1. 26-11-04Neuro_Informatics Workshop NeuroInformatics : Bridging the gap between neuron and neuro- imaging. Stan Gielen Dept. of Biophysics University of Nijmegen

2. 26-11-04Neuro_Informatics Workshop Relevance for Neuro-Informatics Scientific topic: interpretation of neuronal data: spike signals neuro-imaging signals. Neuron-models good (complex) data

3. 26-11-04Neuro_Informatics Workshop The neural code Firing rate Recruitment Synchronous firing Neuronal assembly

4. 26-11-04Neuro_Informatics Workshop Synchronization of firing related to attention Riehle et al. Science, 1999

5. 26-11-04Neuro_Informatics Workshop Hazard rate modulates motor cortical oscillatory neuronal activity Courtesy by Schoffelen and Fries F.C. Donders Center

6. 26-11-04Neuro_Informatics Workshop Coherence between motor cortex and muscle EMG Courtesy by Schoffelen and Fries F.C. Donders Center

7. 26-11-04Neuro_Informatics Workshop Scientific questions Beta (15-30 Hz) and gamma (30-70 Hz) oscillations in EEG and MEG have poor signal-to-noise ratio: epiphenomenon, artefact or functional significance ? To what extent are EEG/MEG oscillations a reflection of common, synchronized action potential firing ? If neuronal synchronization has any functional implications: what are the mechanisms to initiate it or to stop it ?

8. 26-11-04Neuro_Informatics Workshop Why exploring neuron models ?

9. 26-11-04Neuro_Informatics Workshop Neuron models Leaky Integrate-and-Fire neuron Hodgkin-Huxley conductance based neuron V mV 0 mV -C m dV/dt = g max, Na m 3 h(V-V na ) + g max, K n 4 (V-V K ) + g leak (V-V na ) V time

10. 26-11-04Neuro_Informatics Workshop Simple model XY Common input n 1 (t)n 2 (t) Correlation ?

11. 26-11-04Neuro_Informatics Workshop Cross-correlation Kxy (t ) between output spikes of two conductance based neurons with common input Stroeve & Gielen (2000) Amount of common input

12. 26-11-04Neuro_Informatics Workshop Cross-correlation Kxy (t ) between output spikes of two conductance based neurons with common input Stroeve & Gielen, 2000) Amount of common input Correlated firing is a poor measure for common input !

13. 26-11-04Neuro_Informatics Workshop Neuronal properties are not constant ! Kuhn, Aertsen, and Rotter, The Journal of Neuroscience, 2004 24:2345–2356 synaptic background activity No synaptic background activity EPSPIPSP background excitatory rate Membrane conductance

14. 26-11-04Neuro_Informatics Workshop Neuronal properties Depend on synaptic input –amplitude of EPSP and IPSP –time constant of neuron –leaky integrator versus coincidence detector

15. 26-11-04Neuro_Informatics Workshop Neuronal signals

16. 26-11-04Neuro_Informatics Workshop Neuronal signals Synaptic contact Action Potential Single-unit and multi-unit activity Local Field Potential

17. 26-11-04Neuro_Informatics Workshop Crosscorrelation LIFHodgkin-Huxley Crosscorrelation is a poor measure for common input; coherence is a better measure

18. 26-11-04Neuro_Informatics Workshop Coherence function for 60% common input Hodgkin-HuxleyLIF

19. 26-11-04Neuro_Informatics Workshop Coherence Local-Field-Spike at 60% common input LIFHodgkin-Huxley

20. 26-11-04Neuro_Informatics Workshop Spike Field Coherence 20406080100100120 0 0.1 0.2 0.3 coherence Single unit Multi unit

21. 26-11-04Neuro_Informatics Workshop Relevance for future NeuroInformatics research Data-base of neuronal models in NEURON and GENESIS Data-base of complex physiological data: –combined local field potentials and single/multi unit recordings –combined neuro-imaging signals and local field/action potential recordings