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Untitled - Laboratory of Neurophysics and Physiology

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50:3171–3190<br />

• Hawkes (1971), Point Spectra <strong>of</strong> Some Mutually Exciting Point Processes, Journal <strong>of</strong> the Royal<br />

Statistical Society Series B 33(3):438–443<br />

• Helias et al. (2011), Towards a unified theory <strong>of</strong> correlations in recurrent neural networks, BMC<br />

Neuroscience 12(Suppl 1):P73<br />

• Helias et al. (2013), Echoes in correlated neural systems, New Journal <strong>of</strong> Physics 15(2):023002<br />

• Moreno-Bote & Parga (2006), Auto- <strong>and</strong> crosscorrelograms for the spike response <strong>of</strong> leaky<br />

integrate-<strong>and</strong>-fire neurons with slow synapses, PRL 96:028101<br />

• Ostojic et al. (2009), How Connectivity, Background Activity, <strong>and</strong> Synaptic Properties Shape<br />

the Cross-Correlation between Spike Trains, J Neurosci 29(33):10234–10253<br />

• Renart et al. (2010), The Asynchronous State in Cortical Circuits, Science 327(5965):587–590<br />

• Shadlen & Newsome (1998), The variable discharge <strong>of</strong> cortical neurons: implications for connectivity,<br />

computation, <strong>and</strong> information coding, J Neurosci 18:3870–96<br />

• Tetzlaff et al. (2012), Decorrelation <strong>of</strong> neural-network activity by inhibitory feedback, PLoS<br />

Comp Biol 8(8):e1002596, doi:10.1371/journal.pcbi.1002596<br />

• Zohary et al. (1994), Correlated Neuronal Discharge Rate <strong>and</strong> Its Implications for Psychophysical<br />

Performance, Nature 370:140–143<br />

T3<br />

Modeling <strong>and</strong> interpretation <strong>of</strong> extracellular potentials<br />

Space Grignard, (9.00-12.20; 13.30-16.50)<br />

Gaute T Einevoll, Norwegian University <strong>of</strong> Life Sciences, Ås, Norway<br />

Szymon Leski, Nencki Institute <strong>of</strong> Experimental Biology, Warsaw, Pol<strong>and</strong><br />

Espen Hagen, Norwegian University <strong>of</strong> Life Sciences, Ås, Norway<br />

While extracellular electrical recordings have been the workhorse in electrophysiology, the interpretation<br />

<strong>of</strong> such recordings is not trivial. The recorded extracellular potentials in general stem from a<br />

complicated sum <strong>of</strong> contributions from all transmembrane currents <strong>of</strong> the neurons in the vicinity <strong>of</strong><br />

the electrode contact. The duration <strong>of</strong> spikes, the extracellular signatures <strong>of</strong> neuronal action potentials,<br />

is so short that the high-frequency part <strong>of</strong> the recorded signal, the multi-unit activity (MUA),<br />

<strong>of</strong>ten can be sorted into spiking contributions from the individual neurons surrounding the electrode.<br />

However, no such simplifying feature aids us in the interpretation <strong>of</strong> the low-frequency part, the local<br />

field potential (LFP). To take a full advantage <strong>of</strong> the new generation <strong>of</strong> silicon-based multielectrodes<br />

recording from tens, hundreds or thous<strong>and</strong>s <strong>of</strong> positions simultaneously, we thus need to develop new<br />

data analysis methods grounded in the underlying biophysics. This is the topic <strong>of</strong> the present tutorial.<br />

In the first part <strong>of</strong> this tutorial, we will go through<br />

• the biophysics <strong>of</strong> extracellular recordings in the brain,<br />

39

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