Newly Discovered Neural Network Gets Visual and Motor Circuits in Sync – Innovita Research

Newly Discovered Neural Network Gets Visual and Motor Circuits in Sync

A recent paper in the journal Neuron presents a discovery of a bi-directional neural network connecting the legs and the visual system to shape walking.

Researchers noticed that the visual neurons of a fruit fly relay a stream of neural activity even in total darkness. It turned out that the neurons were tracking the animal’s steps.

A fruit fly.

A fruit fly. Image credit: Buntysmum via Pixabay, free license

Researchers used a whole-cell patch recording technique to track the charge of neurons. Neurons’ charge is synched to the animal’s steps in a manner optimal for fine-tuning each movement. When the foot is up in the air, the neuron sends out adjustment directions to the motor region if needed. When the foot is on the ground, the neuron is inhibited.

The neural network operates on a fast timescale to monitor and correct each step and simultaneously promotes the animal’s behavioral goal. For example, when the fly is walking fast, the neuronal charge becomes increasingly positive to help maintain the action plan.

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.

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