title: Causal entropies--a measure for determining changes in the temporal organization of neural systems.
abstract: We propose a novel measure to detect temporal ordering in the activity of individual neurons in a local network, which is thought to be a hallmark of activity-dependent synaptic modifications during learning. The measure, called causal entropy, is based on the time-adaptive detection of asymmetries in the relative temporal patterning between neuronal pairs. We characterize properties of the measure on both simulated data and experimental multiunit recordings of hippocampal neurons from the awake, behaving rat, and show that the metric can more readily detect those asymmetries than standard cross correlation-based techniques, especially since the temporal sensitivity of causal entropy can detect such changes rapidly and dynamically.
authors.affiliation: Department of Physics, University of Michigan, 450 Church Street, Ann Arbor, MI 48109-1040, USA.
authors.forename: Jack
authors.initials: J
authors.lastname: Waddell
authors.forename: Rhonda
authors.initials: R
authors.lastname: Dzakpasu
authors.forename: Victoria
authors.initials: V
authors.lastname: Booth
authors.forename: Brett
authors.initials: B
authors.lastname: Riley
authors.forename: Jonathan
authors.initials: J
authors.lastname: Reasor
authors.forename: Gina
authors.initials: G
authors.lastname: Poe
authors.forename: Michal
authors.initials: M
authors.lastname: Zochowski
mesh_terms: D000064:Acclimatization; D000818:Animals; D006624:Hippocampus / Q000502:physiology; D008297:Male; D018782:Maze Learning; D009474:Neurons / Q000502:physiology*; D011336:Probability; D017966:Pyramidal Cells / Q000502:physiology*; D051381:Rats; D011916:Rats, Inbred F344