An important problem in computational neuroscience is to understand how networks of
spiking neurons can carry out various computations underlying behavior. Balanced spiking …

A number of recent studies have sought to understand the behavior of artificial and biological
neural networks by comparing representations across layers, networks and brain areas. …

An important problem in computational neuroscience is to understand how networks of
spiking neurons can carry out various computations underlying behavior. Balanced spiking …

The generative model for our observations is defined as s= Ax+ ε. We assume that the latent
x is normally distributed, N (µx, Σx), and that it has dimensionality D. We further assume that …

… by Rullán Buxó and Pillow [24]. Briefly, the recurrent circuit embeds Langevin sampling
dynamics into a population of spiking neurons so that samples can be read out from the …

A number of recent studies have sought to understand the behavior of both artificial and
biological neural networks by comparing representations across layers, networks and brain …

In sampling-based Bayesian models of brain function, neural activities are assumed to be
samples from probability distributions that the brain uses for probabilistic computation. …

Understanding how the brain constructs statistical models of the sensory world remains a
longstanding challenge for computational neuroscience. Here, we derive an unsupervised …

… In Supplemental Figure E.1 we show the full time course of the inferred statistics for one set
of network parameters and in Supplemental Figure E.3 we show the statistics after the full 1.5…