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NE101 Lecture Notes

Ex the brain is a network there are networks all

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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
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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.
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