Causal entropies

Causal entropies—A measure for determining changes in the temporal organization of neural systemsJack Waddella, Rhonda Dzakpasua, Victoria Boothb, c, Brett Rileyb, Jonathan Reasord, Gina Poeb, e and Michal Zochowskia, f, Corresponding Author Contact Information, E-mail The Corresponding AuthoraDepartment of Physics, University of Michigan, Ann Arbor, MI 48109-1040, USAbDepartment of Anesthesiology, University of Michigan, Ann Arbor, MI 48109-0615, USAcDepartment of Mathematics, University of Michigan, Ann Arbor, MI 48109, USAdDepartment of Neurology, University of Michigan, Ann Arbor, MI 48109, USAeDepartment of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI 48109-0615, USAfBiophysics Research Division, University of Michigan, 450 Church St., Ann Arbor, MI 48109, USAReceived 12 April 2006; revised 4 November 2006; accepted 14 December 2006. Available online 22 December 2006.AbstractWe propose a novel measure to detect temporal ordering in the activity of individual neurons in a local network,next term 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.Keywords: Temporal pattern formation; Multiunit recording; Hippocampal CA1; Long term potentiation; Asymmetric correlation

ScienceDirect - Journal of Neuroscience Methods : Causal entropies—A measure for determining changes in the temporal organization of neural systems