Learning Invariant Features in Modulatory Networks through Conflict and Ambiguity.

This work lays the foundation for a framework of cortical learning based on the idea of a competitive column, which is inspired by the functional organization of neurons in the cortex. A column describes a prototypical organization for neurons that gives rise to an ability to learn scale, rotation, and translation-invariant features. This is empowered by a recently developed learning rule, conflict learning, which enables the network to learn over both driving and modulatory feedforward, feedback, and lateral inputs.

Biologically plausible learning in neural networks with modulatory feedback.

Although Hebbian learning has long been a key component in understanding neural plasticity, it has not yet been successful in modeling modulatory feedback connections, which make up a significant portion of connections in the brain. We develop a new learning rule designed around the complications of learning modulatory feedback and composed of three simple concepts grounded in physiologically plausible evidence. Using border ownership as a prototypical example, we show that a Hebbian learning rule fails to properly learn modulatory connections, while our proposed rule correctly learns a stimulus-driven model.

An enhanced exercise and cognitive programme does not appear to reduce incident delirium in hospitalised patients: a randomised controlled trial.

Objective: To determine if a programme of progressive resistance exercise, mobilisation and orientation, in addition to usual care, was superior to usual care alone in the prevention of incident delirium in older hospitalised patients.

Design: A randomised controlled trial.

Setting: The study was performed at a secondary referral hospital in Melbourne, Australia between May 2005 and December 2007.