Scientists Aid in Uncovering the Neural Mechanisms of Vision
When confronted with images that deviate from anticipated patterns, such as encountering a “do not enter” sign instead of the expected stop sign, how does the brain respond and adapt in contrast to exposure to images that align with predictions?
A team, including researchers from York University, embarked on a quest to address a fundamental question. The prevalent theory posits that the brain constructs a predictive model of the world, and this model is adjusted when incoming sensory data contradicts it.
However, the outcomes of the research, as detailed in the recently published paper, surprised the researchers, including Joel Zylberberg, Associate Professor at York Faculty of Science and co-corresponding author.
Long-Term Measurement of Top-Down Signals to Sensory Areas
According to Zylberberg, testing this theory presented challenges, necessitating the measurement of top-down signals to the brain’s sensory areas over extended periods.
To explore how the brain learns new sensory input patterns, the researchers employed a mouse model, displaying visual patterns over multiple days and introducing images that deviated from those patterns. The focus was on the visual cortex, where retina-processed visual information is managed.
Several researchers, including Zylberberg, are affiliated with the Canadian Institute for Advanced Research’s Learning in Machines and Brains group. This study was conducted as part of the Allen Institute for Brain Science’s Brain Observatory and OpenScope program, akin to an observatory for studying the brain through shared data, similar to astronomers collaborating to explore the universe.
Distal Apical Dendrites Gain Sensitivity to Pattern-Violating Signals Over Time
Measurements were taken at the distal apical dendrites and cell bodies of neurons in the visual cortex, assessing how they processed matching and pattern-violating signals. The results revealed a surprising evolution in the brain’s response to pattern-violating images over time, with distal apical dendrites becoming increasingly sensitive to such inputs, while cell bodies lost their initial strong sensitivity.
To conclude, Zylberberg, a computational neuroscientist, notes that this discovery could provide crucial insights into sensory computation and predictive learning in the brain. The findings suggest that different forms of pattern-violating stimuli may elicit distinct prediction errors, unveiling a previously unknown component of the brain’s role in sensory learning. This understanding holds significance for improving machine learning algorithms and applications, potentially contributing to advancements in restoring vision.
Read the original article on: Medical XPress
