Topic: variance

Gradient descent is a central tool in modern machine learning, largely because it scales well to train large networks on computationally challenging tasks. As such, computational neuroscientists are exploring potential means by which the brain could leverage gradient descent. However, it is unlikely that the brain could implement perfect gradient descent, but instead could rely on using approximations of the true gradient signal. Therefore, it is likely that the gradient estimates used by the brain would have both bias and variance. Here, we use model agnostic mathematical analysis and simulat

The ability to simultaneously record the activity from tens to thousands and maybe even tens of thousands of neurons has allowed us to analyze the computational role of population activity as opposed to single neuron activity. Recent work on a variety of cortical areas suggests that neural function may be built on the activation of population-wide activity patterns, the neural modes, rather than on the independent modulation of individual neural activity. These neural modes, the dominant covariation patterns within the neural population, define a low dimensional neural manifold that captures m