Computational Neuroscience: Computational Neuroscience: Towards Neuropharmacological Applications Towards Neuropharmacological Applications Péter Érdi Henry R. Luce Professor Center for Complex Systems Kalamazoo College Kalamazoo, MI KFKI Research Institute for Particle and Nuclear Physics of the Hungarian Academy of Science Budapest, Hungary http://www.kzoo.edu/physics/ccss http://www.rmki.kfki.hu/biofiz/cneuro
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Computational Neuroscience: Towards Neuropharmacological Applications Computational Neuroscience: Towards Neuropharmacological Applications Péter Érdi.
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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
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tVEtgtI
tVEtgtI
lll
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NaNaNa tstVtstVdt
tds
tngtg
thtmgtg
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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
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
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
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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