Summary: A new computer model of C. elegans neuronal activity serves to unmask the roles of different neurons.
A recent study from the Complexity Science Hub (CSH) in Vienna paves the way for a better understanding of the complexity of the human brain, one of the largest and most sophisticated organs in the human body.
The study – which develops a mathematical and computational framework for analyzing neural activity in C.elegansa small worm that has been used as a model organism to study neural activity – was released on Friday in the review Computational Biology PLoS.
The microscopic organism, made up of just 1,000 cells – including 300 neurons – has been accurately mapped, but the role of neurons in controlling behavior remains controversial, says Edward Lee, post-doctoral fellow at CSH and author of the paper. .
Based on recent advances in measuring neurons in live worms, the new study offers a way to unmask the roles of neurons using more natural perturbations.
“In the work, we try to be more holistic, in the sense that we take all the data and try to figure out which sets of neurons go together and are associated with a particular behavior,” says Lee.
“In other words, if I want the worm to turn left, I don’t care about one particular neuron, I probably care about several different neurons.”
Experiment with a simple neural system
Lee and his team study the worm as an example because its simple neural system provides a solid foundation for understanding the brain mechanisms of higher animals, such as humans.
The researchers developed a mathematical model for collective neural activity. They then conducted a silicone experiment with small neural disturbances that can trigger behavioral responses and can be replicated in a scientific trial.
“The idea is that if you can, in a model, bypass each of the neurons in different ways, you can measure how the behavior changes. And if the behavior changes, for example, more strongly when two neurons are brought closer together, then somehow so these two neurons form a whole and are not independent of each other,” explains Lee.
Future research in neuroscience
Lee says the results point to interesting neurons that can be used as a starting point for neuroscientific research.
The study, which analyzed about 50 neurons in the C.elegans nervous system, suggests that there are a handful of “pivotal” neurons that are associated with a statistically significant response.
“It might be a good idea to look at these neurons,” says the CSH scientist.
“Knowing that a neuron is involved in a specific behavior doesn’t tell you what it’s doing. Some of the experimental results don’t indicate that a neuron was necessarily involved in behavior in a significant way, for example,” Lee says.
When several neurons are involved in a particular behavior, it can be interesting to study how they work together or against each other.
The article poses several new hypotheses regarding how behavioral control might be centralized in particular neural cells.
“We offer a theoretical framework for asking these questions and making predictions,” Lee concludes, adding that he hopes experiments will answer them in the years to come.
About this neuroscience research news
Author: Verena Ahne
Contact: Verena Ahne – CSH
Image: Image is in public domain
Original research: Free access.
“Discovering sparse control strategies in neural activity” by Edward Lee et al. Computational Biology PLOS
Uncover sparse control strategies in neural activity
Biological circuits such as neural or gene regulatory networks use internal states to map sensory inputs to an adaptive repertoire of behavior. Characterizing this mapping is a major challenge for systems biology.
Although experiments that probe internal states are growing rapidly, the complexity of the organism presents a fundamental obstacle given the many possible ways in which internal states could be mapped to behavior.
Using C.elegans as an example, we propose a protocol for systematic perturbation of neural states that limits experimental complexity and could potentially help characterize collective aspects of the neurobehavioral map.
We consider experimentally motivated small perturbations—those that are most likely to preserve natural dynamics and are closer to internal control mechanisms—neural states and their impact on collective neural activity. Then, we connect these perturbations to the local information geometry of collective statistics, which can be fully characterized using pairwise perturbations.
Application of the protocol to a minimal model of C.elegans neural activity, we find that collective neural statistics are most sensitive to a few major perturbative modes. The dominant eigenvalues initially decay as a power law, unveiling a hierarchy that arises from variation in individual neural activity and pairwise interactions.
The highest-ranking modes tend to be dominated by a few “pivotal” neurons that explain most of the system’s sensitivity, suggesting a sparse mechanism of collective control.