Newly Discovered Neural Network Synchronizes Visual and Motor Circuits

Summary: Researchers have identified a neural network that connects the legs to the visual system to shape walking.

Source: Champlimaud Center of the Unknown

A fruit fly walks on a small styrofoam ball shaped into a floating 3D treadmill. The room is completely dark, yet an electrode recording visual neurons in the fly’s brain relays a mysterious stream of neural activity, rising and falling like a sine wave.

When Eugenia Chiappe, a neuroscientist at the Champalimaud Foundation in Portugal, first saw these results, she had a hunch that her team had made an exceptional discovery. They were recording from visual neurons, but the room was dark, so there was no visual cue that could drive the neurons that way.

“That meant the unusual activity was either an artifact, which was unlikely, or it came from a non-visual source,” Chiappe recalled. “After the possibility of interference was investigated and ruled out, I was sure: the neurons were faithfully following the animal’s footsteps.”

A few years and many discoveries later, Chiappe and his team now present their discovery in the scientific journal neuron: a bidirectional neural network connecting the legs and the visual system to shape walking.

“One of the most remarkable aspects of our discovery is that this network allows walking on two different time scales simultaneously,” Chiappe said. “It works on a rapid timescale to monitor and correct each step while promoting the animal’s behavioral goal.”

Neural “mood” tracking

“Vision and action may seem unrelated, but they are actually closely associated; just choose a point on the wall and try to place your finger on it with your eyes closed,” Chiappe said. “Yet little is known about the neural basis of this link.”

In this study, the team focused on a particular type of visual neuron known to connect to motor areas of the brain. “We wanted to identify the signals 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 allowed him to tap into the “mood” of neurons, which can be positive or negative.

“Neurons communicate with each other using electrical currents that change 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. In contrast, if the charge is more negative, the neuron is more inhibited,” Fujiwara explained.

Watch every step

The team tracked neuron loading and found that it was synchronized with the animal’s footsteps in an optimal way to fine-tune each movement.

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In this study, the team focused on a particular type of visual neuron known to connect to motor areas of the brain. Image is in public domain

“When the foot was in the air, the neuron was more positive, ready to send adjustment instructions to the motor region if needed. In contrast, when the foot was on the ground, making adjustments impossible, the load was more negative, effectively inhibiting the neuron,” Chiappe said.

Stay focus

When the team analyzed their results in more detail, they noticed that the neuron load also changed over a longer period of time. Specifically, as the fly walked fast, the charge became more and more positive.

“We think this variation helps maintain the animal’s behavioral focus,” Fujiwara said. “The longer the fly has been walking, the more likely it is to need help to maintain this course of action. As a result, neurons become increasingly ‘more alert’ and ready to be recruited for motion control.

The brain is not always the boss

Many experiments followed, creating a more complete description of the network and demonstrating its direct involvement in walking. But according to Chiappe, this study goes even further than revealing a new visuo-motor circuit, it also offers a new perspective on the neural mechanisms of movement.

“The current view of how behavior is generated is very top-down: the brain controls the body. But our results provide a clear example of how signals from the body contribute to movement control.

See also

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“Although our findings were made in the animal model of the fly, we hypothesize that similar mechanisms may exist in other organisms. Velocity-related representations are essential during exploration, navigation and navigation. spatial perception, functions common to many animals, including humans,” she concluded.

About this neuroscience research news

Author: Press office
Source: Champlimaud Center of the Unknown
Contact: Press service – Champlimaud Center of the unknown
Picture: Image is in public domain

Original research: Access closed.
Walking strides direct rapid and flexible recruitment of visual circuits for gait control in Drosophilaby Eugenia Chiappe et al. neuron


Walking strides direct rapid and flexible recruitment of visual circuits for gait control in Drosophila

Strong points

  • HS cells receive gait-coupled signals via ascending neurons
  • Gait-coupled signals reflect an internal motor context
  • Motor context modulates HS cells at multiple timescales
  • HS cells drive rapid steering based 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 longer-term goals of an animal, thus requiring neural mechanisms that can operate on 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 leg mechanosensory cell activity, propagating via specific ascending neurons, is essential for visual circuit-driven stepwise directional adjustments, and, at longer time scales, provides information on the state of the moving body to flexibly recruit the visual circuitry for course control.

Thus, our results 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.

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