Summary: Researchers have developed a very precise model simulating pet movements, using the nematode worm C. worm as a foundation. With programs in robotics and medicine, this model enables both repetitive and strange behaviors.
The strategy could help identify movement disorders, improve mechanical designs, and aid in drug discovery. The simulation’s precision offers a new way to know the factors influencing actions.
Important Information:
- Using machine learning techniques, the unit predicts complex animal actions.
- Programs include improving robotics and diagnosing motion disorders.
- could lead to better medicine tests and personalized care plans.
Origin: OIST
Scientists have discovered a novel way to accurately create complex animal movements.
The research team’s goal was to effectively model the intricate and ostensibly unpredictable movements of living things, a problem that has been plagued biology for a long time. They focused on the worm worm , Caenorhabditis worm, a model organism commonly used in natural analysis.
The findings, published in , PNAS, support identify and understand animal actions, with possible applications ranging from robotics to medical research.  ,
Pet conduct exists in a space between normal and strange actions, unlike basic bodily systems like a pendulum or a bead on a spring. Capturing that delicate balance is very tricky and that’s what makes our model unique—no one has ever presented a model of an animal this lifelike before”, explained Prof. Greg Stephens, leader of the , Biological Physics Theory Unit , at the Okinawa Institute of Science and Technology ( OIST ).
Properly mimicking real insect movements ,
” An animal’s behavior are influenced by several factors, including its inner state, economic activities, development story, and genetic inheritance. Communicating these influences in a straightforward, predictive model is outstanding and somewhat contradictory. This complexity, and our ability to model it effectively, is noteworthy”, explained Dr. Antonio Costa, lead author at the Paris Brain Institute at Sorbonne University.  ,
The creation of the model involved a long process that involved several steps. The team started by recording high-resolution videos of worm movements. Every video frame was analyzed using machine learning techniques to determine the shape of the worm in each one.
In order to gain a deeper understanding of worm behavior, they then examined how these shapes changed over time. Finally, they determined how much past data was needed to make reliable predictions.  ,  ,
” We compared statistical properties of real animal behavior, such as movement speed and frequency of behavioral changes, with those generated by our simulations”, Dr. Costa added. The close matching of these data sets demonstrates our model’s high level of accuracy, the authors claim.
Implications for medicine and robotics ,
The research’s implications extend far beyond the study of worms. The team is already in contact with businesses that use this nematode worm to evaluate the behavior effects of chemical substances.
They are also applying the model to other species,  , including zebrafish larvae, which are frequently used in drug discovery research. Additionally, the researchers are exploring applications in human medicine, particularly in the study of movement disorders like Parkinson’s disease.  ,
The potential impact on medical research is significant. Moving disorders are frequently based on subjective observations made during brief clinical visits. These changes may not be obvious enough to be observed directly, which is a part of the difficulty of diagnosing these medical conditions.
This new approach could provide more continuous, objective measurements of patient movements, even in home settings, leading to more precise diagnoses and personalized treatment strategies.  ,
Beyond medicine, the model could have applications in fields such as robotics, where achieving natural-looking movement has been a persistent challenge. Engineers may be able to create more adaptable and effective robotic systems by better understanding how animals navigate their environments.  ,
The team anticipates that this approach will open new avenues for understanding the intricate connections between environmental factors, genetics, and behavior across a wide range of species as they continue to refine and improve their modeling techniques.  ,
About this AI, robotics, and neuroscience research news
Author: Tomomi Okubo
Source: OIST
Contact: Tomomi Okubo – OIST
Image: The image is credited to Neuroscience News
Original Research: Closed access.
” Caenorhabditis elegans behavior across scales: a Markovian dynamics” by Greg Stephens and al. PNAS
Abstract
Caenorhabditis elegans behavior across scales: a Markovian dynamics
How can we capture the diversity of animal behavior, from quick body twitches to aging?
Using high-resolution videos of the nematode worm , Caenorhabditis elegans, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion and longer-lived behavioral states.
We adjust the sequence length for maximum prediction by using short posture sequences as an instantaneous behavioral measure. Within the space of posture sequences, we construct a fine-scale, maximum entropy partition so that transitions among microstates define a high-fidelity Markov model, which we also use as a means of principled coarse-graining.
We use resistive force theory to translate these dynamics into movement and capture the statistical characteristics of foraging trajectories. Predictive across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a top–down subdivision of the worm’s foraging behavior, revealing both “runs-and-pirouettes” as well as previously uncharacterized finer-scale behaviors.
We use our model to examine the relevance of these subtle traits for foraging success while recovering a balance between local and global search strategies.
