Vrije Universiteit Amsterdam Computational Analysis of Spatiotemporal Patterns of Activity in Neuronal Networks Arjen van Ooyen (EN) Arjen Brussaard (EN) Ronald van Elburg (EN) Mathisca de Gunst (Statistics) Fabio Rigat (Statistics) PhD Vacancy (Statistics) VU: Jaap van Pelt (NN) Randal Koene (NN) NIH: NWO Computational Life Sciences Program

Spatiotemporal Patterns of Activity Cortical function based on dynamic patterns of activity in networks Exploring how dynamics depends on structural and functional network connectivity Key genes that affect network activity and thus cognition and behavior Techniques for recording neuronal activity –Single unit –Simultaneous recording of many neurons

Monitoring Neuronal Activity 40 Hz stimulation Photodiode array monitoring of voltage-sensitive dye activity (Brussaard et al., VU) Recording action potentials with multi-electrode arrays (Van Pelt et al., NIH)

CASPAN 1.Development of statistical methods for analysis and comparison of experimentally observed patterns of activity 2.Development of macroscopic neuronal network models with realistic functional and structural connectivity to simulate neuronal activity 3.Development of neuronal microcircuit models to investigate how fine structure of synaptic connectivity contributes to dynamics of neuronal activity NWO Computational Life Sciences Program Computational Analysis of Spatiotemporal Patterns of Activity in Neuronal Networks

Spatiotemporal Pattern Analysis Breakdown of long-range temporal correlations in 5-Hz oscillations of depressive patients Detection of long-range temporal correlations Klaus Linkenkaer-Hansen et al. Detrended fluctuation analysis

Spatiotemporal Pattern Analysis Senseman and Robbins, J. Neurophysiol KL basis images Karhunen-Loeve decomposition RC

Spatiotemporal Pattern Analysis Drawback existing statistical methods: not based on underlying biological processes Develop a model-based statistical approach that uses knowledge of the dynamics in neurons ‘Simple’ stochastic neural networks

Generating Network Connectivity Kalisman et al., Biol. Cybern DendriticAxonal Presynaptic cell

Generating Network Connectivity Excitatory cell Inhibitory cell EeEe EiEi ErEr Threshold VmVm Integrate-and-fire model neurons e i e e i i e i For each type of connection: Range Number Strength Koene, Van Ooyen, Van Pelt

Decay Time GABAergic Current and Network Activity      GABA A receptor  1 +/+  1 -/-  1 +/+  1 -/- 40 Hz stimulation Experimental data Bosman et al., in prep

ms Model Data Van Ooyen, Bosman, Brussaard, Neurocomputing 2004; Bosman et al., in prep  1 -/-  1 +/+

Neuronal Microcircuits Somogyi et al., Brain Res. Reviews 1998 Input-output characteristics influenced by: Type of cells Connectivity pattern Excitation-Inhibition feedback loops Synaptic strength Short-term synaptic plasticity Neuronal morphology Van Ooyen et al., Network 2002 Van Ooyen, Fonds, Van Elburg, in prep 100 ms 25 mV

Neuronal Microcircuit Models MP BP Pyr Facilitation Depression ex in Input Van Elburg, Burnashev, Van Ooyen Izhikevich et al., TINS 2003 Postsynaptic EPSP Postsynaptic EPSP

Neuroinformatic Challenges For analysing activity patterns: new statistical methods that uses knowledge of neuronal dynamics Stochastic model for the generation of connectivity and its variation (for microcircuit and macroscopic model) Macroscopic network model, incorporating short- term synaptic plasticity, to study synchrony and spread of activity Microcircuit model, and characterization of its dynamics Investigate use of microcircuit model as elementary unit (transfer function) in large scale network model

Oscillations  -band 40 Hz 20 ms 40pA  1 +/+  1-/-  1-/-   1-/-  1-/- 2 min 10  V +carbachol+kaincacid +carbachol+kaincacid +flunitrazepam Bosman et al., in prep