Ex the brain is a network there are networks all

This preview shows page 27 - 29 out of 32 pages.

Ex: The brain is a “network”. There are networks all around us social networks infrastructure networks ASIDE: Network Terminology A network has two components nodes edges (connect nodes) Ex: social network node: you edge: friendship Ex: The brain is a network: two classes – structural and functional structural – anatomical connections functional nodes: brain regions | edges: similarity in the activity that the nodes produce Ex: The brain is a “small world network”. Neighborly with short-cuts interesting properties short path length & clustered Many real-world networks are small world networks. Why is this useful for the brain? 2. Data analysis Use computational tools to characterize observed brain activity. Use sophisticated tools to characterize EEG activity. The power spectrum Help us identify important rhythms Vital: we're overrun with data Ex: Multielectrode array 100 electrodes, 10 kHz, 24 hrs > 80 billion data points How do we make sens of these rich, overwhelming data? Will continue to become more vital Even higher density recordings 3. Modeling Mathematical equations to mimic an observed system. Ex: single neuron activity Make it simpler Ex: binary representation New model: a statistical model of spiking A rule we use to determine whether a neuron spikes at each instant of time (e.g., a coin flip) Idea: Abandon some biological realism for model simplification. Requires balance Make it complicated. Ex: Single neuron – We want to capture the biological details
Image of page 27

Subscribe to view the full document.

December 5, 2012: Introduction to Computational Neuroscience (cont.) Application of computational neuroseicne: Multi-scale seizure dynamics Data Temporal scales Spatial scales Why do seizures stop? Why do seizures fail to stop? Macroscale Data: Invasive EEG or EcoG electrodes but directly on brain multivariate, high density 100 electrodes sampling 500 Hz purpose: localize seizure focus standard analysis: visual inspection How to characterize these data? EcoG Data: visual inspection onset: “increased activity” cessation: “no activity” Why do seizures stop? Temporal scales: rhythms one electrodes beginning: fast rhythmic patterning middle: convulsions – larger amplitude, intermediate rhythmic pattern end: Ictal chirp - slow rhythmic pattern quantify: time-frequency spectra color represents amplitude Hypothesis: rhythms slow during seizurse Spatial scales: coupling plot voltage of two electrodes Are they coupled? - Related in some way? Yes – draw a line (an edge) that display similar brain activity We employ cross correlation functional network Results Networks evolving in time more connections at beginning & end of seizure as opposed to middle Data overload: Succinct measure to characterize network evolution.
Image of page 28
Image of page 29
  • Fall '12
  • PaulLipton
  • cells

{[ snackBarMessage ]}

Get FREE access by uploading your study materials

Upload your study materials now and get free access to over 25 million documents.

Upload now for FREE access Or pay now for instant access
Christopher Reinemann
"Before using Course Hero my grade was at 78%. By the end of the semester my grade was at 90%. I could not have done it without all the class material I found."
— Christopher R., University of Rhode Island '15, Course Hero Intern

Ask a question for free

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern