Primate study sheds light on a neural mechanism that separates signal from noise in the brain

When the brain is observed through imaging, there is a lot of “noise,” which is spontaneous electrical activity that comes from a resting brain. This appears to be different from brain activity that comes from sensory inputs, but just how similar—or different—the noise is from the signal has been a matter of debate.

New research led by a team at the University of Tokyo further untangles the relationship between internally generated noise and stimulus-related patterns in the brain, and finds that the patterns of spontaneous activity and stimulus-evoked response are similar in lower visual areas of the cerebral cortex, but gradually become independent, or “orthogonal,” as one moves from lower to higher visual areas.

The findings not only enhance our understanding of the mechanism that enables the brain to distinguish between signal and noise, but could also provide clues for developing noise-resistant artificial intelligence incorporating a mechanism similar to that found in the biological brain. The study is published in the journal Nature Communications.

“The brain is very noisy,” said Professor Kenichi Ohki of the Graduate School of Medicine. “It is constantly active even without any sensory inputs. Despite the noise, our sensory perception is very stable. We were interested in the mechanism by which the brain handles internally generated noise to achieve stable perception.”

An orthogonal, or independent, relationship between this internal brain noise and stimulus-related signals would explain how sensory perception remains stable.

In order to test which theory explains the relationship between brain noise and stimulus-related activity, researchers observed marmoset monkeys, which have a flat neocortex (the largest region in primate brains) that makes it easier to observe cortical areas involved in the brain’s higher functions. They injected a virus carrying a genetically encoded calcium indicator called GCaMP, which includes a green fluorescent protein that is bound to calcium ions that highlights brain activity on imaging scans.

At first, the spontaneous brain activity looked like waves with patchy spatial patterns. This patchy activity seems to be a general characteristic of primate brains. The spontaneous noise and the stimulus-related activity looked similar in lower cortical areas, which is consistent with previous research. However, as researchers looked closer at a higher cortical area, a part of the primate brain that helps monkeys process a moving image, there were less similarities between the two types of brain activity.

Cellular imaging and analysis of the neural activity found a hierarchy in place that helped separate brain noise and stimulus-related signals.

“The hierarchical structure of the cortical network is crucial for separating internal noise from sensory outputs. This separation process is called orthogonalization,” said now-Professor Teppei Matsui of the Graduate School of Brain Science at Doshisha University in Kyoto, who was lecturer at the University of Tokyo’s Graduate School of Medicine at the time of this research.

Looking ahead, researchers hope to continue to study the brain to understand this orthogonal relationship and hope to understand what this means for artificial intelligence. Unlike artificial neural networks, spontaneous activity is a characteristic feature of the biological brain.

“The next step is to identify neocortical neural circuits critical for the hierarchical orthogonalization,” said Ohki. “We are also hoping that the present finding contributes to developing new noise-resistant artificial intelligence.”

When the brain is observed through imaging, there is a lot of “noise,” which is spontaneous electrical activity that comes from a resting brain. This appears to be different from brain activity that comes from sensory inputs, but just how similar—or different—the noise is from the signal has been a matter of debate.

New research led by a team at the University of Tokyo further untangles the relationship between internally generated noise and stimulus-related patterns in the brain, and finds that the patterns of spontaneous activity and stimulus-evoked response are similar in lower visual areas of the cerebral cortex, but gradually become independent, or “orthogonal,” as one moves from lower to higher visual areas.

The findings not only enhance our understanding of the mechanism that enables the brain to distinguish between signal and noise, but could also provide clues for developing noise-resistant artificial intelligence incorporating a mechanism similar to that found in the biological brain. The study is published in the journal Nature Communications.

“The brain is very noisy,” said Professor Kenichi Ohki of the Graduate School of Medicine. “It is constantly active even without any sensory inputs. Despite the noise, our sensory perception is very stable. We were interested in the mechanism by which the brain handles internally generated noise to achieve stable perception.”

An orthogonal, or independent, relationship between this internal brain noise and stimulus-related signals would explain how sensory perception remains stable.

In order to test which theory explains the relationship between brain noise and stimulus-related activity, researchers observed marmoset monkeys, which have a flat neocortex (the largest region in primate brains) that makes it easier to observe cortical areas involved in the brain’s higher functions. They injected a virus carrying a genetically encoded calcium indicator called GCaMP, which includes a green fluorescent protein that is bound to calcium ions that highlights brain activity on imaging scans.

At first, the spontaneous brain activity looked like waves with patchy spatial patterns. This patchy activity seems to be a general characteristic of primate brains. The spontaneous noise and the stimulus-related activity looked similar in lower cortical areas, which is consistent with previous research. However, as researchers looked closer at a higher cortical area, a part of the primate brain that helps monkeys process a moving image, there were less similarities between the two types of brain activity.

Cellular imaging and analysis of the neural activity found a hierarchy in place that helped separate brain noise and stimulus-related signals.

“The hierarchical structure of the cortical network is crucial for separating internal noise from sensory outputs. This separation process is called orthogonalization,” said now-Professor Teppei Matsui of the Graduate School of Brain Science at Doshisha University in Kyoto, who was lecturer at the University of Tokyo’s Graduate School of Medicine at the time of this research.

Looking ahead, researchers hope to continue to study the brain to understand this orthogonal relationship and hope to understand what this means for artificial intelligence. Unlike artificial neural networks, spontaneous activity is a characteristic feature of the biological brain.

“The next step is to identify neocortical neural circuits critical for the hierarchical orthogonalization,” said Ohki. “We are also hoping that the present finding contributes to developing new noise-resistant artificial intelligence.”