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IEEE Brain: A Large-scale Standardized Physiological Pipeline Reveals Functional Organization of the Mouse Visual Cortex
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IEEE Brain: A Large-scale Standardized Physiological Pipeline Reveals Functional Organization of the Mouse Visual Cortex
An important open question in visual neuroscience is how visual information is represented in cortex. Important results characterized neural coding by assessing the responses to artificial stimuli, with the assumption that responses to gratings, for example, capture the key features of neural responses, and deviations, such as extra-classical effects, are relatively minor. The failure of these responses to have strong predictive power has renewed these questions. It has been suggested that this characterization of visual responses has been strongly influenced by the biases inherent in recording methods and the limited stimuli used in experiments. In creating the Allen Brain Observatory, we sought to reduce these biases by recording large populations of neurons in the mouse visual cortex using a broad array of stimuli, both artificial and natural. This open dataset is a large-scale, systematic survey of physiological activity in the awake mouse cortex recorded using 2-photon calcium imaging. Neural activity was recorded in cortical neurons of awake mice who were presented a variety of visual stimuli, including gratings, noise, natural images, and natural movies. This dataset consists of over 63,000 neurons recorded in over 1300 imaging sessions, surveying 6 cortical areas, 4 cortical layers, and 14 transgenically defined cell types (Cre lines). We found that visual responses throughout the mouse cortex are highly variable. Using the joint reliabilities of responses to multiple stimuli, we classify neurons into functional classes and validate this classification with models of visual responses. Only 10% of neurons in the mouse visual cortex show reliable responses to all of the stimuli used, and are reasonably well predicted by linear-nonlinear models. The remaining neurons fall into classes characterized by responses to specific subsets of the stimuli and the neurons in the largest class do not reliably responsive to any of the stimuli. These classes reveal a functional organization within the mouse visual cortex wherein putative dorsal areas show specialization for visual motion signals.
An important open question in visual neuroscience is how visual information is represented in cortex. Important results characterized neural coding by assessing the responses to artificial stimuli, with the assumption that responses to gratings, for example, capture the key features of neural responses, and deviations, such as extra-classical effects, are relatively minor. The failure of these responses to have strong predictive power has renewed these questions. It has been suggested that this characterization of visual responses has been strongly influenced by the biases inherent in recording methods and the limited stimuli used in experiments. In creating the Allen Brain Observatory, we sought to reduce these biases by recording large populations of neurons in the mouse visual cortex using a broad array of stimuli, both artificial and natural. This open dataset is a large-scale, systematic survey of physiological activity in the awake mouse cortex recorded using 2-photon calcium imaging. Neural activity was recorded in cortical neurons of awake mice who were presented a variety of visual stimuli, including gratings, noise, natural images, and natural movies. This dataset consists of over 63,000 neurons recorded in over 1300 imaging sessions, surveying 6 cortical areas, 4 cortical layers, and 14 transgenically defined cell types (Cre lines). We found that visual responses throughout the mouse cortex are highly variable. Using the joint reliabilities of responses to multiple stimuli, we classify neurons into functional classes and validate this classification with models of visual responses. Only 10% of neurons in the mouse visual cortex show reliable responses to all of the stimuli used, and are reasonably well predicted by linear-nonlinear models. The remaining neurons fall into classes characterized by responses to specific subsets of the stimuli and the neurons in the largest class do not reliably responsive to any of the stimuli. These classes reveal a functional organization within the mouse visual cortex wherein putative dorsal areas show specialization for visual motion signals.