The cooperative behaviour of interacting neurons and synapses is studied using models and methods from statistical physics. The competition between training error and entropy may lead to discontinuous properties of the neural network. This is demonstrated for a few examples: perceptron, associative memory, learning from examples, generalization, multilayer networks, structure recognition, Bayesian estimate, on-line training, noise estimation and time series generation.
The exchange of ideas between statistical physics and computer science has been very fruitful and is currently gaining momentum as a consequence of the revived interest in neural networks, machine learning and inference in general. Statistical physics methods complement other approaches to the theoretical understanding of machine learning processes and inference in stochastic modeling. They facilitate, for instance, the study of dynamical and equilibrium properties of randomized training processes in model situations. At the same time, the approach inspires novel and efficient algorithms and facilitates interdisciplinary applications in a variety of scientific and technical disciplines.
Federal and State housing policies have changed the face of public housing, which is increasingly becoming the accommodation of last resort. This social residualisation of public housing in Australia and elsewhere has meant that sustaining tenancies is a challenge for public housing authorities because of the need to balance the sometimes conflicting objectives of social outcomes for tenants with difficult behaviours and the neighbours and communities who are negatively impacted upon by their behaviour, to say nothing of the need for sustainability outcomes for business managers in social housing. The difficulties of meeting this challenge not only increase the risk of homelessness for these vulnerable tenants through their eviction, but may also threaten the tenancies of those affected by their behaviours.