Vrije Universiteit Amsterdam Computational Analysis of Spatiotemporal Patterns of Activity in Neuronal Networks Arjen van Ooyen (EN) Arjen Brussaard (EN)

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

s

wmIV

LFP

s

wmIV

LFP

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. 2001

KL basis images

Karhunen-Loeve decomposition

R C

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. 2003

Dendritic Axonal

Presynaptic cell

Generating Network Connectivity

Excitatory cellInhibitory cell

Ee

Ei

Er

Threshold

Vm

Integrate-and-fire model neurons

e ie e

i iei

For each type of connection:• Range• Number• Strength

Koene, Van Ooyen, Van Pelt

Decay Time GABAergic Current and Network Activity

20 ms40 pA

1 +/+

1 -/-1 -/-

GABAA receptor 1 +/+ 1 -/-

1 +/+

1 -/-

40 Hz stimulation Experimental data Bosman et al., in prep

msModel 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 2002Van Ooyen, Fonds, Van Elburg, in prep 100 ms

25 mV

Neuronal Microcircuit Models

MPBP

Pyr

Facilitation Depression

exex

in

in

Input

Van Elburg, Burnashev, Van Ooyen

Izhikevich et al., TINS 2003

PostsynapticEPSP

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 Hz20 ms40 pA

1 +/+

1 -/-1 -/-1 +/+

1 -/- 1 -/-

2 min

10 V

+ carbachol + kainc acid

+ carbachol + kainc acid

+ flunitrazepam

Bosman et al., in prep