title: Extracting information from neuronal populations: information theory and decoding approaches.
abstract: To a large extent, progress in neuroscience has been driven by the study of single-cell responses averaged over several repetitions of stimuli or behaviours. However,the brain typically makes decisions based on single events by evaluating the activity of large neuronal populations. Therefore, to further understand how the brain processes information, it is important to shift from a single-neuron, multiple-trial framework to multiple-neuron, single-trial methodologies. Two related approaches--decoding and information theory--can be used to extract single-trial information from the activity of neuronal populations. Such population analysis can give us more information about how neurons encode stimulus features than traditional single-cell studies.
authors.affiliation: Department of Engineering, University of Leicester, Leicester, LE1 7RH, UK. rqqg1@leicester.ac.uk
authors.forename: Rodrigo
authors.initials: R
authors.lastname: Quian Quiroga
authors.forename: Stefano
authors.initials: S
authors.lastname: Panzeri
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology*; D006801:Humans; D007257:Information Theory; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D009482:Neurophysiology / Q000379:methods*; D012815:Signal Processing, Computer-Assisted