2. Computational Neuroscience: Towards Neuropharmacological Applications Computational Neuroscience: 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

3. ContentsContents Computational neuroscience: microscopic and macroscopic methods Modeling the pharmacological modulation of the septohippocampal system Dynamical approach to neurology/psychiatry

4. Computational Neuroscience: Microscopic and Macroscopic Methods

5. Computational Neuroscience: Microscopic and Macroscopic Methods Subneural Components Brain Regions Layers / Modules Structural Decomposition Schemas Functional Decomposition Neural Networks Structure meets Function Neurons Brain / Behavior / Organism by Micheal A. Arbib The bottom-up modeling approach

6. Computational Neuroscience: Microscopic and Macroscopic Methods The top-down modeling approach Neural Networks Structure meets Function Neurons Brain / Behavior / Organism Subneural Components by Micheal A. Arbib Brain Regions Layers / Modules Structural Decomposition Schemas Functional Decomposition

7. Computational Neuroscience: Microscopic and Macroscopic Methods Reverse engineering the brain, learning how its components work... Describing morphology Identifying ion channels Adding synaptic connections

8. Single-cell models: the compartmental technique The Hodgkin-Huxley framework Cl - K+K+ A-A- Na + Ionic movementEquivalent electrical circuit The HH equations Modelled action potential

9. Computational Neuroscience: Microscopic and Macroscopic Methods Incorporating knowledge on the microscopic into modeling the macroscopic MeasurementTheory Unit & intracellular recordingHodgkin-Huxley formalism EEG & brain imaging techniquesBudapest Group: statistical neurodynamical approach to activity propagation in neural populations

10. Computational Neuroscience: Microscopic and Macroscopic Methods Activity propagation in the feline cortex Adaptation of the database by Scannel et. al.

11. Computational Neuroscience: Microscopic and Macroscopic Methods Activity propagation in the feline cortex Control Dorsomedial prefrontal cortex inhibition induced epilepsy From http://www.rmki.kfki.hu/biofiz/cneuro/tutorials/duke/ population activity high low

12. Modeling the pharmacological modulation of the septohippocampal system Modeling the pharmacological modulation of the septohippocampal system

13. Modeling the pharmacological modulation of the septohippocampal system Modeling the pharmacological modulation of the septohippocampal system Effects of reboxetine on theta activity 3 sec 1 mV Time (sec) Events (Hz) Control 1 mV 3 sec Frequency (Hz) Power Frequency (Hz) Power Frequency (Hz) Events (Hz) Time (sec) Hippocampal EEGFourier tr.Cross corr. After treatment with reboxetine

14. Modeling the pharmacological modulation of the septohippocampal system Modeling the pharmacological modulation of the septohippocampal system Effects of desipramine on theta activity 3 sec 1 mV Power Frequency (Hz) Events (Hz) Time (sec) Control After treatment with reboxetine Hippocampal EEGFourier tr.Cross corr. 1 mV 3 sec Power Frequency (Hz) Events (Hz) Time (sec)

15. Modeling the pharmacological modulation of the septohippocampal system Modeling the pharmacological modulation of the septohippocampal system Effects of fluvoxamine on theta activity Events (Hz) 1 mV 3 sec Control Hippocampal EEGFourier tr.Cross corr. After treatment with reboxetine 3 sec 1 mV Power Frequency (Hz) Time (sec) Power Frequency (Hz) Events (Hz) Time (sec)

16. Towards a computational/physiological molecular screening (and drug discovery) Towards a computational/physiological molecular screening (and drug discovery) Septohippocampal system Temporal pattern Desired temporal pattern Comp. Nontrivial e.g. Θ: enhanced cognition anxiogenics interface to further testing computational & pharmaceutical modulation

17. Modeling the pharmacological modulation of the septohippocampal system Modeling the pharmacological modulation of the septohippocampal system The septohippocampal system Location of the hippocampus in rodents Location of the hippocampus in human

18. Modeling the pharmacological modulation of the septohippocampal system Modeling the pharmacological modulation of the septohippocampal system Septum Hippocampus The septohippocampal system

19. Modeling the pharmacological modulation of the septohippocampal system Modeling the pharmacological modulation of the septohippocampal system The septohippocampal system Dentate Gyrus CA3 CA1 granule cells rat: 600 - 1000 x 10 3 human: 9000 x 10 3 pyramidal cells rat: 250 x 10 3 human: 4600 x 10 3 pyramidal cells rat: 160 x 10 3 human: 2300 x 10 3 C: convergence, D: divergence C: 50 - 100 D: 15 C, D: 5 - 10 x 10 3 C, D: 10 3 Entorhinal Cortex hippocampus proper: CA3 + CA1 hippocampus: DG + CA3 + CA1 hippocampal formation: EC + DG + CA3 + CA1 + Sub Subiculum

