Summary: Researchers have identified a neural network that connects the legs to the visual system in order to shape walking.
Source: Champlimaud Center for the Unknown
A fruit fly walks on a small styrofoam ball fashioned into a floating 3D treadmill. The room is completely dark, and yet, an electrode recording visual neurons in the fly’s brain relays a mysterious stream of neural activity, rising and falling like a sinusoidal wave.
When Eugenia Chiappe, a neuroscientist at the Champalimaud Foundation in Portugal, first saw these results, she had a hunch her team had made an exceptional discovery. They were recording from visual neurons, but the room was dark, so there was no visual signal that could drive the neurons in that manner.
“That meant that the unusual activity was either an artifact, which was unlikely, or that it was coming from a non-visual source,” Chiappe recalled. “After the possibility of interference was investigated and dismissed, I was sure: the neurons were faithfully tracking the animal’s steps.”
A few years and many new insights later, Chiappe and her team now present their discovery in the scientific journal Neuron: a bi-directional neural network connecting the legs and the visual system to shape walking.
“One of the most remarkable aspects of our finding is that this network supports walking on two different timescales simultaneously,” said Chiappe. “It operates on a fast timescale to monitor and correct each step while promoting the animal’s behavioral goal.”
Tracking neural ‘mood’
“Vision and action may seem unrelated, but they are actually tightly associated; just choose a point on the wall and try placing your finger on it with your eyes closed,” said Chiappe. “Still, little is known about the neural basis of this link.”
In this study, the team focused on a particular type of visual neuron that is known to connect to motor brain areas. “We wanted to identify the signals that these neurons receive and understand if and how they participate in movement,” explained Terufumi Fujiwara, the first author of the study.
To answer these questions, Fujiwara used a powerful technique called whole-cell patch recording that enabled him to tap into the neurons’ “mood,” which can be either positive or negative.
“Neurons communicate with each other using electric currents that alter the overall charge of the receiving neuron. When the neuron’s net charge is more positive, it is more likely to become active and then transmit signals to other neurons. On the other hand, if the charge is more negative, the neuron is more inhibited,” Fujiwara explained.
Watching each step
The team tracked the neurons’ charge and revealed that it was synched to the animal’s steps in a manner that was optimal for fine-tuning each movement.
“When the foot was up in the air, the neuron was more positive, ready to send out adjustment directions to the motor region if needed. On the other hand, when the foot was on the ground, making adjustments impossible, the charge was more negative, effectively inhibiting the neuron,” said Chiappe.
Keeping the course
When the team analyzed their results further, they noticed that charge of the neurons was also changing on a longer timescale. Specifically, when the fly was walking fast, the charge became increasingly more and more positive.
“We believe that this variation helps maintain the animal’s behavioral goal,” said Fujiwara. “The longer the fly has been walking fast, the higher are the chances that it would need help to maintain this action plan. Therefore, the neurons become increasingly ‘more alert’ and ready to be recruited for movement control.”
The brain is not always the boss
Many experiments followed, creating a fuller description of the network and demonstrating its direct involvement in walking. But according to Chiappe, this study goes even further than revealing a new visual-motor circuit, it also provides a fresh perspective on the neural mechanisms of movement.
“The current view of how behavior is generated is very ‘top-down’: the brain commanding the body. But our results provide a clear example of how signals originating from the body contribute to movement control.
“Though our findings were made in the fly animal model, we speculate that similar mechanisms may exist in other organisms. Speed-related representations are critical during exploration, navigation, and spatial perception, functions that are common to many animals, including humans,” she concluded.
About this neuroscience research news
Author: Press Office
Source: Champlimaud Center for the Unknown
Contact: Press Office – Champlimaud Center for the Unknown
Image: The image is in the public domain
Original Research: Closed access.
“Walking strides direct rapid and flexible recruitment of visual circuits for course control in Drosophila” by Eugenia Chiappe et al. Neuron
Walking strides direct rapid and flexible recruitment of visual circuits for course control in Drosophila
- HS cells receive stride-coupled signals via ascending neurons
- The stride-coupled signals reflect an internal motor context
- Motor context modulates HS cells at multiple timescales
- HS cells drive rapid steering depending on motor context
Flexible mapping between activity in sensory systems and movement parameters is a hallmark of motor control. This flexibility depends on the continuous comparison of short-term postural dynamics and the longer-term goals of an animal, thereby necessitating neural mechanisms that can operate across multiple timescales.
To understand how such body-brain interactions emerge across timescales to control movement, we performed whole-cell patch recordings from visual neurons involved in course control in Drosophila.
We show that the activity of leg mechanosensory cells, propagating via specific ascending neurons, is critical for stride-by-stride steering adjustments driven by the visual circuit, and, at longer timescales, it provides information about the moving body’s state to flexibly recruit the visual circuit for course control.
Thus, our findings demonstrate the presence of an elegant stride-based mechanism operating at multiple timescales for context-dependent course control.
We propose that this mechanism functions as a general basis for the adaptive control of locomotion.