One of the central questions of perception is how organisms reliably estimate hidden or abstract quantities ofinterest usingnoisyandambiguous sensory information. Almost equally important is representing the reliability of these estimates, especially in complex environments and situations ofrisk,where theuncertainty associated withachoice mayradically change theoptimal course of action.

We also develop a new auto-encoding algorithm that finds a latent space to disentangle latent input coordinates intowell-separated clusters, which isamenable tooursample complexityanalysis.

We also develop a new auto-encoding algorithm that finds a latent space to disentangle latent input coordinates intowell-separated clusters, which isamenable tooursample complexityanalysis.

When learning tasks over time, artificial neural networks suffer from aproblem known as Catastrophic Forgetting (CF). This happens when the weights of a network are overwritten during the training of a new task causing forgetting of oldinformation.