Biology subjects

Navlakha, S.

Publications and source records attributed to Navlakha, S..

13 recordsLinked to original sources

bioRxiv2026

How the olfactory bulb maintains stable odor manifolds amid adaptation and representational drift

Adaptive coding in sensory circuits enables stable perception while accommodating experience-dependent changes. In the olfactory bulb (OB), repeated odor exposure reshapes population activity even without explicit behavioral feedback, but the underlying circuit mechanisms remain unclear. By analyzing longitudinal two-photon calcium imaging datasets from the mouse OB, we identified three concurrent forms of representational change: gain adaptation, similarity-dependent pattern separation or convergence, and a rotation of encoding subspace resulting in the representational drift. Using a computational model of the mitral cell-granule cell circuit, we showed that Hebbian plasticity and structural connectivity constraints are sufficient to reproduce these transformations. Despite global representational drift, the relative geometry of odor response vectors remained stable, preserving a low-dimensional odor manifold. Together, our results reveal how local plasticity and network structure jointly enable both stability and flexibility in early sensory coding.

neuroscience↗

bioRxiv2025

Sparse, random sampling is sufficient for central tolerance

Negative selection in the thymus limits autoimmunity by eliminating T cells that react strongly to self. Individual T cells, however, are only exposed to a small fraction of all self peptides during their "training" in the thymus, and it is puzzling how tolerance can be generalized to the remaining "test" self peptides across peripheral tissues in the body. Using a machine learning perspective, we show that such generalization is possible because the immune system satisfies two conditions: first that peptide abundance levels in the human thymus and periphery are highly correlated (i.e., training distribution {approx} test distribution), and second that cross-reactivity allows T cells to effectively learn binding information of similar peptides without explicitly interacting with all of them. Together, we show that sparse, random sampling of only 10% of self peptides in the thymus is sufficient to avoid reactivity to 90% of peripheral self, and we support this result with diverse experimental data. We then validate two predictions by our model; the first is that only 200-250 antigen presenting cells need to be seen by a T cell to ensure its robust selection, and the second relates how peptides missing from the thymus can drive auto-immunity of peripheral tissues. Overall, we provide a plausible answer to a long-standing question underlying adaptive immunity, and we highlight how generalization, a fundamental challenge faced by nearly every learning algorithm, is uniquely tackled by the immune system.

immunology↗

bioRxiv2025

Growth Cost and Transport Efficiency Tradeoffs Define Root System Optimization Across Varying Developmental Stages and Environments in Arabidopsis

Root system architecture (RSA) is central to plant adaptation and fitness, yet the design principles and regulatory mechanisms connecting RSA to environmental adaptation are not well understood. We developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework, which describes the balance between resource transport efficiency and construction cost. Applying Ariadne to Arabidopsis thaliana, we found that root architectures consistently assume Pareto-optimal forms across developmental stages, genotypes, and environmental conditions. Using the Discovery Engine, an engine that combines machine learning together with interpretability techniques, we found developmental stage, the hy5/chl1-5 genotype, and manganese availability as important determinants of the cost-efficiency tradeoff, with manganese exerting a unique influence not observed for other nutrients. These results reveal that RSA plasticity is genetically constrained to cost-efficiency optimal configurations and that developmental and environmental factors shift RSA on the pareto front, with manganese acting as a strong modulator of the transport efficiency and construction cost balance.

plant biology↗

bioRxiv2025

An evolutionarily conserved mechanism for reformatting odor concentration in early olfactory circuits

