A central open question of computational neuroscience is to identify the data structures and algorithms that are used in mammalian cortex to support successive acts of the basic cognitive tasks of memorization and association. This paper addresses the simultaneous challenges of realizing these two distinct tasks with the same data ... more >>>

It had previously been shown that generic cortical microcircuit
models can perform complex real-time computations on continuous
input streams, provided that these computations can be carried out
with a rapidly fading memory. We investigate in this article the
computational capability of such circuits in the ... more >>>

Circuits composed of threshold gates (McCulloch-Pitts neurons, or
perceptrons) are simplified models of neural circuits with the
advantage that they are theoretically more tractable than their
biological counterparts. However, when such threshold circuits are
designed to perform a specific computational task they usually
differ ... more >>>

Motivated by the resurgence of neural networks in being able to solve complex learning tasks we undertake a study of high depth networks using ReLU gates which implement the function $x \mapsto \max\{0,x\}$. We try to understand the role of depth in such neural networks by showing size lowerbounds against ... more >>>