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Study reveals that methods to infer the connectivity of neural circuits are affected by systematic errors

A) Left: figure representing a three-neuron circuit with two connections. Center: all neurons are correlated. Right: circuit inference algorithms should ‘explain away’ correlations between the top neurons based on their common input, ruling out a direct connection. B) Neurons (black dots) in a ring network. Gray curve, Mexican hat-shaped weights from an example node (gray dot) to the rest; all other neurons have the same weight profile. C) The resulting weight matrix W (top) is circulant, comprising rotations of the same row (bottom). D) Scalar parameter r modulates the strength of all recurrent weights. Spike raster plots (top) and snapshots of synaptic activity (bottom) of the network at weak (red) and strong (blue) weights (small and large r). Credit: Das & Fiete, Nature Neuroscience (2020).

In recent years, a growing number of computer scientists have tried to develop computational methods inspired by the structure, function and plasticity of neural circuits in the human brain. Achieving a comprehensive understanding of biological neural circuits is of vital importance for the creation of these neuro-inspired computing systems.

To fully comprehend the mechanisms that allow biological neural circuits to compute information and adapt over time, neuroscientists should be able to examine the connections between individual neurons. While recent advancements in circuit tracing techniques have opened up new possibilities for studying these connections, collecting data using these techniques can still be very challenging and expensive.

Some scientists have thus devised of estimating neural connectivity based on multicell neural activity recordings. While these methods are widely used, they might not lead to reliable representations of neural connections.

Researchers at the University of Texas at Austin have recently carried out a study investigating the effectiveness of existing methods for algorithmically estimating the wiring of neural networks. Their findings, published in Nature Neuroscience, suggest that

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COVID-19: Holiday Celebrations | CDC

During the celebration

Follow these tips to reduce your risk of being exposed to, getting, or spreading COVID-19 during the celebration:

Social distance and limit close contact

  • Maintain a distance of at least 6 feet or more from people you don’t live with. Be particularly mindful in areas where it may harder to keep this distance, such as restrooms and eating areas.
  • Avoid using restroom facilities at high traffic times, such as at the end of a public event.
  • Avoid busy eating areas, such as restaurants during high volume mealtimes, if you plan to eat out at a restaurant.
  • Minimize gestures that promote close contact. For example, do not shake hands, elbow bump, or give hugs. Instead wave and verbally greet others.

Wear masks

  • Wear a mask at all times when around people who don’t live in your household to reduce the risk of spreading the virus.
  • Avoid singing, chanting, or shouting, especially when not wearing a mask and within 6 feet of others.

Do not use costume masks in place of cloth masks

  • Do not use a costume mask (such as for Halloween) as a substitute for a cloth mask unless it is made of two or more layers of breathable fabric that covers your mouth and nose and doesn’t leave gaps around your face.
  • Do not wear a costume mask over a cloth mask because it can be dangerous if the costume mask makes it hard to breathe. Instead, consider using a Halloween-themed cloth mask.

Limit contact with commonly touched surfaces or shared items

Wash hands

  • Wash your hands often with soap and water for at least 20 seconds, especially after you have been in a public place, or after blowing your nose, coughing, or sneezing. If soap and water are not readily available, use a hand