20. Modeling the pharmacological modulation of the septohippocampal system Modeling the pharmacological modulation of the septohippocampal system Septohippocampal system Locus Coeruleus Raphe Nucleus NE 5HT GABA NE re-uptake inhibition (reboxetine, desipramine) 5HT 2C agonist (m-cPP, Ro60-0175) 5HT 2C antagonist (SB-206553, SB-242084) 5HT 2C re-uptake inhibition (fluvoxamine) Inverse benzodiazepine agonist (FG-7142) Message from Mihaly Hajos’ works treatmentinduce/enhance θ NE re-uptake inhibition+ 5HT re-uptake inhibition– 5HT 2C antagonist+ 5HT 2C agonist– inverse benzodiazepine+ agonist

21. Simulation versus planning Modeling the pharmacological modulation of the septohippocampal system Modeling the pharmacological modulation of the septohippocampal system Knowledge from Anatomy Pharmacology Physiology Behavioral neuroscience Physics Mathematics Computer Science Building mathematical models Conduction computer experiments Designing biological experiments using their results understanding the phenomena

22. Simulation versus planning Modeling the pharmacological modulation of the septohippocampal system Modeling the pharmacological modulation of the septohippocampal system time (sec) Potential (V) Firing pattern of control hippocampal CA1 pyramidal cell time (sec) Potential (V) Firing pattern of K A current blocked hippocampal CA1 pyramidal cell Reversible and irreversible transition between modes KAKA blockade

23. Computer Experiment Modeling the pharmacological modulation of the septohippocampal system Modeling the pharmacological modulation of the septohippocampal system The experiment to be shown was done using the GENESIS simulation environment. A modified Traub’94 type pyramidal neuron was examined. Membrane potential vs. time curve measured in the axon. Current injection (10 nA) Time (sec) Potential (V) Recording site axon basal dendrites soma apical dendrites color code for membrane potential +50 mV-60 mV The model consists of 66 compartments for dendrites, the soma and the axon. Current types implemented are: Ca 2+, K DR, K AHP, K A, K C and Na currents. The model also accounts for intracellular Ca 2+ concent- ration.

24. Computer Experiment Modeling the pharmacological modulation of the septohippocampal system Modeling the pharmacological modulation of the septohippocampal system Control hippocampal CA1 pyramidal neuron

25. Computer Experiment Modeling the pharmacological modulation of the septohippocampal system Modeling the pharmacological modulation of the septohippocampal system Hippocampal CA1 pyramidal neuron after selective blockade of K A channels

26. Dynamical approach to neurology/psychiatry neurology/psychiatry

27. Dynamical approach to neurology/psychiatry Schizophrenia positive and negative symptoms hallucinationuncomplicated actions and speech decreased motivation state time state time ‘waving’ ‘steady’ Models: ‘lesion models’: does not explain waving neurotransmitter model (DOPA) disconnection hypothesisFriston NMDA: delayed maturation of NMDA receptors cortical pruning (synaptic depression) changes in attractor structure ‘pathological attractors’ “E” state “E” state storage and recall of memory traces

28. Dynamical approach to neurology/psychiatry The NMDA Receptor Delayed Maturation Hypothesis Excessive growth of synapses Reactive anomalous sprouting Frontal cortex, basal view Spontaneously occurring NMDA receptor hypofunction SCHIZOPHRENIA increase in the expression of the “immaturate” NR2D receptor subtype E. Ruppin

29. Dynamical approach to neurology/psychiatry The NMDA Receptor Delayed Maturation Hypothesis Pathological attractors appear “E” state “E” state recall of learned memory traces recall of never learned items “delusion” “hallucination”

30. Dynamical approach to neurology/psychiatry Introduction to Attractors

31. One of the main intention of computational neuroscience is to integrate 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 crucial role in cognitive processes, such as learning, memory formation and spatial navigation. Many neurological disorders, such as epilepsy, Alzheimer diseases, depression, anxiety, partially schizophrenia are hippocampus-dependent diseases.] Computational models of normal and pathological processes may help to develop more efficient therapeutic strategies. Closing Words