Southern Federal University

Laboratory of neuroinformatics ofsensory and motor systems

A.B.Kogan Research Institute for Neurocybernetics

Ruben A. Tikidji – Hamburyanrth@nisms.krinc.ru

Introduction to modern methods and tools for biologically plausible modeling

of neural structures of brain

Part III

Previous lectures in a nutshell1. 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.

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.

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.

Previous lectures in a nutshell

Tools for biologically plausible modelingSimulator Publicat

ions Version

Firstrelease

Latestrelease

Primaryauthor

License MSWindows

Mac OS X Linux Other ActiveCommunity

Language

Emergent (formerlyPDP++ and PDP)

AisaMingusOReilly07

4.0 1986 2008 Dr. RandyO'Reilly

GNU GPL XP, 2003,Vista

Intel, PPC Any,Fedora,Ubuntu

Any Unix emergent-users list,Wiki

C++

GENESIS (the GEneralNEural SImulationSystem)

BeemanEtAl07

2.3 1988 2007 Dr. JamesBower &Dr. DaveBeeman

GNU GPL Cygwin Intel, PPC Yes Any Unix SourceForgelist

C

NEURON (originallyCABLE)

Hines93HinesCarnevale97HinesEtAl06

6.2 1986 2008 Dr. MichaelHines

GNU GPL 95+ Intel, PPC Debian Any Unix NEURONForum

C, C++

SNNAP (Simulator forNeural Networks andAction Potentials)

Unknown

8.1 2001 2007 Dr. JohnByrne & Dr.DouglasBaxter

Proprietary Java Java Java Java Availablebut defunct

Java

Catacomb2 (ComponentsAnd Tools for AccessibleCOmputer Modeling inBiology

Unknown

2.111 2001 2003 RobertCannon

GNU GPL Java Java Java Java No Java

Topographica NeuralMap Simulator

BednarEtAl04

0.9.4 1998 2008 Dr. James A.Bednar

GNU GPL Vista, XP,NT

Build fromsource

Build fromsource

Build fromsource

Mailing list,boards

Python/C++

NEST (NEuralSimulation Tool)

DiesmannEtAl95DiesmannGewaltig02GewaltigEtAl02Djurfeldt08

2.0 2004 2006 Unknown Proprietary Unknown Unknown Unknown Any Unix,build fromsource

NEST-userslist

Unknown

http://grey.colorado.edu/emergent/index.php/Comparison_of_Neural_Network_Simulators

Tools for biologically plausible modeling

Simulator Publications

Version

Firstrelease

Latestrelease

Primaryauthor

License MSWindows

Mac OS X Linux Other ActiveCommunity

Language

KInNeSS - KDEIntegratedNeuroSimulationSoftware

GorchotechnikovEtAl04GrossbergEtAl05

0.3.4 2004 2008 Dr. AnatoliGorchetchnikov

GNU GPL No No KDE 3.1required

No No C++

XNBC VibertAzmy92VibertEtAl97VibertEtAl01

9.10-h

1988 2006 Dr. Jean-FrançoisVIBERT

GNU GPL 9x, 2000,XP

Build fromsource

RPM(Fedora),Build fromsource

Tru 64,Ultrix, AIX,SunOS,HPux

No C++

PCSIM: A Parallel neuralCircuit SIMulator

Unknown

0.5.0 2008 2008 Dr. DejanPecevskiDr. ThomasNatschlager

GNU GPL No No Build fromsource

No No Python/C++

NeuroCAD Unknown

0.00.21a

2003 2007 Dr. RubenTikidji -Hamburyan

GNU GPL No No Yes Any Unix No C

http://grey.colorado.edu/emergent/index.php/Comparison_of_Neural_Network_Simulators

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.

NeuroCAD – how to make model?

Step II:Link its by NeuroCAD Engine

shared memory

Step III:Export variable blocks in shared memory of NeuroCAD Engine Step IV:

Connect variables.

Step IV:Connect variables.

Step I:Select and export required modules from modules data bases as c-code and compile it Modules

(shared objects files *.so)Step V:Make modules runtime scheduler and run.

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

NeuroCAD Benchmarks

NeuroCAD vs. GENESIS ~ 5 – 15 times

NeuroCAD -normal NeuroCAD – tab Neuron – tab0.2740 0.1955 1.1740

1 0.71 4.28NeuroCAD -normal1 6.01NeuroCAD – tab

1Neuron – tab

http://nisms.krinc.ru/neurocad.orgrth@nisms.krinc.ru

The big model of Purkinje CellE. 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)

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

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

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

●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.

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

Detailed model of thalamo-cortical part of cat vision system

Detailed model of thalamo-cortical part of cat vision system

●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.

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

Детальная модель таламо – кортикальной частизрительной системы кошки

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

Slo

w w

ave

sle

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wak

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ate

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

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

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

How to localize the sound source: coincidence detectors or I-E

populations?

?

Δt ~ 40 mksτ ≥ 700 mks

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 – ХамбурьянИсследование возможных механизмов детектирования коротких временных задержек популяцией E-I нейроновНейрокомпьютеры: разработка, применение, (в печати)

How to localize the sound source: coincidence detectors or I-E

populations?

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

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

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

Comparison with psychophysical tests

Detection quality measure(criterion)

∑×

= ××=

km

1i i

i

TN

N

km

1

∆∆Φ

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).

,

Plot diagram of model outputs and average value of pulse amount for ten computer experiments with 1- 4 кHz noise presence.

Ф= 0,51 Ф= 0,47 Ф= 0,32

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