Our understanding of neural population coding has been limited by a lack of analysis methods to characterize spiking data from large populations. The biggest challenge comes from the fact that the number of possible network activity patterns scales exponentially with the number of neurons recorded ([Formula: see text]). Here we introduce a new statistical method for characterizing neural population activity that requires semi-independent fitting of only as many parameters as the square of the number of neurons, requiring drastically smaller data sets and minimal computation time. The model works by matching the population rate (the number of neurons synchronously active) and the probability that each individual neuron fires given the population rate. We found that this model can accurately fit synthetic data from up to 1000 neurons. We also found that the model could rapidly decode visual stimuli from neural population data from macaque primary visual cortex about 65 ms after stimulus onset. Finally, we used the model to estimate the entropy of neural population activity in developing mouse somatosensory cortex and, surprisingly, found that it first increases, and then decreases during development. This statistical model opens ...Continue Reading
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BRAIN Initiative Cell Census Network (BICCN)
The BRAIN Initiative Cell Census Network aims to identify and provide experimental access to the different brain cell types to determine their roles in health and disease. Discover the latest research from researchers in the BRAIN Initiative Cell Census Network here.
Here is the latest research on barrel cortex, a region of somatosensory and motor corticies in the brain, which are used by animals that rely on whiskers for world exploration.