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 statistical methods 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