Summary: According to neurosciences, a small community of neurons in fruit flies can properly maintain an internal map, challenging earlier theories that essential large networks for accuracy.
This innovative understanding reveals that little networks, when properly connected, may perform complex tasks like tracking geographical orientation. The investigation changes how scientists think about mind functions, such as storage and decision-making, suggesting little brain systems are more powerful than previously believed.
Important Facts:
- Fruit fly use a small community of neurons to maintain a precise internal gyroscope.
- Smaller sites may do complicated computations, but they require specific links.
- This finding transforms our knowledge of how the mind handles decision-making and transportation.
Origin: HHMI
Scientists had a concern.
Researchers have been studying how an individual’s head tracks its location without external cues for decades, similar to how we can observe where we are even with our eyes closed.
An internal compass that tracks where you are in the world is maintained by networks of neurons called band object networks, which were based on head recordings from rodents.
A big network with many neurons, as opposed to a small network with some neurons, may cause the compass ‘ needle to drift, leading to errors.
Therefore, the researchers discovered a compass inside the small fruit fly.
” The bird’s map is pretty accurate, but it’s built from a truly little community, contrary to what prior ideas assumed”, says Janelia Group Leader Ann Hermundstad. ” But, there was evidently a gap in our knowledge of mental gyroscopes”.
Then, study led by Marcella Noorman, a doctorate in the Hermundstad Lab at HHMI’s Janelia Research Campus, explains this problem. With a very tiny system, like in berries flies, it is possible to build a perfectly correct internal gyroscope.
The research alters how brains functiones in a variety of tasks, from navigation to navigation to decision-making, is thought about.
This “really increases our understanding of what little networks may do,” Noorman claims. They really can do a lot more complex computations than originally believed, according to  .
Generating a necklace object
How could the fruit bird’s little head create an accurate inner compass when Noorman arrived at Janelia in 2019? Hermundstad and others had been puzzled over how to solve this.  ,  ,  ,
Noorman first set out to show that you , could n’t  , generate a ring attractor with a small network of neurons, but that you needed to add “extra stuff” — like other cell types and more detailed biophysical properties of the cells – to get it to work.
To do that, she stripped aside all the , “extra things” from existing versions, to see if she could make a circle object with what was left over. She thought this would n’t be possible.
But Noorman struggled to show her thesis. That’s when she decided she needed a unique view.
” I had to turn my mindset and think, nicely, maybe it’s because you , can , make a ring attractor with a little network”, she says,  ,” and then find out what certain conditions the network has to meet to make that happen”.
By changing her notion, Noorman discovered that, in reality, it is possible to make a circle object with as few as four neurons, as long as the connections between them are properly adjusted. Finding physiological proof that the fly brain can produce a ring attractor, Noorman collaborated with other Janelia researchers to test the new theory in the lab.
Noorman claims that” smaller brains and smaller brains can perform more complex computations than we thought.” However, in order to do this, the neurons must be connected much more precisely than they would otherwise be in a larger brain, where you can use a lot of neurons to perform the same computation.
There’s therefore a difference between how many neurons you use for this computation and how carefully you connect them, she says.
Next, the researchers want to know whether the “extra stuff” can increase the ring attractor network’s robustness and whether the base computation can serve as a foundation for more complex computations in larger networks with multiple variables.
Additional studies may be conducted to better understand how the network’s connections between neurons are altered and how sensory signals might affect how the network’s representation of head direction is influenced.
For Noorman, a mathematician turned neuroscientist, it has been challenging but fun to figure out how to translate biology into a math problem that can be solved.
It’s been fun to actually figure out and understand how the fly’s head direction system works, she says.” The fly’s head direction system is the first example of neural activity that I’d ever seen.
About this news about neuroscience research
Author: Nanci Bompey
Source: HHMI
Contact: Nanci Bompey – HHMI
Image: The image is credited to Neuroscience News
Original Research: Open access.
By Ann Hermundstad and colleagues,” Maintaining and updating accurate internal representations of continuous variables with a few neurons.” Nature Neuroscience
Abstract
With a few neurons and maintaining and updating accurate internal representations of continuous variables
Many animals rely on persistent internal representations of continuous variables for working memory, navigation, and motor control. Large networks of neurons are typically assumed to be necessary for maintaining such representations accurately, according to current theories. Only networks with few neurons are thought to produce discrete representations.
However, analyzing two-photon calcium imaging data from tethered flies in the dark suggests that their tiny head-direction system can provide a surprisingly accurate and continuous representation.
We thus ask whether it is possible for a small network to generate a continuous, rather than discrete, representation of such a variable. Even very small networks can be tuned to maintain continuous internal representations, as shown in our analysis, but this does so at the expense of sensitivity to noise and tuning variations.
This work expands the range of small network computations and raises the possibility that larger networks could contain more and higher-dimensional variables than previously thought.
