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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."
[2]
How the Brain Sorts Noise from Signal to Maintain Stable Perception - Neuroscience News
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. 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." 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.
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A new study on marmoset monkeys uncovers how the brain distinguishes between internal noise and sensory signals, potentially influencing the development of noise-resistant AI.
A groundbreaking study led by researchers at the University of Tokyo has shed light on how the primate brain distinguishes between internally generated noise and sensory signals, a finding that could have significant implications for artificial intelligence development 12.
The brain is constantly active, generating spontaneous electrical activity even in the absence of sensory inputs. This "noise" has long puzzled scientists, who have debated its relationship to stimulus-related brain activity. Professor Kenichi Ohki of the Graduate School of Medicine at the University of Tokyo explains, "The brain is very noisy. It is constantly active even without any sensory inputs. Despite the noise, our sensory perception is very stable" 1.
To investigate this phenomenon, the research team used marmoset monkeys, whose flat neocortex allows for easier observation of cortical areas involved in higher brain functions. They employed a novel technique involving a genetically encoded calcium indicator called GCaMP, which highlights brain activity on imaging scans 1.
The study revealed a fascinating hierarchical structure in the brain's cortical network. In lower visual areas of the cerebral cortex, patterns of spontaneous activity and stimulus-evoked responses were similar. However, as researchers examined higher visual areas, these patterns gradually became independent or "orthogonal" 2.
Professor Teppei Matsui, now at Doshisha University, elaborates: "The hierarchical structure of the cortical network is crucial for separating internal noise from sensory outputs. This separation process is called orthogonalization" 1.
This discovery not only enhances our understanding of brain functionality but also holds promise for developing more advanced artificial intelligence systems. Unlike current AI models, biological brains have a unique capacity to manage complex, spontaneous activity 2.
Professor Ohki suggests, "We are hoping that the present finding contributes to developing new noise-resistant artificial intelligence" 1. This could lead to AI systems that more closely mimic the brain's ability to maintain stable perception despite internal noise.
The research team plans to delve deeper into this phenomenon. "The next step is to identify neocortical neural circuits critical for the hierarchical orthogonalization," says Ohki 1. This continued exploration could further bridge the gap between neuroscience and artificial intelligence, potentially revolutionizing both fields.
As we unravel more mysteries of the brain's intricate workings, we inch closer to creating AI systems that can rival the remarkable capabilities of biological neural networks in processing information amidst noise.
Reference
[1]
Medical Xpress - Medical and Health News
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