Complex neural dynamics produced by the recurrent architecture of neocortical circuits is
critical to the cortex's computational power. However, the synaptic learning rules underlying …

Dynamic excitatory-inhibitory (EI) balance is a paradigmatic mechanism invoked to explain
the irregular low firing activity observed in the cortex. However, we will show that the EI …

Neural dynamics within recurrent cortical networks is an important component of neural
processing. However, the learning rules that allow networks composed of hundreds or …

How do neural networks learn to represent information? Here, we address this question by
assuming that neural networks seek to generate an optimal population representation for a …

Neural computations emerge from local recurrent neural circuits or computational units such
as cortical columns that comprise hundreds to a few thousand neurons. Continuous …

The importance of the large number of thin-diameter and unmyelinated axons that connect
different cortical areas is unknown. The pronounced propagation delays in these axons may …

Brain computational challenges vary between behavioral states. Engaged animals react
according to incoming sensory information, while in relaxed and sleeping states …

Neural activity in awake behaving animals exhibits a vast range of timescales that can be
several fold larger than the membrane time constant of individual neurons. Two types of …

Action potential generation (spiking) in the neocortex is organized into repeating non-
random patterns during both awake experiential states and non-engaged states ranging …

The ability to acquire and maintain appropriate representations of time-varying, sequential
stimulus events is a fundamental feature of neocortical circuits and a necessary first step …