Understanding how stimuli from the sensory periphery are progressively reformatted to yield useful representations is a fundamental challenge in neuroscience. In olfaction, assessing odor concentration is key for many behaviors, such as tracking and navigation. Initially, as odor concentration increases, the average response of first-order sensory neurons also increases. However, the average response of second-order neurons remains flat with increasing concentration - a transformation that is believed to help with concentration-invariant odor identification, but that seemingly discards concentration information before it could be sent to higher brain regions. By combining neural data analyses from diverse species with computational modeling, we propose strategies by which second-order neurons preserve concentration information, despite flat mean responses at the population level. We find that individual second-order neurons have diverse concentration response curves that are unique to each odorant -- some neurons respond more with higher concentration and others respond less -- and together this diversity generates distinct combinatorial representations for different concentrations. We show that this encoding scheme can be recapitulated using a circuit computation, called divisive normalization, and we derive sufficient conditions for this diversity to emerge. We then discuss two mechanisms (spike rate vs. timing based) by which higher order brain regions may decode odor concentration from the reformatted representations. Since vertebrate and invertebrate olfactory systems likely evolved independently, our findings suggest that evolution converged on similar algorithmic solutions despite stark differences in neural circuit architectures. Finally, in land vertebrates a parallel olfactory pathway has evolved whose second-order neurons do not exhibit such diverse response curves; rather neurons in this pathway represent concentration information in a more monotonic fashion on average, potentially allowing for easier odor localization and identification at the expense of increased energy use.

neuroscience↗

bioRxiv2024

Reticulate leaf venation in Pilea peperomioides is a Voronoi diagram

Reticulate leaf venation, characterized by the presence of loops, is a distinguishing feature of many flowering plants. However, our understanding of both the geometry and the morphogenesis of reticulate vein patterns is far from complete. We show that in the Chinese money plant (Pilea peperomioides), major veins form an approximate Voronoi diagram surrounding secretory pores known as hydathodes. We also propose a mechanistic model based on polar transport of the plant hormone auxin to produce Voronoi patterns. In contrast with classical models where veins directly connect auxin sources to sinks, our model generates veins that bisect the space between adjacent auxin sources, collectively forming loops. The paradigm change offered by this model may open the door to study reticulate vein formation in other species.

plant biology↗

bioRxiv2024

BATMAN: Improved T cell receptor cross-reactivity prediction benchmarked on a comprehensive mutational scan database

Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database, called BATCAVE, encompassing complete single amino acid mutational assays of more than 22,000 TCR-peptide pairs, centered around 25 immunogenic human and mouse epitopes, across both major histocompatibility complex classes, against 151 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. We also developed an active learning version of BATMAN, which can efficiently learn the binding profile of a novel TCR by selecting an informative yet small number of peptides to assay. When validated on our database, BATMAN outperforms existing methods and reveals important biochemical predictors of TCR-peptide interactions. Finally, we demonstrate the broad applicability of BATMAN, including for predicting off-target effects for TCR-based therapies and polyclonal T cell responses.

immunology↗

bioRxiv2023

Stochastic coding: a conserved feature of odor representations and its implications for odor discrimination

Sparse coding is thought to improve discrimination of sensory stimuli by reducing overlap between their representations. Two factors, however, can offset sparse codings advantages. Similar sensory stimuli have significant overlap, and responses vary across trials. To elucidate the effect of these two factors, we analyzed odor responses in the fly and mouse olfactory regions implicated in learning and discrimination -- the Mushroom Body (MB) and the Piriform Cortex (PCx). In both species, we show that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic. Computationally, we show that the range of observed variability arises from probabilistic synapses in inhibitory feedback connections within central circuits rather than sensory noise, as is traditionally assumed. We propose this coding scheme to be advantageous for coarse- and fine-odor discrimination. More reliable cells enable quick discrimination between dissimilar odors. For similar odors, however, these cells overlap, and do not provide distinguishing information. By contrast, more unreliable cells are decorrelated for similar odors, providing distinguishing information, though this requires extended training with more trials. Overall, we have uncovered a stochastic coding scheme that is conserved in vertebrates and invertebrates, and we identify a candidate mechanism, based on variability in a winner-take-all inhibitory circuit, that improves discrimination with training.

