. Users can probe biological circuits in the NeuroArch database with plain English queries, such as \"show glutamatergic local neurons in the left antennal lobe\" and \"show neurons with dendrites in the left mushroom body and axons in the fan-shaped body\". This simple yet powerful interface replaces the usual, cumbersome checkboxes and dropdown menus prevalent in todays neurobiological databases. Equipped with powerful 3D visualization, NeuroNLP standardizes tools and methods for graphical rendering, representation, and manipulation of brain circuits, while integrating with existing databases such as the FlyCircuit. The userfriendly graphical user interface complements the natural language queries with additional controls for exploring the connectivity of neurons and neural circuits. Designed with an open-source, modular structure, it is highly scalable/flexible/extensible to additional databases or to switch between databases and supports the creation of additional parsers for other languages. By supporting access through a web browser from any modern laptop or smartphone, NeuroNLP significantly increases the accessibility of fruit fly brain data and improves the impact of the data in both scientific and educational exploration.

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bioRxiv2016

NeuroGFX: a graphical functional explorer for fruit fly brain circuits

Recently, multiple focused efforts have resulted in substantial increase in the availability of connectome data in the fruit fly brain. Elucidating neural circuit function from such structural data calls for a scalable computational modeling methodology. We propose such a methodology that includes i) a brain emulation engine, with an architecture that can tackle the complexity of whole brain modeling, ii) a database that supports tight integration of biological and modeling data along with support for domain specific queries and circuit transformations, and iii) a graphical interface that allows for total flexibility in configuring neural circuits and visualizing run-time results, both anchored on model abstractions closely reflecting biological structure. Towards the realization of such a methodology, we have developed NeuroGFX and integrated it into the architecture of the Fruit Fly Brain Observatory (http://fruitflybrain.org). The computational infrastructure in NeuroGFX is provided by Neurokernel, an open source platform for the emulation of the fruit fly brain, and NeuroArch, a database for querying and executing fruit fly brain circuits. The integration of the two enables the algorithmic construction/manipulation/revision of executable circuits on multiple levels of abstraction of the same model organism. The power of this computational infrastructure can be leveraged through an intuitive graphical interface that allows visualizing execution results in the context of biological structure. This provides an environment where computational researchers can present configurable, executable neural circuits, and experimental scientists can easily explore circuit structure and function ultimately leading to biological validation. With these capabilities, NeuroGFX enables the exploration of function from circuit structure at whole brain, neuropil, and local circuit level of abstraction. By allowing for independently developed models to be integrated at the architectural level, NeuroGFX provides an open plug and play, collaborative environment for whole brain computational modeling of the fruit fly.

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