Topic: neural population responses

The statistical structure of neural population responses is a topic of intense interest in systems neuroscience. The signal eigenspectrum (eigenvalues ordered from high to low) is a fundamental summary statistic that characterizes the dimensionality and efficiency or redundancy of population tuning curves. However, the problem of characterizing signal eigenvalue spectra from limited, noisy neural response data is complicated by two factors. First, noise in the neural responses (aka ``noise correlations'') can interfere with the estimation of the eigenvalues of the signal distribution. Second,

The primate ventral visual system is critical for object recognition, but how its neural representations change during adult categorization learning remains debated. Some studies suggest that neural responses of visual areas change with training, e.g., during perceptual learning. Others propose that behavioral changes are readily explained by adjustments in the readout of information by downstream frontoparietal regions, with sensory regions remaining largely unchanged. Here, we investigated changes in neural representations in macaque visual areas V4 and IT during the learning of novel catego