Dynamic synapses presynaptic mechanisms Niels Cornelisse Centre for Neurogenomics and Cognitive Research ( VU Amsterdam

Computational Neuroscience Computational Neuroscience: How does the brain compute??

How does the brain compute? Model of the brain before (reticular theory)

How does the brain compute? 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. Santiago Ramón y Cajal ( )

How does the brain compute? 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 soma axon dendrites Santiago Ramón y Cajal ( )

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

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

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

How does the brain compute? Synaptic plasticity (Short term): computational properties stimulation recording recording facilitationshort term depression

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

Short term plasticity pepe pepe pepe pepe AeAe A e =EPSP amplitude evoked by AP N a =Number of active synapses p e =probability of release at a synapse per AP A m =Amplitude of EPSP evoked by one synapse (mEPSP) What determines the strength of an evoked post-synaptic current?

Short term plasticity readily releasable pool (R) docked pool (D) reserve pool (U) pvpv Ca 2+ B B B B B p e =probability of release per AP at a synapse R=readily releasable pool p v =release probability per vesicle per AP STD: depletion of vesicles STP: calcium accumulation or buffer saturation

Realistic models for short term plasticity R D U fDfD fRfR bRbR bDbD Ca 2+ B B B B B Parameters: forward/backward rates pool sizes release probabilities calcium dependence Presynaptic calcium: Vesicle dynamics:

Autapses hippocampal island cultures

Autapses 1. Mini’s: q=charge per mini

Autapses 2. Electrically evoked EPSC’s: Q e =charge evoked EPSC =N a p e q

Hypertonic solution (Sucrose) Autapses 3. Sucrose induced EPSC’s: Q t =N a Rq QtQt

Autapses to count synapses: fixate cells -> immunostaining synapsin

Counting synapses mean signal area N tot =310 SynapsCount.m

Counting active synapses Before After stimulation with high K + staining synapses with FM-dye

Realistic models for short term plasticity R D U fDfD fRfR bRbR bDbD Ca 2+ B B B B B readily releasable pool size: probability of release: Manipulating system parameters: transgenic animals (Doc2B, Munc18, Rab3a...) overexpression with viral constructs external calcium concentration calcium buffers (EGTA,BAPTA)

Plans... 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

Acknowledgements 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

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

Short term plasticity

How does the brain compute? Island Electrophysiology: Electrophysiological assays for autapses RRP size probability of evoked release probability of spontaneous release Refill rate f = mini frequency N t = total number of synapses N a = number of active synapses n = total number of ready releasable vesicles per synapse p s = probability of spontaneous release per vesicle per second. p e = probability of evoked release per vesicle per AP Q = total charge EPSC (evoked) Q t = charge of transient EPSC response to sucrose Q ss =charge of steady state current during a time interval  t q = charge mini EPSC k rf = refill rate k rel =release rate (only if N t =N a !!!) (if N in f is N t !!!)

Island Electrophysiology: Electrophysiological assays for autapses 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) f = mini frequency N t = total number of synapses N a = number of active synapses n = total number of ready releasable vesicles per synapse p s = probability of spontaneous release per vesicle per second. p e = probability of evoked release per vesicle per AP Q = total charge EPSC (evoked) Q t = charge of transient EPSC response to sucrose Q ss =charge of steady state current during a time interval  t q = charge mini EPSC k rf = refill rate k rel =release rate 3. Sucrose induced EPSC’s: