title: Detecting dynamical changes within a simulated neural ensemble using a measure of representational quality.
abstract: Technological advances allowing simultaneous recording of neuronal ensembles have led to many developments in our understanding of how the brain performs neural computations. One key technique for extracting information from neural populations has been population reconstruction. While reconstruction is a powerful tool, it only provides a value and gives no indication of the quality of the representation itself. In this paper, we present a mathematically and statistically justified measure for assessing the quality of a representation in a neuronal ensemble. Using a simulated neural network, we show that this measure can distinguish between system states and identify moments of dynamical change within the system. While the examples used in this paper all derive from a standard network model, the measure itself is very general. It requires only a representational space, measured tuning curves, and neural ensembles.
authors.affiliation: Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA.
authors.forename: Jadin C
authors.initials: JC
authors.lastname: Jackson
authors.forename: A David
authors.initials: AD
authors.lastname: Redish
mesh_terms: D000818:Animals; D003198:Computer Simulation; D001330:Electronic Data Processing; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons; D017711:Nonlinear Dynamics; D011336:Probability; D012107:Research Design; D012399:Rotation; D013269:Stochastic Processes; D013997:Time Factors