Dynamic synapses presynaptic mechanisms Niels Cornelisse

1. www.cncr.nl Dynamic synapses presynaptic mechanisms Niels Cornelisse Centre for Neurogenomics and Cognitive Research (www.cncr.nl) VU Amsterdam niels@cncr.vu.nl

2. Computational Neuroscience www.cncr.nl Computational Neuroscience: How does the brain compute??

3. How does the brain compute? www.cncr.nl Model of the brain before 1890 .... (reticular theory)

4. How does the brain compute? www.cncr.nl Santiago Ramón y Cajal (1852-1934) Superficial layers of the human frontal cortex drawn by Cajal on the basis of Golgi impregnation. The main cell types of the cerebral cortex i.e. small and large pyramidal neurons (A, B, C, D, E) and non pyramidal (F, K) cells (interneurons in the modern nomenclature) are superbly outlined.

5. How does the brain compute? www.cncr.nl axon flow of information Santiago Ramón y Cajal (1852-1934) soma dendrites Superficial layers of the human frontal cortex drawn by Cajal on the basis of Golgi impregnation. The main cell types of the cerebral cortex i.e. small and large pyramidal neurons (A, B, C, D, E) and non pyramidal (F, K) cells (interneurons in the modern nomenclature) are superbly outlined.

6. How does the brain compute? www.cncr.nl axon Alan Lloyd Hodgkin Andrew Fielding Huxley flow of information soma dendrites

7. How does the brain compute? www.cncr.nl • synaptic plasticity (long term): • LTP and LTD • Dependent on pre- and post-synaptic spike timing • Learning and memory formation • Postsynaptically: insertion of AMPA receptors Donald Hebb

8. How does the brain compute? www.cncr.nl Synaptic plasticity (Short term): computational properties recording stimulation facilitation

9. How does the brain compute? www.cncr.nl Synaptic plasticity (Short term): computational properties recording stimulation recording facilitation short term depression

10. Short term plasticity www.cncr.nl synaptic filtering decorrelation and burst detection depressing low-pass facilitating high-pass more efficient code intermediate enhances burst encoded signals band-pass

11. Short term plasticity www.cncr.nl What determines the strength of an evoked post-synaptic current? Ae Ae=EPSP amplitude evoked by AP Na=Number of active synapses pe=probability of release at a synapse per AP Am=Amplitude of EPSP evoked by one synapse (mEPSP) pe pe pe pe

12. Short term plasticity www.cncr.nl Ca2+ Ca2+ B B Ca2+ Ca2+ B B Ca2+ B reserve pool (U) pe=probability of release per AP at a synapse R=readily releasable pool pv=release probability per vesicle per AP docked pool (D) readily releasable pool (R) STD: depletion of vesicles STP: calcium accumulation or buffer saturation pv

13. Realistic models for short term plasticity www.cncr.nl Ca2+ Ca2+ Ca2+ B Ca2+ B B Ca2+ B B Presynaptic calcium: Vesicle dynamics: U • Parameters: • forward/backward rates • pool sizes • release probabilities • calcium dependence fD bD bR D R fR

14. Autapses www.cncr.nl hippocampal island cultures

15. Autapses www.cncr.nl 1. Mini’s: q=charge per mini

16. Autapses www.cncr.nl 2. Electrically evoked EPSC’s: Qe=charge evoked EPSC =Napeq

17. Autapses www.cncr.nl Hypertonic solution (Sucrose) 3. Sucrose induced EPSC’s: Qt Qt=NaRq

18. Autapses www.cncr.nl to count synapses: fixate cells -> immunostaining synapsin

19. Counting synapses www.cncr.nl SynapsCount.m mean signal area Ntot=310

20. Counting active synapses www.cncr.nl Before After stimulation with high K+ staining synapses with FM-dye

21. Realistic models for short term plasticity www.cncr.nl Ca2+ Ca2+ Ca2+ B Ca2+ B B Ca2+ B B readily releasable pool size: probability of release: U • Manipulating system parameters: • transgenic animals (Doc2B, Munc18, Rab3a ...) • overexpression with viral constructs • external calcium concentration • calcium buffers (EGTA,BAPTA) fD bD bR D R fR

22. Plans ... www.cncr.nl Present: measuring mutants -Munc18 -Doc2B 2005: -modeling calcium buffer saturation -measuring mutants -Rab3a -modeling effect Munc18 Future: -measuring more mutants -modeling effect Doc2B, Rab3a Far future: -building realistic microcircuits (networks of 2-3 cells) Far far future: -building realistic neural networks

23. Acknowledgements www.cncr.nl Functional Genomics: Matthijs Verhage Keimpe Wierda Ruud Toonen Sander Groffen Experimental Neurophysiology: Arjen Brussaard Nail Burnashev Huib Mansvelder Hans Lodder Tessa Lodder LUMC: Wouter Veldkamp

24. How does the brain compute? www.cncr.nl Microcircuit layer 2/3 visual cortex (Burnashev & Zilberter, in prep.)

25. Short term plasticity www.cncr.nl

26. How does the brain compute? www.cncr.nl Island Electrophysiology: Electrophysiological assays for autapses f = mini frequency Nt = total number of synapses Na= number of active synapses n = total number of ready releasable vesicles per synapse ps = probability of spontaneous release per vesicle per second. pe= probability of evoked release per vesicle per AP Q = total charge EPSC (evoked) Qt= charge of transient EPSC response to sucrose Qss=charge of steady state current during a time interval t q = charge mini EPSC krf= refill rate krel=release rate RRP size probability of evoked release (only if Nt=Na!!!) (if Nin f is Nt !!!) probability of spontaneous release Refill rate

27. www.cncr.nl Island Electrophysiology: Electrophysiological assays for autapses f = mini frequency Nt = total number of synapses Na= number of active synapses n = total number of ready releasable vesicles per synapse ps = probability of spontaneous release per vesicle per second. pe= probability of evoked release per vesicle per AP Q = total charge EPSC (evoked) Qt= charge of transient EPSC response to sucrose Qss=charge of steady state current during a time interval t q = charge mini EPSC krf= refill rate krel=release rate 1. Mini’s: 2. Electrically evoked EPSC’s: (assuming that more than one vesicle may be released per AP and no saturation at the postsynaptic site) 3. Sucrose induced EPSC’s: