26-11-04 Neuro_Informatics Workshop

NeuroInformatics : Bridging the gap between neuron and neuro-

imaging.

Stan GielenDept. of BiophysicsUniversity of Nijmegen

26-11-04 Neuro_Informatics Workshop

Relevance for Neuro-Informatics

• Scientific topic: interpretation of neuronal data: spike signals <=> neuro-imaging signals.

• Neuron-models• good (complex) data

26-11-04 Neuro_Informatics Workshop

The neural code

Firing rate

Recruitment

Synchronous firingNeuronal

assembly

26-11-04 Neuro_Informatics Workshop

Synchronization of firing related to attention

Riehle et al. Science, 1999

26-11-04 Neuro_Informatics Workshop

Hazard rate modulates motor corticaloscillatory neuronal activity

Courtesy by Schoffelen and Fries

F.C. Donders Center

26-11-04 Neuro_Informatics Workshop

Coherence between motor cortex and muscle EMG

Courtesy by Schoffelen and Fries

F.C. Donders Center

26-11-04 Neuro_Informatics Workshop

Scientific questions• Beta (15-30 Hz) and gamma (30-70 Hz) oscillations in

EEG and MEG have poor signal-to-noise ratio: epiphenomenon, artefact or functional significance ?

• To what extent are EEG/MEG oscillations a reflection of common, synchronized action potential firing ?

• If neuronal synchronization has any functional implications: what are the mechanisms to initiate it or to stop it ?

26-11-04 Neuro_Informatics Workshop

Why exploring neuron models ?

26-11-04 Neuro_Informatics Workshop

Neuron models

Leaky Integrate-and-Fire neuron Hodgkin-Huxley conductance based neuron

V mV

0 mV

-Cm dV/dt = gmax, Nam3h(V-Vna) +

gmax, K n4 (V-VK ) + g leak(V-Vna)

jj

ijii IwRmVV

dt

dV )70(

V

time

26-11-04 Neuro_Informatics Workshop

Simple model

X Y

Common inputn1(t) n2(t)

Correlation ?

26-11-04 Neuro_Informatics Workshop

Cross-correlation Kxy (t ) between output spikes of two

conductance basedneurons with common input

Stroeve & Gielen (2000)

Amount of common input

Hzi 30

Hzi 90

26-11-04 Neuro_Informatics Workshop

Cross-correlation Kxy (t ) between output spikes of two

conductance basedneurons with common input

Stroeve & Gielen, 2000)

Amount of common input

Hzi 30

Hzi 90

Correlated firing is a poor measure for common input !

26-11-04 Neuro_Informatics Workshop

Neuronal properties are not constant !

Kuhn, Aertsen, and Rotter, The Journal of Neuroscience, 2004 24:2345–2356

synaptic background activity

No synaptic background activity

EPSP IPSP

background excitatory rate

Mem

bra

ne

con

du

ctan

ce

26-11-04 Neuro_Informatics Workshop

Neuronal properties

• Depend on synaptic input– amplitude of EPSP and IPSP– time constant of neuron– leaky integrator versus coincidence detector

26-11-04 Neuro_Informatics Workshop

Neuronal signals

26-11-04 Neuro_Informatics Workshop

Neuronal signalsSynaptic

contact

Action Potential

Single-unit and multi-unit activity

Local Field Potential

26-11-04 Neuro_Informatics Workshop

CrosscorrelationLIF Hodgkin-Huxley

Crosscorrelation is a poor measure for common input;

coherence is a better measure

26-11-04 Neuro_Informatics Workshop

Coherence function for 60% common input

Hodgkin-HuxleyLIF

26-11-04 Neuro_Informatics Workshop

Coherence Local-Field-Spike at 60% common input

LIF Hodgkin-Huxley

26-11-04 Neuro_Informatics Workshop

Spike Field Coherence

20 40 60 80 1001200

0.1

0.2

0.3

cohe

renc

e Single unit

Multi unit

26-11-04 Neuro_Informatics Workshop

Relevance for future NeuroInformatics research

• Data-base of neuronal models in NEURON and GENESIS

• Data-base of complex physiological data:– combined local field potentials and single/multi unit

recordings

– combined neuro-imaging signals and local field/action potential recordings