|14:00||Michael J. Barber||Neural belief propagation without multiplication||
Neural network models can be derived from the hypothesis that populations of neurons perform statistical inference. Such networks can be generated from a broad class of probabilistic models, but often function through the multiplication of neural firing rates. By introducing additional assumptions about the nature of the probabilistic models, we derive a class of neural networks that function only through weighted sums of neural activities.
|14:30||Arnaud Buhot||Strong and fragile glassy behaviour in kinetically constrained systems|
|15:45||Michael J. Barber||Noise-induced signal enhancement in heterogeneous neural networks||
Neural networks can represent complex functions, but are often constructed of very simple units. We investigate the limitations imposed by such a simple unit, the McCulloch-Pitts neuron. We explore the role of stochastic resonance in units of finite precision and show how to construct neural networks that overcome the limitations of single units.
|16:15||Sebastian Risau-Gusman||Typical properties of Soft Margin Classifiers|