neuroscience↗

bioRxiv2022

A neural theory for counting memories

"Ive never smelled anything like this." "Ive seen you once before." "Ive heard this song many times." Keeping track of the number of times different stimuli have been experienced is a critical computation for behavior. This computation occurs ubiquitously across sensory modalities, and naturally without reward or punishment. However, the neural circuitry that mediates this computation remains unknown. Here, we propose a theoretical two-layer neural circuit that can store counts of stimulus occurrence frequencies. This circuit implements a data structure, called a count sketch, that is commonly used in computer science to maintain item frequencies in streaming data. Our first model implements the count sketch data structure using Hebbian synapses and outputs stimulus-specific frequencies. Our second model uses anti-Hebbian plasticity and only tracks frequencies within four count categories ("1-2-3-many"), which we suggest makes a better trade-off between the number of categories that need to be distinguished and the potential ethological value of those categories. Using real-world datasets, we show how both models can closely track the frequencies of different stimuli experienced, while being robust to noise, thus expanding the traditional novelty-familiarity memory axis from binary to continuous. Finally, we show that an implementation of the "1-2-3-many" count sketch -- including network architecture, synaptic plasticity rule, and output neuron that encodes count categories -- exists in a novelty detection circuit in the insect mushroom body, and we argue that similar circuit motifs also appear in mammals, suggesting that basic memory counting machinery may be broadly conserved.

neuroscience↗

bioRxiv2020

A correspondence between normalization strategies in artificial and biological neural networks

A fundamental challenge at the interface of machine learning and neuroscience is to uncover computational principles that are shared between artificial and biological neural networks. In deep learning, normalization methods, such as batch normalization, weight normalization, and their many variants, help to stabilize hidden unit activity and accelerate network training, and these methods have been called one of the most important recent innovations for optimizing deep networks. In the brain, homeostatic plasticity represents a set of mechanisms that also stabilize and normalize network activity to lie within certain ranges, and these mechanisms are critical for maintaining normal brain function. In this survey, we discuss parallels between artificial and biological normalization methods at four spatial scales: normalization of a single neurons activity, normalization of synaptic weights of a neuron, normalization of a layer of neurons, and normalization of a network of neurons. We argue that both types of methods are functionally equivalent -- i.e., they both push activation patterns of hidden units towards a homeostatic state, where all neurons are equally used -- and that such representations can increase coding capacity, discrimination, and regularization. As a proof of concept, we develop a neural normalization algorithm, inspired by a phenomena called synaptic scaling, and show that this algorithm performs competitively against existing normalization methods on several datasets. Overall, we hope this connection will inspire machine learners in three ways: to uncover new normalization algorithms based on established neurobiological principles; to help quantify the trade-offs of different homeostatic plasticity mechanisms used in the brain; and to offer insights about how stability may not hinder, but may actually promote, plasticity.

neuroscience↗

bioRxiv2020

Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans.

Nervous systems extract and process information from their environment to alter animal behavior and physiology. Despite progress in understanding how different stimuli are represented by changes in neuronal activity, less is known about how they affect broader neural network properties. We developed a framework to use graph-theoretic features of neural network activity and predict ecologically-relevant stimulus properties - namely, stimulus identity and valence. Specifically, we used the transparent nematode, Caenorhabditis elegans, with its small nervous system, to define neural network features associated with various chemosensory stimuli. We trapped animals using a microfluidic device and exposed their noses to chemical stimuli known to be attractive or repellent, while monitoring changes in neural activity in more than 40 neurons in their heads. We found that repellents trigger higher average neural activity across the network, and that the tastant salt increases neural variability. In contrast, graph-theoretic features, which capture patterns of interactions between neurons, are better suited to decode stimulus identity than measures of neural activity. Furthermore, we show that a simple machine learning classifier trained using graph-theoretic features alone or in combination with neural activity features can accurately predict stimulus identity. These results indicate that graph theory reveals network characteristics that are distinct from neural activity, confirming its utility in extracting stimulus properties from neural population data. Significance StatementChanges in the external environment (stimuli) alter patterns of neural activity in animal nervous systems. A central challenge in computational neuroscience is to identify how stimulus properties alter interactions between neurons. We recorded neural activity data from C. elegans head neurons while the animal experienced various chemosensory stimuli. We then used a combination of activity statistics (i.e., average, standard deviation, and several frequency-based measures) and graph-theoretic features of network structure (e.g., modularity - the extent to which a network can be divided into independent clusters) to define neural properties that can accurately predict stimulus identity. Our method is general and can be used across species.

