Biology subjects

Manoonpong, P.

Publications and source records attributed to Manoonpong, P..

3 recordsLinked to original sources

bioRxiv2026

Embodied timing turns a robotic tug-of-war into cooperative transport

Living systems move loads with agents that pull against one another. Kinesin and dynein pull one vesicle, ants haul a dead insect, and antagonist muscles move a joint. Whether the timing of the forces can, by itself, determine which side moves the load is not well understood. Here, combining experiments, theory, and simulations of a tug-of-war between two matched teams of spring-coupled robots, we show that the relative timing of the pull forces breaks the symmetry and decides the contest. Teams pulling in unison or in strict alternation tie. Between these, the lagging team wins, most strongly near a quarter cycle, because the springs unload while the leading team coasts and the lagging team still pulls. In the model and in robot simulations, braking rather than coasting between pulls, once strong enough, shifts the advantage to the leading team. Winning and losing leave distinct signatures in each robot's motor current. Using these local signals, we formulate an embodied decentralized phase-adaptation rule that enables transient leadership to switch between teams, allowing the collective to transport high-friction cargo and to navigate around obstacles towards a prescribed target without explicit communication.

biophysics↗

bioRxiv2022

Network architecture producing swing to stance transitions in an insect walking system

The walking system of the stick insect is one of the most thoroughly described invertebrate systems. We know a lot about the role of sensory input in the control of stepping of a single leg. However, the neuronal organization and connectivity of the central neural networks underlying the rhythmic activation and coordination of leg muscles still remain elusive. It is assumed that these networks can couple in the absence of phasic sensory input due to the observation of spontaneous recurrent patterns (SRPs) of coordinated motor activity equivalent to fictive stepping-phase transitions. Here we sought to quantify the phase of motor activity within SRPs in the isolated and interconnected meso- and metathoracic ganglia. We show that SRPs occur not only in the meso-, but also in the metathoracic ganglia of the stick insect, discovering a qualitative difference between them. We construct a network based on neurophysiological data capable of reproducing the measured SRP phases to investigate this difference. By comparing network output to the biological measurements we confirm the plausibility of the architecture and provide a hypothesis to account for these qualitative differences. The neural architecture we present couples individual central pattern generators to reproduce the fictive stepping-phase transitions observed in deafferented stick insect preparations after pharmacological activation, providing insights into the neural architecture underlying coordinated locomotion.

neuroscience↗

bioRxiv2020

Integrating Non-Spiking Interneurons in Spiking Neural Networks

Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well researched biological example of such a mixed network is the sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This pathway is also well researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network. Additionally, we show that non-spiking neurons can effectively manipulate post-synaptic spiking neurons in an event-based architecture. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.

neuroscience↗