Principle Component Analysis (PCA) is used by many but understood by few.
In this talk I hope to explain some of the math behind the tool, to raise awareness for its potential (and pitfalls) and to discuss the relationship between PCA and cluster analysis. In addition I will present some examples of its use in neuro-science, from my own work in robotics and an application in economics.
Date: 21/10/2016
Time: 16:00
Location: LB252
olfaction synaptic plasticity robotics machine learning Associative memory Purkinje cell Computer Science Neuromorphic hardware neural networks neuroscience Convolutional Neural Networks PCA Neuronal Morphology optimization Evolutionary algorithms Dendritic computation Persistent homology Deep learning computational modelling Open position cerebellum Neurons computational neuroscience Software development COVID-19 Network connectivity Studentship Homoeostasis Structural plasticity Artificial intelligence