Our brain is like a busy factory constantly processing a wide range of visual stimuli. It’s fascinating how our neurons transform these stimuli and how they change depending on our internal and behavioral states. Scientists wondered if they could predict what we’re seeing based on our brain activity. To find out, they used computational models to analyze datasets from the Allen Brain Institute. They discovered that different brain regions have varying abilities to extract visual information. The visual cortical areas scored the highest, while the hippocampal regions performed close to chance. Interestingly, the researchers also found that changes in behavior affected decoding accuracy, making it more challenging for the models to predict what we were seeing. They suspect this is because our brain activity is influenced by both the stimuli and our behavior. Additionally, they were able to reconstruct movie frames from neural activity using linear classifiers! However, they found that similarity between reconstructed and actual frames was higher when the models were trained and tested on similar behavioral states. This study provides valuable insights into how visual coding works in the mouse brain across different areas and states.

IntroductionOur brain is bombarded by a diverse range of visual stimuli, which are converted into corresponding neuronal responses and processed throughout the visual system. The neural activity patterns that result from these external stimuli vary depending on the object or scene being observed, but they also change as a result of internal or behavioural states. This raises the question of to what extent it is possible to predict the presented visual stimuli from neural activity across behavioural states, and how this varies in different brain regions.MethodsTo address this question, we assessed the computational capacity of decoders to extract visual information in awake behaving mice, by analysing publicly available standardised datasets from the Allen Brain Institute. We evaluated how natural movie frames can be distinguished based on the activity of units recorded in distinct brain regions and under different behavioural states. This analysis revealed the spectrum of visual information present in different brain regions in response to binary and multiclass classification tasks.ResultsVisual cortical areas showed highest classification accuracies, followed by thalamic and midbrain regions, with hippocampal regions showing close to chance accuracy. In addition, we found that behavioural variability led to a decrease in decoding accuracy, whereby large behavioural changes between train and test sessions reduced the classification performance of the decoders. A generalised linear model analysis suggested that this deterioration in classification might be due to an independent modulation of neural activity by stimulus and behaviour. Finally, we reconstructed the natural movie frames from optimal linear classifiers, and observed a strong similarity between reconstructed and actual movie frames. However, the similarity was significantly higher when the decoders were trained and tested on sessions with similar behavioural states.ConclusionOur analysis provides a systematic assessment of visual coding in the mouse brain, and sheds light on the spectrum of visual information present across brain areas and behavioural states.

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