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Electrophysiology of neurons

Electrophysiology of neurons. Some things to remember…. Electrical properties of a (simplified) single cell. Ligand -gated ion chanels alter permeability to Na + ,K + , Cl - during generation of synaptic potentials.

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Electrophysiology of neurons

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  1. Electrophysiology of neurons

  2. Some things to remember…

  3. Electrical properties of a (simplified) single cell Ligand-gated ion chanelsalter permeability to Na+,K+ , Cl- during generation of synaptic potentials Differences in ion concentrations set up by Na+-K+ATPase pump – high [K+], low [Na+, Cl-] inside cell; high [Na+, Cl-] , low [K+] outside cell Permeability of membrane to these ions determines the membrane potential Voltage-gated ion channels alter permeability to Na+ and K+ during generation of action potential

  4. Electrical properties of a (simplified) single cell SUMMED input from (tens of) thousands of synapses: continuous process synaptic potentials 20pA 40ms action potentials ALL or NOTHING: binary, point process

  5. Cells differ from one another – morphologically Scale bar = 100 microns Segev, 1998

  6. Cells differ from one another – electrically

  7. There are lots of them (≈ 5-10 million cells in a 3x3x3mm voxel) Buzsaki, 2004

  8. What do we want to know about?

  9. Intracellular recording Aims to establish something about the properties of single cell, e.g. membrane properties or properties of synapse Needs an electrode whose tip is smaller than the cell! (typically 50-500 nm) a lot of mechanical stability

  10. Classic example – miniature synaptic potentials Fatt & Katz, 1952

  11. Patch clamping after Neher & Sakmann, 1970s

  12. State of the art – in vivo patch clamp Bruno & Sakmann, 2006

  13. Intracellular recording: pros and cons  permits measurement of synapses/membrane properties • we can fill the cell with a dye (and reconstruct it afterwards) difficult to obtain in vivo recordings (normally anaesthetised)  cell damage  affects physiology Sjostrom and Hausser, UCL (also state of the art!)

  14. Extracellular recording Aims to record firing patterns of a cell, typically with respect to environment/behaviour Needs electrode that will remain stable during recording – less stringent than intracellular so in vivo recording more straightforward May need spike sorting to differentiate cells recorded on same electrode

  15. Classic example – visual cortex Hubel & Wiesel, 1960s

  16. State of the art – juxtacellular recording after Pinault et al., 1996

  17. State of the art – juxtacellular recording Ungless et al., 2004

  18. Extracellular recording – pros and cons  can use in awake, behaving animals difficult to know which cell you’re recording (juxtacellular technique has low yield)  may bias sampling when listening for ‘noisy’ cells/cells with certain response property  spike variability assumed to be noise, when it might not be…

  19. Multi-unit recording Aims to record activity of populations of cells stimultaneously Needs some clever maths and technology to pick out the individual voices in the chorus

  20. Tetrodes Buzsaki, 2004

  21. Silicon probes Buzsaki, 2004

  22. Example – spike cross-correlograms Fujisawa et al., 2008

  23. Two-photon calcium imaging Ohki et al., 2006

  24. Multi-unit recording – pros and cons • can begin to ask sophisticated questions about populations carrying meaningful information (acting as ‘cell assemblies’) • can examine the interactions between cells and how these change during task can never label cells (although can identify putative interneurons/excitatory cells) limited by how well we can separate units from one another

  25. Local field potential Aims to record gross current flow in extracellular space Reflects synaptic inputs into dendritic trees with particular orientations – so low frequency cf. action potentials (typically lowpass filter at 300Hz)

  26. LFP and cortical depth

  27. Current source density analysis Mitzdorf, 1985

  28. Relationship between LFPs and EEG: confusing! Mitzdorf, 1985

  29. Phase-locking between LFP oscillations and spike timing of different cells Klausberger et al., 2008

  30. What do we want to know about?

  31. Relating neural activity to BOLD fMRI signals Red = BOLD fMRI timecourse Blue = LFP Green = single unit spiking Logothetis, 2001

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