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Computational Neuroscience:Computational Neuroscience:
Towards Neuropharmacological ApplicationsTowards Neuropharmacological ApplicationsComputational Neuroscience:Computational Neuroscience:
Towards Neuropharmacological ApplicationsTowards Neuropharmacological Applications
Péter ÉrdiHenry R. Luce Professor
Center for Complex SystemsKalamazoo College
Kalamazoo, MI
KFKI Research Institute for Particle and Nuclear Physicsof the Hungarian Academy of Science
Budapest, Hungary
http://www.kzoo.edu/physics/ccss
http://www.rmki.kfki.hu/biofiz/cneuro
ContentsContentsContentsContents
•Computational neuroscience: microscopic and macroscopic methods
•Modeling the pharmacological modulation of the septohippocampal
system
•Dynamical approach to neurology/psychiatry
Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:
Microscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic Methods
Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:Microscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic Methods
SubneuralComponents
Brain RegionsLayers / ModulesStructural
Decomposition
SchemasFunctional
Decomposition
Neural NetworksStructure
meetsFunction
Neurons
Brain / Behavior / Organism
by Micheal A. Arbib
The bottom-upmodeling approach
Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:Microscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic Methods
The top-downmodeling approach
Neural NetworksStructure
meetsFunction
Neurons
Brain / Behavior / Organism
SubneuralComponents
by Micheal A. Arbib
Brain RegionsLayers / ModulesStructural
Decomposition
SchemasFunctional
Decomposition
Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:Microscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic Methods
Reverse engineering the brain,
learning how its components work...
Describing morphology
Identifying ion channels
Adding synaptic connections
Single-cell models: the compartmental techniqueSingle-cell models: the compartmental techniqueSingle-cell models: the compartmental techniqueSingle-cell models: the compartmental techniqueThe Hodgkin-Huxley frameworkThe Hodgkin-Huxley frameworkThe Hodgkin-Huxley frameworkThe Hodgkin-Huxley framework
Cl-
K+A-
Na+
Ionic movement Equivalent electrical circuit
lK,Na,inja
m''
m'a
m'
m
mm
mmmm
ktIR
tVtV
R
tVtV
tgEtVR
EtV
dt
tdVC
kkk
tVEgtI
tVEtgtI
tVEtgtI
lll
KKK
NaNaNa tstVtstVdt
tds
tngtg
thtmgtg
1
4KK
3NaNa
The HH equationsModelled action potential
Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:Microscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic Methods
Incorporating knowledge on themicroscopic
into modeling the macroscopic
Measurement Theory
Unit & intracellular recording Hodgkin-Huxley formalism
EEG & brain imaging techniques Budapest Group: statisticalneurodynamical approach to activitypropagation in neural populations
Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:Microscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic Methods
Activity propagation in the feline cortex
Adaptation of the database by Scannel et. al.
Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:Computational Neuroscience:Microscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic MethodsMicroscopic and Macroscopic Methods
Activity propagation in the feline cortex
ControlDorsomedial prefrontal cortex
inhibition induced epilepsy
From http://www.rmki.kfki.hu/biofiz/cneuro/tutorials/duke/
population
activity
hig
hlo
w
Modeling the pharmacologicalModeling the pharmacologicalmodulationmodulation
of the septohippocampal systemof the septohippocampal system
Modeling the pharmacologicalModeling the pharmacologicalmodulationmodulation
of the septohippocampal systemof the septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Effects of reboxetine on theta activity
3 sec
1 m
V
20
15
10
5
0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0secondsTime Time (sec)
Events
(H
z)
Control
1 m
V
3 sec
3
2
1
0
0 5 10HzFrequencyFrequency (Hz)
Pow
er
3
2
1
0
0 5 10HzFrequencyFrequency (Hz)
Pow
er
Frequency (Hz)
20
15
10
5
0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0secondsTime
Events
(H
z)Time (sec)
Hippocampal EEG Fourier tr. Cross corr.
After treatment with reboxetine
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Effects of desipramine on theta activity
3 sec
1 m
V
Pow
er
0.8
0.6
0.4
0.2
0.0
0 5 10HzFrequencyFrequency (Hz)
Events
(H
z) 60
40
20
0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0secondsTime Time (sec)
Control
After treatment with reboxetine
Hippocampal EEG Fourier tr. Cross corr.
1 m
V
3 sec
0.8
0.6
0.4
0.2
0.0
0 5 10HzFrequency
Pow
er
Frequency (Hz)Events
(H
z)
60
40
20
0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0secondsTime Time (sec)
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Effects of fluvoxamine on theta activity
Events
(H
z)
1 m
V
3 sec
Control
Hippocampal EEG Fourier tr. Cross corr.
After treatment with reboxetine
3 sec
1 m
V
Pow
er
0.8
0.6
0.4
0.2
0.0
0 5 10HzFrequencyFrequency (Hz)
60
40
20
0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0secondsTime Time (sec)
Pow
er
0.8
0.6
0.4
0.2
0.0
0 5 10HzFrequencyFrequency (Hz)
Events
(H
z)
60
40
20
0
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0secondsTime Time (sec)
Towards a computational/physiologicalTowards a computational/physiologicalmolecular screening (and drug discovery)molecular screening (and drug discovery)
Towards a computational/physiologicalTowards a computational/physiologicalmolecular screening (and drug discovery)molecular screening (and drug discovery)
Septohippocampalsystem Temporal pattern
Desired temporal pattern
Comp.
