AACIMP 2009 Summer School lecture by Ruben Tikidji-Hamburyan. "Neuromodelling" course. 5th hour.

1. Southern Federal University
A.B.Kogan Research Institute for Neurocybernetics
Laboratory of neuroinformatics of
sensory and motor systems
Introduction to modern methods and
tools for biologically plausible modeling
of neural structures of brain
Part III
Ruben A. Tikidji – Hamburyan
rth@nisms.krinc.ru

2. Previous lectures in a nutshell
1. There is brain in head of human and animal. We use it for thinking.
2. Brain is researched at different levels. However physiological methods
is constrained. To avoid this limitations mathematical modeling is
widely used.
3. The brain is a huge network of connected cells. Cells are called
neurons, connections - synapses.
4. It is assumed that information processes in neurons take place at
membrane level. These processes are electrical activity of neuron.
5. Neuron electrical activity is based upon potentials generated by
selective channels and difference of ion concentration in- and outside
of cell.
6. Dynamics of membrane potential is defined by change of
conductances of different ion channels.
7. The biological modeling finishes and physico-chemical one begins at
the level of singel ion channel modeling.

3. Previous lectures in a nutshell
8. Instead of detailed description of each ion channel by energy function
we may use its phenomenological representation in terms of dynamic
system. This first representation for Na and K channels of giant squid
axon was supposed by Hodjkin&Huxley in 1952.
9. However, the H&H model has not key properties of neuronal activity.
To avoid this disadvantage, this model may be widened by additional
ion channels. Moreover, the cell body may be divided into
compartments.
10.Using the cable model for description of dendrite arbor had blocked
the researches of distal synapse influence for ten years up to 80s and
allows to model cell activity in dependence of its geometry.
11.There are many types of neuronal activity and different classifications.
12.The most of accuracy classification methods use pure mathematical
formalizations.
13.Identification of network environment is complicated experimental
problem that was resolved just recently. The simple example shows
that one connection can dramatically change the pattern of neuron
output.

4. Previous lectures in a nutshell
14.In the way of forth simplifications we can formally model only
dynamics of membrane potential without details of electrogenesis. This
approach is called phenomenological neural modeling.
15. There are many phenomenological models. Each author attempted to
find the balance between simplicity of model description and
completeness of showed dynamics.
16.There are a few models of synaptic transmissions. These models also
divided into detailed and phenomenological models.
17.The learning and memory are fundamental features of brain but there
are a lot of open issues how its work at the network and neuron levels.
18.The key function for learning rule isn't determined now. Last
experimental data show that learning rule varies at different synapses
on dendritic arbor.
19.Last observations indicate that intracellular calcium concentration
switches learning from nonsensitive condition through depression to
potentiation. In spine head, value of calcium concentration is controlled
by NMDA receptors and back propagating action potential. Including
in model biochemical reactions controlled by Ca concentration
dramatically increases its complicity.

5. Tools for biologically plausible modeling
Simulator Publicat Versi First Latest Primary License MS Mac OS X Linux Other Active Language
ions on release release author Windows Community
Emergent (formerly AisaMin 4.0 1986 2008 Dr. Randy GNU GPL XP, 2003, Intel, PPC Any, Any Unix emergent- C++
PDP++ and PDP) gusORei O'Reilly Vista Fedora, users list,
lly07 Ubuntu Wiki
GENESIS (the GEneral Beeman 2.3 1988 2007 Dr. James GNU GPL Cygwin Intel, PPC Yes Any Unix SourceForge C
NEural SImulation EtAl07 Bower & list
System) Dr. Dave
Beeman
NEURON (originally Hines93 6.2 1986 2008 Dr. Michael GNU GPL 95+ Intel, PPC Debian Any Unix NEURON C, C++
CABLE) HinesCa Hines Forum
rnevale9
7
HinesEt
Al06
SNNAP (Simulator for Unknow 8.1 2001 2007 Dr. John Proprietary Java Java Java Java Available Java
Neural Networks and n Byrne & Dr. but defunct
Action Potentials) Douglas
Baxter
Catacomb2 (Components Unknow 2.111 2001 2003 Robert GNU GPL Java Java Java Java No Java
And Tools for Accessible n Cannon
COmputer Modeling in
Biology
Topographica Neural BednarE 0.9.4 1998 2008 Dr. James A. GNU GPL Vista, XP, Build from Build from Build from Mailing list, Python/C++
Map Simulator tAl04 Bednar NT source source source boards
NEST (NEural Diesman 2.0 2004 2006 Unknown Proprietary Unknown Unknown Unknown Any Unix, NEST-users Unknown
Simulation Tool) nEtAl95 build from list
Diesman source
nGewalti
g02
Gewaltig
EtAl02D
jurfeldt0
8
http://grey.colorado.edu/emergent/index.php/Comparison_of_Neural_Network_Simulators

6. Tools for biologically plausible modeling
Simulator Publicat Vers First Latest Primary License MS Mac OS X Linux Other Active Language
ions ion release release author Windows Community
KInNeSS - KDE Gorchote 0.3.4 2004 2008 Dr. Anatoli GNU GPL No No KDE 3.1 No No C++
Integrated chnikov Gorchetchni required
NeuroSimulation EtAl04G kov
Software rossberg
EtAl05
XNBC VibertAz 9.10 1988 2006 Dr. Jean- GNU GPL 9x, 2000, Build from RPM Tru 64, No C++
my92Vib -h François XP source (Fedora), Ultrix, AIX,
ertEtAl9 VIBERT Build from SunOS,
7VibertE source HPux
tAl01
PCSIM: A Parallel neural Unknow 0.5.0 2008 2008 Dr. Dejan GNU GPL No No Build from No No Python/C++
Circuit SIMulator n Pecevski source
Dr. Thomas
Natschlager
NeuroCAD Unknow 0.00. 2003 2007 Dr. Ruben GNU GPL No No Yes Any Unix No C
n 21a Tikidji -
Hamburyan
http://grey.colorado.edu/emergent/index.php/Comparison_of_Neural_Network_Simulators

7. NeuroCAD – Problem definition
To create a computer environment, combining
flexibility and universality of script machines,
with efficacy of monolithically compiled, high
optimized application.
It would be very nice, if found solution allows to perform
computations in homogeneous, heterogeneous and SMP
system. Thereby parallelism is included in background of
NeuroCAD project.

8. NeuroCAD – how to make model?
Step I:
Select and export required
modules from modules
data bases as c-code and
compile it Modules
(shared objects files *.so)
Step V:
Step II: Make modules runtime
Link its by NeuroCAD Engine scheduler and run.
Step III:
Export variable blocks
in shared memory of
NeuroCAD Engine Step IV:
Connect
variables.
Step IV:
Connect variables.
shared memory

9. The synchrony of computations
▼ – one model time step
А – Module requiring 4 iterations for each step (RK4)
Б – One iteration module(EulExp)
В – 4 iterations module with overstep = 3

10. NeuroCAD Benchmarks
NeuroCAD vs. GENESIS ~ 5 – 15 times
NeuroCAD -normal NeuroCAD – tab Neuron – tab
0.2740 0.1955 1.1740
1 0.71 4.28NeuroCAD -normal
1 6.01 NeuroCAD – tab
1 Neuron – tab
http://nisms.krinc.ru/neurocad.org
rth@nisms.krinc.ru

11. The big model of Purkinje Cell
E. DeSchutter J.M. Bower
«An Active Membrane Model of the Cerebellar Purkinje Cell»
J. Neurophysiology Vol. 71, No. 1, January 1994.
● 1600 compartments
● 12 types of ion channels
●
Ca2+ concentration dynamics
●
Ca2+ dependent K+ channels
● Two synaptic types
● Three types of dendritic zones
● More than 60 tests and real data
comparisons (runtime for some
tests in 1994 was approximately
two weeks)

12. The big model of Purkinje Cell
E. DeSchutter J.M. Bower
«An Active Membrane Model of the Cerebellar Purkinje Cell»
J. Neurophysiology Vol. 71, No. 1, January 1994.

