Biology subjects

Jayaram, K.

Publications and source records attributed to Jayaram, K..

4 recordsLinked to original sources

bioRxiv2026

Excitation-inhibition interactions mediate firefly flash synchronization

Large populations of fireflies can synchronize their bioluminescent flashes with remarkable precision, producing collective rhythms that emerge from interactions among intrinsically variable individuals. In the North American firefly Photuris frontalis, this behavior usually manifests as a stable, population-level single-period beat whose mechanistic origins remain unresolved. To identify the local interaction rules giving rise to this emergent synchrony, we performed controlled perturbation experiments on isolated P. frontalis males using fixed-period light stimuli. By measuring changes in flash period as a function of stimulus timing, we reconstructed the phase-response curve (PRC) governing individual flash dynamics. The resulting PRC exhibits a biphasic structure, revealing phase-advancing (excitatory) and phase-delaying (inhibitory) responses. Using this PRC, we formalized an integrate-and-fire model that quantitatively reproduced the observed adaptable entrainment across tested stimuli. These results establish a direct mechanistic link between phase sensitivity and emergent collective synchronization, demonstrating how excitation-inhibition interactions influence large-scale rhythmic coherence in firefly populations.

animal behavior and cognition↗

bioRxiv2025

Physically intelligent soft antennae enhance tactile perception by active touch

Soft robotic sensors today struggle to interpret complex tactile scenes without incurring significant computational costs. Inspired by insect antennae--compliant, distributed sensors that efficiently process tactile information through physical intelligence--we investigated whether mechanical design and active touch sensing strategies could enhance robotic tactile feature perception. We hypothesized that insect-inspired antenna dynamics, specifically flexural stiffness gradients and active touch speed, could simplify tactile classification. Using a sim-to-real framework that bridges bioinspired computational models with a multi-link soft robot antenna, we introduce the notion of tactile fields--spatiotemporal representations of tactile stimuli shaped by contact location, feature type, and active touch speed. Our analyses show that cockroach-inspired antenna mechanics jointly with active touch speeds improve feature classification accuracy compared to conventional sensors with uniform flexural stiffness gradient by increasing tactile data sparsity and dispersion. An exploration of stiffness and damping of antenna mechanics revealed design trade-offs that influence tactile discrimination and structural stability. Through sim-to-real transfer, stiffness gradients and structured active touch motions were demonstrated on a miniature distributed soft robotic antenna, validating their effectiveness in real-world robotic systems. Taken together, this work presents a biologically grounded framework for tactile sensor design that reduces computational load and enhances adaptability.

bioengineering↗

bioRxiv2025

Mechanical and morphological features of the cockroach antenna confer flexibility, reveal a kinematic chain system and predict strain information for proprioception

A broad class of animals rely on touch sensation for perception. Among insects, the American cockroach P. americana is a touch specialist that uses a pair of soft antennae with distributed sensors to touch its environment to guide decision making. During touch, forces on the antenna can activate thousands of mechanosensors. To understand the content of this sensory information, it is critical to understand how antenna mechanics shape the transmission of contact forces. Here, we investigate the mechanical behavior and morphology of the American cockroach antenna at the individual segment level through experiments, mathematical modeling, imaging with Micro-Computed Tomography (Micro-CT), 3D reconstruction of antenna morphology, and finite element modeling (FEM). Our experimental results and model predictions reveal that the antenna flagellum bends according to a kinematic chain model, with rigid segments connected by joints. Whereas the middle region of the antenna consistently fractured under cyclic bending, the tip region remained intact under large deformations, revealing mechanical specialization along the antenna. Micro-CT imaging revealed an invagination of the exocuticle at segment intersections of the tip. To test the hypothesis that this structure can enhance flexibility and robustness, we used FEM and confirmed that the invagination allows for larger bending without structural failure (buckling). Applying FEM to a morphologically accurate kinematic chain model of the flagellum revealed the relationship between the local strain at the location of marginal sensilla and intersegment angle, predicting the information available for antenna proprioception. Taken together, these findings reveal biomechanical adaptations of insect antennae and provide a critical step toward a mechanistic understanding of touch sensation in a touch specialist. SUMMARY STATEMENTBy combining experiments, imaging and modeling, we demonstrate distinct mechanical features in the cockroach antenna and provide a framework to model the neuromechanics of the antenna in an insect touch specialist.

biophysics↗

bioRxiv2022

Strength-mass scaling law governs mass distribution inside honey bee swarms

To survive during colony reproduction, bees create dense clusters of thousands of suspended individuals. How can this swarm, which is orders of magnitude larger than the size of an individual, maintain mechanical stability? We hypothesize that the internal structure in the bulk of the swarm, about which there is little prior information, plays a key role in mechanical stability and thermoregulation. Here, we provide the first-ever 3D reconstructions of the positions of the bees in the bulk of the swarm using x-ray computed tomography. We find that the mass of bees in a layer decreases with distance from the attachment surface. By quantifying the distribution of bees within swarms varying in size (made up of 4000-10000 bees), we find that the same power law governs the smallest and largest swarms, with the weight supported by each layer scaling with the mass of each layer to the {approx} 1.5 power. This arrangement ensures that each layer exerts the same fraction of its total strength, and on average a bee supports a lower weight than its maximum grip strength. This illustrates the extension of the scaling law relating weight to strength of single organisms to the weight distribution within a superorganism made up of thousands of individuals.

animal behavior and cognition↗