Probability of repeating patterns in simultaneous neural data.
Large data sets arising from neurophysiological experiments are frequently observed with repeating temporal patterns. Our ability to decode these patterns is dependent on the development of methods to assess whether the patterns are significant or occurring by chance. Given a hypothesized sequence within these data, we derive probability formulas to allow assessment of the likelihood of recurrence occurring by chance. We illustrate our approach using data from hippocampal neurons from awake, behaving rats.
Smith
Anne C
AC
Department of Anesthesiology and Pain Medicine, University of California, Davis, Davis, CA 95616, USA. annesmith@ucdavis.edu
Nguyen
Vinh Kha
VK
Karlsson
Mattias P
MP
Frank
Loren M
LM
Smith
Peter
P
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation / Q000592:standards; D006624:Hippocampus / Q000502:physiology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D009949:Orientation / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data*; D051381:Rats; D012815:Signal Processing, Computer-Assisted; D013028:Space Perception / Q000502:physiology