Computational Neuroscience: Towards Neuropharmacological Applications Computational Neuroscience: Towards Neuropharmacological Applications Péter Érdi

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


The top-downmodeling approach

Neural NetworksStructure

meetsFunction

Neurons

Brain / Behavior / Organism

SubneuralComponents

by Micheal A. Arbib

Brain RegionsLayers / ModulesStructural

Decomposition

SchemasFunctional

Decomposition


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


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


Activity propagation in the feline cortex

Adaptation of the database by Scannel et. al.


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


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


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



Effects of desipramine on theta activity

3 sec

1 m

V

Pow

er

0.8

0.6

0.4

0.2

0.0


Events

(H

z) 60

40

20

0


Control



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




Effects of fluvoxamine on theta activity

Events

(H

z)

1 m

V

3 sec

Control



3 sec

1 m

V

Pow

er

0.8

0.6

0.4

0.2

0.0


60

40

20

0


Pow

er

0.8

0.6

0.4

0.2

0.0


Events

(H

z)

60

40

20

0


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



The septohippocampal system

Location of the hippocampus inrodents

Location of the hippocampus inhuman



Septum

Hippocampus





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



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



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



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



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



Control hippocampal CA1 pyramidal neuron

Computer Experiment



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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Computational Neuroscience: Towards Neuropharmacological Applications Computational Neuroscience: Towards Neuropharmacological Applications Péter Érdi