13. The big model of Purkinje Cell
E. DeSchutter J.M. Bower
«An Active Membrane Model of the Cerebellar Purkinje Cell»
J. Neurophysiology Vol. 71, No. 1, January 1994.

14. The big model of Purkinje Cell
E. DeSchutter J.M. Bower
«An Active Membrane Model of the Cerebellar Purkinje Cell»
J. Neurophysiology Vol. 71, No. 1, January 1994.

15. Detailed model of thalamo-cortical part of cat vision system
S. Hill, G. Tononi
«Modeling Sleep and Wakefulness in the Thalamocortical System»
J. Neurophysiology Vol. 93, 1671-1698, 2005.
● approximately 65000 neurons
● approximately 1.5 million synapses
● ration number of neurons in model
and average cat 1:9
● Three cortex layers and two thalamus
layers with modeling of primary and
secondary zones of visual perception
● Neuron model – hybrid of H-H and IaF with 4 types of ion channels.
● 5 types of synapses. Synaptic model includes mediator waste effect.
● Predominant anisotropy of network with local formed ensembles.

16. Detailed model of thalamo-cortical part of cat vision system

19. Детальная модель таламо – кортикальной части
зрительной системы кошки
S. Hill, G. Tononi
«Modeling Sleep and Wakefulness in the Thalamocortical System»
J. Neurophysiology Vol. 93, 1671-1698, 2005.
● Около 65000 нейронов
● Около 1.5 миллионов синапсов
● Отношение количества клеток в
модели к среднему у реального
животного 1:9
● Трехслойная кора и двухслойный
таламический уровень с
моделированием первичных и
вторичных зон восприятия
● Модель нейрона – переходный вариант между Х.-Х. и ИН. Может
содержать четыре типа ионных каналов.
● 5 разновидностей синапсов. Модели синапса учитывают эффекты
истощения медиатора.
● Существенно анизотропная сеть с единичными, локальными,
сформированными ансамблями.

20. Thalamic circuitry model based on
modified "integrate–and–fire neurons"

21. Thalamic circuitry model based on
modified "integrate–and–fire neurons"
Awake state
Slow wave sleep

22. Thalamic circuitry model based on
modified "integrate–and–fire neurons"

23. Thalamic circuitry model based on
modified "integrate–and–fire neurons"

24. How to localize the sound source:
coincidence detectors or I-E
populations?
?
Δt ~ 40 mks
τ ≥ 700 mks

25. How to localize the sound source:
coincidence detectors or I-E
populations?
Тикиджи-Хамбурьян Р.А., Полевая С.А.
Локализация источника звука искусственной
нейронной сетью, основанной на
модифицированных импульсных нейронах со
следовой поляризацией.
Нейрокомпьютеры: разработка, применение,
2004, № 11, с. 41-45.
В.А. Васильков, Р.А. Тикиджи – Хамбурьян
Исследование возможных механизмов
детектирования коротких временных задержек
популяцией E-I нейронов
Нейрокомпьютеры: разработка, применение,
(в печати)

26. Outputs of I-E neurons population when Δt in [-4, 4]ms.

27. Outputs of I-E neurons population when Δt in [-1, 1]ms.

28. Outputs of I-E neurons population when Δt in [-0.2, 0.2]ms.

29. Comparison with psychophysical tests

30. Detection quality measure(criterion)
1 m×k
∆N i
Φ=
m×k
∑ N × ∆T
i =1
,
i
where: N – amount of network elements, ∆N – change of pulses amount
in population respecting to change of time delay (∆t) to ∆T, m – amount
of simulations with different ∆t in one test, k – general amount of tests
(number of experiments).

31. Ф= 0,51 Ф= 0,47 Ф= 0,32
Plot diagram of model outputs and average value of pulse amount for ten
computer experiments with 1- 4 кHz noise presence.

32. The bar chart of dependence of Ф value from noise
amplitude.