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Introduction to Modern Methods and Tools for Biologically Plausible Modelling of Neural Structures of Brain. Part 3
This document summarizes research on biologically plausible modeling of neural structures in the brain. It discusses several key points: 1. Mathematical modeling is widely used to study the brain since physiological methods have limitations. The brain can be modeled at different levels, from single ion channels to large neural networks. 2. Models of neural structures like the Purkinje cell and the thalamo-cortical visual system have been developed with a high level of biological detail, including thousands of neurons and ion channels. 3. Research is exploring how neural circuits in the thalamus could underlie different sleep/wake states using modified integrate-and-fire neuron models. Coincidence detection and excitation-inhibition populations are
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Introduction to Modern Methods and Tools for Biologically Plausible Modelling of Neural Structures of Brain. Part 3
- 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 ina 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 ina 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 ina 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 biologicallyplausible 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 biologicallyplausible 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 – Problemdefinition 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 – howto 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 ofcomputations ▼ – 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 NeuroCADvs. 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 modelof 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 modelof 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 modelof 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 modelof 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 ofthalamo-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 ofthalamo-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 modelbased on modified "integrate–and–fire neurons"
- 21. Thalamic circuitry modelbased on modified "integrate–and–fire neurons" Awake state Slow wave sleep
- 22. Thalamic circuitry modelbased on modified "integrate–and–fire neurons"
- 23. Thalamic circuitry modelbased on modified "integrate–and–fire neurons"
- 24. How to localizethe sound source: coincidence detectors or I-E populations? ? Δt ~ 40 mks τ ≥ 700 mks
- 25. How to localizethe 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-Eneurons population when Δt in [-4, 4]ms.
- 27. Outputs of I-Eneurons population when Δt in [-1, 1]ms.
- 28. Outputs of I-Eneurons population when Δt in [-0.2, 0.2]ms.
- 29.
- 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 chartof dependence of Ф value from noise amplitude.