neuroscience↗

bioRxiv2019

Better tired than lost: turtle ant trail networks favor coherence over shortest paths

Creating a routing backbone is a fundamental problem in both biology and engineering. The routing backbone of the trail networks of arboreal turtle ants (Cephalotes goniodontus) connects many nests and food sources using trail pheromone deposited by ants as they walk. Unlike species that forage on the ground, the trail networks of arboreal ants are constrained by the vegetation. We examined what objectives the trail networks meet by comparing the observed ant trail networks with networks of random, hypothetical trail networks in the same surrounding vegetation and with trails optimized for four objectives: minimizing path length, minimizing average edge length, minimizing number of nodes, and minimizing opportunities to get lost. The ants trails minimized path length by minimizing the number of nodes traversed rather than choosing short edges. In addition, the ants trails reduced the opportunity for ants to get lost at each node, favoring nodes with 3D configurations most likely to be reinforced by pheromone. Thus, rather than finding the shortest edges, turtle ant trail networks take advantage of natural variation in the environment to favor coherence, keeping the ants together on the trails. Author SummaryWe investigated the trail networks of arboreal turtle ants in the canopy of the tropical forest, to ask what characterizes the colonys choice of foraging paths within the vegetation. We monitored day to day changes in the junctions and edges of trail networks of colonies in the dry forest of western Mexico. We compared the paths used by the ants to simulated random paths in the surrounding vegetation. We found that the paths of turtle ants prioritize coherence, keeping ants together on the trail, over minimizing the average edge length. The choice of paths reduces the number of junctions in the trail where ants could get lost, and favors junctions with a physical configuration that makes it likely that successive ants will reinforce the same path. Our work suggests that design principles that emphasize keeping information flow constrained to streamlined, coherent trails may be useful in human-designed distributed routing and transport networks or robot swarms.

ecology↗

bioRxiv2017

A distributed algorithm to maintain and repair the trail networks of arboreal ants

We study how the arboreal turtle ant (Cephalotes goniodontus) solves a fundamental computing problem: maintaining a trail network and finding alternative paths to route around broken links in the network. Turtle ants form a routing backbone of foraging trails linking several nests and temporary food sources. This species travels only in the trees, so their foraging trails are constrained to lie on a natural graph formed by overlapping branches and vines in the tangled canopy. Links between branches, however, can be ephemeral, easily destroyed by wind, rain, or animal movements. Here we report a biologically feasible distributed algorithm, parameterized using field data, that can plausibly describe how turtle ants maintain the routing backbone and find alternative paths to circumvent broken links in the backbone. We validate the ability of this probabilistic algorithm to circumvent simulated breaks in synthetic and real-world networks, and we derive an analytic explanation for why certain features are crucial to improve the algorithms success. Our proposed algorithm uses fewer computational resources than common distributed graph search algorithms, and thus may be useful in other domains, such as for swarm computing or for coordinating molecular robots.

bioinformatics↗

bioRxiv2017

A neural algorithm for a fundamental computing problem

Similarity search, such as identifying similar images in a database or similar documents on the Web, is a fundamental computing problem faced by many large-scale information retrieval systems. We discovered that the flys olfac-tory circuit solves this problem using a novel variant of a traditional computer science algorithm (called locality-sensitive hashing). The flys circuit assigns similar neural activity patterns to similar input stimuli (odors), so that behav-iors learned from one odor can be applied when a similar odor is experienced. The flys algorithm, however, uses three new computational ingredients that depart from traditional approaches. We show that these ingredients can be translated to improve the performance of similarity search compared to tra-ditional algorithms when evaluated on several benchmark datasets. Overall, this perspective helps illuminate the logic supporting an important sensory function (olfaction), and it provides a conceptually new algorithm for solving a fundamental computational problem.

neuroscience↗