1 / 16

Noise, Power Laws, and the Local Field Potential

Noise, Power Laws, and the Local Field Potential. Sloan-Swartz 2008 Summer Meeting. Joshua Milstein 1 , Florian Morman 1,2 , Itzhak Fried 2 and Christof Koch 1 1 California Institute of Technology 2 David Geffen School of Medicine and Semel Institute of Neuroscience and Human Behavior,

orvillem
Download Presentation

Noise, Power Laws, and the Local Field Potential

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Noise, Power Laws, and the Local Field Potential Sloan-Swartz 2008 Summer Meeting Joshua Milstein1, Florian Morman1,2, Itzhak Fried2 and Christof Koch1 1California Institute of Technology 2David Geffen School of Medicine and Semel Institute of Neuroscience and Human Behavior, University of California, Los Angeles

  2. Scaling Exponent () N = 106 546 16 16 Physical Motivation Human Intracranial Recordings

  3. Compartmental Model To generate the membrane currents Pyramidal hippocampal cell within rat CA1 3-D topological reconstruction • Hodgkin-Huxley Style Kinetics • Voltage dependent Na+, K+,Ca2+ currents • 12 different processes i (nA) NEURON Simulation Environment t (ms) Used to compare intracellular to extracellular recordings Henze (2000) & Gold (2006)

  4. Scale Invariance: Number of Earthquakes/Year Earthquake Magnitude Power Laws Power/Slope

  5. Electron Shot Noise Pulse Amplitude Time

  6. Stochastic Variable: tk1 tk2 tk3 tk4 tk5 tk6 tk7 Time Neuron Shot Noise Spike Timing Pulse Shape

  7. Autocorrelation Function Wiener-Kinchin Theorem: Power Spectrum Autocorrelation Function

  8. Simple Case I: Uncorrelated, Slow Synaptic Pulses

  9. Pulse Amplitude Time Contains All Time/Frequency Dependence Simple Case II: Sharp Spike

  10. Binary Sequence: 10011010011010010001001100000100010111 White Noise Independent at each timestep

  11. Autocorrelation Function: Power Spectrum: Brown(ian) Noise

  12. Amplitude Timestep Spike Train White Noise ?!? Random Walk with a Threshold

  13. Autocorrelation Function: Let and Telegraph Process

  14.  = -2

  15. Summary Experimental Evidence for a Universal 1/f^2 Scaling in the LFP of Humans 2. Developed a Simple Mathematical Treatment for Understanding Power Laws in the LFP 3. Brownian Noise Can Arise From Single Neuron Activity Biophysical Examples: a. Sharp spikes followed by slow decay b. UP-DOWN states of activity ** Funded by the Swartz Foundation **

More Related