Nontrivial
e.g. Θ: enhanced cognition
anxiogenics
interface tofurther testing
computational & pharmaceutical modulation
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
The septohippocampal system
Location of the hippocampus inrodents
Location of the hippocampus inhuman
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Septum
Hippocampus
The septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
The septohippocampal system
Dentate Gyrus
CA3
CA1
granule cells
rat: 600 - 1000 x 103
human: 9000 x 103
pyramidal cells
rat: 250 x 103
human: 4600 x 103
pyramidal cells
rat: 160 x 103
human: 2300 x 103
C: convergence, D: divergence
C: 50 - 100D: 15
C, D: 5 - 10 x 103
C, D: 103
En
torh
inal C
ort
ex
hippocampus proper: CA3 + CA1
hippocampus: DG + CA3 + CA1
hippocampal formation: EC + DG + CA3 + CA1 + Sub
Subiculum
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Septohippocampalsystem
Locus Coeruleus
Raphe Nucleus
NE
5HT
GABA
NE re-uptake inhibition(reboxetine,desipramine)
5HT2C agonis
t(m-cPP,Ro60-0175)
5HT2C antagonis
t(SB-206553,SB-242084)
5HT2C re-uptake inhibition
(fluvoxamine)
Inverse benzodiazepine agonist
(FG-7142)
Message from Mihaly Hajos’ works
treatment induce/enhance θ
NE re-uptake inhibition +5HT re-uptake inhibition –5HT2C antagonist +5HT2C agonist –inverse benzodiazepine +
agonist
Simulation versus planning
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Knowledge from
•Anatomy
•Pharmacology
•Physiology
•Behavioral neuroscience
•Physics
•Mathematics
•Computer Science
Building mathematicalmodels
Conduction computerexperiments
Designing biologicalexperiments
using their results
understanding thephenomena
Simulation versus planning
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
time (sec)
Pote
nti
al (V
)
Firing pattern of controlhippocampal CA1 pyramidal cell
time (sec)
Pote
nti
al (V
)
Firing pattern of KA current blockedhippocampal CA1 pyramidal cell
Reversible and irreversible transition between modes
KA
blockade
Computer Experiment
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
The experiment to be shown was done using the GENESIS simulationenvironment.
A modified Traub’94 type pyramidalneuron was examined.
Membrane potential vs. timecurve measured in the axon.
Current injection (10 nA)
Time (sec)
Pote
nti
al (V
)
Recording site
axon
basal dendrites
soma
apicaldendrites
color code for membrane potential
+50 mV -60 mV
The model consists of 66compartments for dendrites,the soma and the axon.
Current types implementedare: Ca2+, KDR, KAHP, KA, KC
and Na currents.
The model also accounts forintracellular Ca2+ concent-ration.
Computer Experiment
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Control hippocampal CA1 pyramidal neuron
Computer Experiment
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Modeling the pharmacological modulationModeling the pharmacological modulationof the septohippocampal systemof the septohippocampal system
Hippocampal CA1 pyramidal neuronafter selective blockade of KA channels
Dynamical approach toDynamical approach toneurology/psychiatryneurology/psychiatry
Dynamical approach toDynamical approach toneurology/psychiatryneurology/psychiatry
Dynamical approach to neurology/psychiatryDynamical approach to neurology/psychiatryDynamical approach to neurology/psychiatryDynamical approach to neurology/psychiatrySchizophrenia
positive and negative symptoms
hallucination uncomplicated actions and speechdecreased motivation
state
time state
time‘waving’ ‘steady’
Models:• ‘lesion models’: does not explain waving• neurotransmitter model (DOPA)• disconnection hypothesis Friston• NMDA: delayed maturation of NMDA receptors• cortical pruning (synaptic depression)
changes in attractor structure‘pathological attractors’
“E”
state
“E”
state
storage and recallof memory traces
Dynamical approach to neurology/psychiatryDynamical approach to neurology/psychiatryDynamical approach to neurology/psychiatryDynamical approach to neurology/psychiatryThe NMDA Receptor Delayed Maturation Hypothesis
Excessive growth of synapses
Reactiveanomaloussprouting
Frontal cortex, basal view
Spontaneously occurring NMDA receptor hypofunction
SCHIZOPHRENIA
increase in the expression of the“immaturate” NR2D receptor subtype
E. Ruppin
Dynamical approach to neurology/psychiatryDynamical approach to neurology/psychiatryDynamical approach to neurology/psychiatryDynamical approach to neurology/psychiatryThe NMDA Receptor Delayed Maturation Hypothesis
Pathological attractors appear
“E”
state
“E”
state
recall of learnedmemory traces
recall of neverlearned items
“delusion”“hallucination”
Dynamical approach to neurology/psychiatryDynamical approach to neurology/psychiatryDynamical approach to neurology/psychiatryDynamical approach to neurology/psychiatryIntroduction to Attractors
One of the main intention of computational neuroscience is tointegrate anatomical, physiological, neurochemical/pharmacological and behavioural data by coherent concepts and models.
[A basic structure for which such integration is particularly important is the hippocampal formation. Hippocampus has a crucialrole in cognitive processes, such as learning, memory formation andspatial navigation. Many neurological disorders, such as epilepsy,Alzheimer diseases, depression, anxiety, partially schizophrenia arehippocampus-dependent diseases.]
Computational models of normal and pathological processes mayhelp to develop more efficient therapeutic strategies.
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