www cncr nl Dynamic synapses presynaptic mechanisms Niels

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

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

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

How does the brain compute? 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. www. cncr. nl

How does the brain compute? www. cncr. nl axon Santiago Ramón y Cajal soma (1852 -1934) 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. flow of information

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

How does the brain compute? 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 www. cncr. nl

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

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

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

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 (m. EPSP) pe pe

Short term plasticity www. cncr. nl reserve pool (U) pe=probability of release per AP at a synapse R=readily releasable pool pv=release probability per vesicle per AP B B docked pool (D) B Ca 2+ Ca 2+ readily releasable pool (R) pv STD: depletion of vesicles STP: calcium accumulation or buffer saturation

Realistic models for short term plasticity www. cncr. nl Presynaptic calcium: Vesicle dynamics: U f. D D b. D B B B b. R f. R R Ca 2+ 2+ Ca Ca 2+ Parameters: • forward/backward rates • pool sizes • release probabilities • calcium dependence

Autapses hippocampal island cultures www. cncr. nl

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

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

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

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

Counting synapses www. cncr. nl Synaps. Count. m area mean signal Ntot=310

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

Realistic models for short term plasticity www. cncr. nl readily releasable pool size: probability of release: U f. D D b. D B B B b. R f. R R Ca 2+ 2+ Ca Ca 2+ Manipulating system parameters: • transgenic animals (Doc 2 B, Munc 18, Rab 3 a. . . ) • overexpression with viral constructs • external calcium concentration • calcium buffers (EGTA, BAPTA)

Plans. . . www. cncr. nl Present: measuring mutants -Munc 18 -Doc 2 B 2005: -modeling calcium buffer saturation -measuring mutants -Rab 3 a -modeling effect Munc 18 Future: -measuring more mutants -modeling effect Doc 2 B, Rab 3 a Far future: -building realistic microcircuits (networks of 2 -3 cells) Far future: -building realistic neural networks

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

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

Short term plasticity www. cncr. nl

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 RRP size ps = probability of spontaneous release per vesicle per second. probability of evoked release probability of spontaneous release (only if Nt=Na!!!) (if N in f is Nt !!!) 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 Refill rate krf= refill rate krel=release rate

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