Summary: New research reveals how the brain separates internally generated noise from sensory signals, ensuring stable perception. The study shows that in lower visual areas, spontaneous brain activity and stimulus-evoked responses are similar, but in higher cortical areas, they become increasingly independent, a process known as orthogonalization.
Using marmoset monkeys and calcium imaging, researchers discovered a hierarchical structure in the brain’s cortical network responsible for this separation. This finding not only deepens our understanding of brain functionality but also holds promise for developing noise-resistant artificial intelligence.
The team aims to identify the neural circuits responsible for this process and explore how these insights can influence AI design. The study underscores the unique capacity of biological brains to manage complex, spontaneous activity.
Key Facts:
- Spontaneous brain activity resembles stimulus-driven signals in lower visual areas but becomes independent in higher cortical areas.
- The separation process, called orthogonalization, relies on the brain’s hierarchical cortical network.
- Insights could lead to the development of noise-resistant AI mimicking biological brains.
Source: University of Tokyo
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.”
About this sensory perception and neuroscience research news
Author: Teppei Matsui
Source: University of Tokyo
Contact: Teppei Matsui – University of Tokyo
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Orthogonalization of spontaneous and stimulus-driven activity by hierarchical neocortical areal network in primates” by Teppei Matsui et al. Nature Communications
Abstract
Orthogonalization of spontaneous and stimulus-driven activity by hierarchical neocortical areal network in primates
How biological neural networks reliably process information in the presence of spontaneous activity remains controversial. In mouse primary visual cortex (V1), stimulus-evoked and spontaneous activity show orthogonal (dissimilar) patterns, which is advantageous for separating sensory signals from internal noise.
However, studies in carnivore and primate V1, which have functional columns, have reported high similarity between stimulus-evoked and spontaneous activity.
Thus, the mechanism of signal-noise separation in the columnar visual cortex may be different from that in rodents.
To address this issue, we compared spontaneous and stimulus-evoked activity in marmoset V1 and higher visual areas. In marmoset V1, spontaneous and stimulus-evoked activity showed similar patterns as expected.
However, in marmoset higher visual areas, spontaneous and stimulus-evoked activity were progressively orthogonalized along the cortical hierarchy, eventually reaching levels comparable to those in mouse V1.
These results suggest that orthogonalization of spontaneous and stimulus-evoked activity is a general principle of cortical computation.
