26-11-04Neuro_Informatics Workshop NeuroInformatics : Bridging the gap between neuron and neuro-...
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Transcript of 26-11-04Neuro_Informatics Workshop NeuroInformatics : Bridging the gap between neuron and neuro-...
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