Biology subjects

Osman, M. A. M.

Publications and source records attributed to Osman, M. A. M..

6 recordsLinked to original sources

bioRxiv2026

A network model of path control

We present a network model that enables a navigating agent to shape the spatial and temporal structure of its paths through its environment. The model is inspired by the insect fan-shaped body. In it, a recurrent neural network receives an input that encodes an agent's velocity vector. Meanwhile, network activity determines the setpoint of a control loop that influences this velocity vector through motor outputs. The network thus guides future movement using memory of past movement. We illustrate how the network's dynamics determine the trajectory features it extracts to produce the setpoint. We then relate the joint dynamics of network activity and the agent's velocity vector to the structure of the resulting paths. This network can produce paths suitable for different forms of exploration. It can also stabilize movements initiated by lower-level systems. Our analyses illustrate how recurrent neural dynamics and memory can shape behavior when embedded in closed loops.

neuroscience↗

bioRxiv2025

Distributed control circuits across a brain-and-cord connectome

Just as genomes revolutionized molecular genetics, connectomes (maps of neurons and synapses) are transforming neuroscience. To date, the only species with complete connectomes are worms1-3 and sea squirts4 (103-104 synapses). By contrast, the fruit fly is more complex (108 synaptic connections), with a brain that supports learning and spatial memory5,6 and an intricate ventral nerve cord analogous to the vertebrate spinal cord7-11. Here we report the first densely reconstructed adult fly connectome that unites the brain and ventral nerve cord, and we leverage this resource to investigate principles of neural control. We show that effector neurons (motor neurons, endocrine cells and efferent neurons targeting the viscera) are primarily influenced by sensory neurons in the same body part, forming local feedback loops. These local loops are linked by long-range circuits involving ascending and descending neurons organized into behavior-centric modules. Single ascending and descending neurons are often positioned to influence the voluntary movements of multiple body parts, together with the endocrine cells or visceral organs that support those movements. Brain regions involved in learning and navigation supervise these circuits. These results reveal an architecture that is distributed, parallelized and embodied, reminiscent of distributed control architectures in engineered systems12,13.

neuroscience↗

bioRxiv2024

A Hopfield network model of neuromodulatory arousal state

Neural circuits display both input-driven activity that is necessary for the real-time control of behavior and internally generated activity that is necessary for memory, planning, and other cognitive processes. A key mediator between these intrinsic and evoked dynamics is arousal, an internal state variable that determines an animals level of engagement with its environment. It has been hypothesized that arousal state acts through neuromodulatory gain control mechanisms that suppress recurrent connectivity and amplify bottom-up input. In this paper, we instantiate this longstanding idea in a continuous Hopfield network embellished with a gain parameter that mimics arousal state by suppressing recurrent interactions between the networks units. We show that dynamics capturing some essential effects of arousal state at the neural and cognitive levels emerge in this simple model as a single parameter--recurrent gain--is varied. Using the models formal connections to the Boltzmann machine and the Ising model, we offer functional interpretations of arousal state rooted in Bayesian inference and statistical physics. Finally, we liken the dynamics of neuromodulator release to an annealing schedule that facilitates adaptive behavior in ever-changing environments. In summary, we present a minimal neural network model of arousal state that exhibits rich but analytically tractable emergent behavior and reveals conceptually clarifying parallels between arousal state and seemingly unrelated phenomena.

neuroscience↗

bioRxiv2023

Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics

Keypoint tracking algorithms have revolutionized the analysis of animal behavior, enabling investigators to flexibly quantify behavioral dynamics from conventional video recordings obtained in a wide variety of settings. However, it remains unclear how to parse continuous keypoint data into the modules out of which behavior is organized. This challenge is particularly acute because keypoint data is susceptible to high frequency jitter that clustering algorithms can mistake for transitions between behavioral modules. Here we present keypoint-MoSeq, a machine learning-based platform for identifying behavioral modules ("syllables") from keypoint data without human supervision. Keypoint-MoSeq uses a generative model to distinguish keypoint noise from behavior, enabling it to effectively identify syllables whose boundaries correspond to natural sub-second discontinuities inherent to mouse behavior. Keypoint-MoSeq outperforms commonly used alternative clustering methods at identifying these transitions, at capturing correlations between neural activity and behavior, and at classifying either solitary or social behaviors in accordance with human annotations. Keypoint-MoSeq therefore renders behavioral syllables and grammar accessible to the many researchers who use standard video to capture animal behavior.

neuroscience↗

bioRxiv2023

ArMo: An Articulated Mesh Approach for Mouse 3D Reconstruction

Characterizing animal behavior requires methods to distill 3D movements from video data. Though keypoint tracking has emerged as a widely used solution to this problem, it only provides a limited view of pose, reducing the body of an animal to a sparse set of experimenter-defined points. To more completely capture 3D pose, recent studies have fit 3D mesh models to subjects in image and video data. However, despite the importance of mice as a model organism in neuroscience research, these methods have not been applied to the 3D reconstruction of mouse behavior. Here, we present ArMo, an articulated mesh model of the laboratory mouse, and demonstrate its application to multi-camera recordings of head-fixed mice running on a spherical treadmill. Using an end-to-end gradient based optimization procedure, we fit the shape and pose of a dense 3D mouse model to data-derived keypoint and point cloud observations. The resulting reconstructions capture the shape of the animals surface while compactly summarizing its movements as a time series of 3D skeletal joint angles. ArMo therefore provides a novel alternative to the sparse representations of pose more commonly used in neuroscience research.

animal behavior and cognition↗

bioRxiv2020

Association of lanthipeptide genes with TnpAREP transposases in marine picocyanobacteria

Lanthipeptides are a family of ribosomally synthesized, post-translationally modified peptides that are widespread among bacteria, typically functioning as antibacterials. The marine picocyanobacteria Prochlorococcus and Synechococcus produce an unusual and diverse set of lanthipeptides of unknown function called prochlorosins. While well-studied model bacteria produce one or two different molecules of this type, a single picocyanobacterium can produce as many as 80; the community of picocyanobacteria in a single milliliter of seawater can collectively encode up to 10,000 prochlorosins. The molecular events that led to this expansion and diversification of the lanthipeptide repertoire in picocyanobacteria - the numerically dominant photosynthesizers in the oceans - is unknown. We present evidence for an unusual association between prochlorosin genes with a single-stranded DNA transposase belonging to the TnpAREP family. The genes co-occur and co-localize across the phylogeny of marine picocyanobacteria forming a distinct association pattern within genomes, most likely resulting from the transposase activity. Given the role of TnpAREP homologs in other bacteria, we propose - based on genomic structures - that they contribute to the creation of the prochlorosin structural diversity through a diversifying recombination mechanism. Post-submission noteSince the original submission of this manuscript to bioRxiv, we have refined our phylogenetic analysis of the TnpA-REP transposases and we do not find strong evidence for or against a causal relationship between the presence of TnpA-REP transposases and the expansion and diversification of lanthipeptides genes. While the genetic association described in the manuscript remains valid, adjacent TnpA-REP and Prochlorosins do not appear phylogenetically linked, which might simply be the consequence of high rates of recombination for both genes. More genomic data are needed to untangle the driving force behind the lanthipeptide gene expansion in marine picocyanobacteria. IMPORTANCEOnly a few mechanisms have been described that promote the diversification of a targeted gene region in bacteria. We present indirect evidence that the TnpAREP transposases associated with prochlorosins in picocyanobacteria could represent a novel such mechanism, and explain the extreme expansion and diversification of prochlorosins in this abundant marine microbe.

microbiology↗