In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimi...
Jean-Pascal Pfister, Taro Toyoizumi, David Barber,...
Recent advances in the technology of multi-unit recordings make it possible to test Hebb's hypothesis that neurons do not function in isolation but are organized in assemblie...
Laura Martignon, Gustavo Deco, Kathryn B. Laskey, ...
We describe a new method for performing a nonlinear form of Principal Component Analysis. By the use of integral operator kernel functions, we can e ciently compute principal comp...
It is well-known that, in unidentifiable models, the Bayes estimation provides much better generalization performance than the maximum likelihood (ML) estimation. However, its ac...
We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and e...