This document provides an overview of modern methods and tools for biologically plausible modeling of neural structures in the brain. It discusses modeling at different levels, from the system level looking at the brain as a whole, down to the subcellular and molecular levels examining individual neurons and ion channels. At each level, it outlines key research methods used to study the brain experimentally and different modeling approaches, including population and dynamical models, formal neural networks, and detailed single-cell models. The document also reviews seminal work in neuroscience like Hodgkin and Huxley's equations for modeling ion channel dynamics and spike generation in neurons.
Introduction to modern methods and tools for biologically plausible modeling ...SSA KPI
AACIMP 2010 Summer School lecture by Ruben Tikidji-Hamburyan. "Physics, Chemistry and Living Systems" stream. "Introduction to Modern Methods and Tools for Biologically Plausible Modeling of Neurons and Neural Networks" course. Part 1.
More info at http://summerschool.ssa.org.ua
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BIOS203 Lecture 1: Introduction to potentials and minimizationbios203
Lecture 1 for BIOS 203 Mini-course at Stanford University taught by Heather J. Kulik. http://bios203.stanford.edu for more info or email bios203.course@gmail.com
Introduction to modern methods and tools for biologically plausible modeling ...SSA KPI
AACIMP 2010 Summer School lecture by Ruben Tikidji-Hamburyan. "Physics, Chemistry and Living Systems" stream. "Introduction to Modern Methods and Tools for Biologically Plausible Modeling of Neurons and Neural Networks" course. Part 1.
More info at http://summerschool.ssa.org.ua
Science Cafe Discovers a New Form of Alternative EnergyEngenuitySC
These are the slides from the May Science Cafe featuring Dr. MVS Chandrashekhar. During this cafe he discussed his work with graphene a new, clean energy source.
BIOS203 Lecture 1: Introduction to potentials and minimizationbios203
Lecture 1 for BIOS 203 Mini-course at Stanford University taught by Heather J. Kulik. http://bios203.stanford.edu for more info or email bios203.course@gmail.com
Вычислительный эксперимент в молекулярной биофизике белков и биомембранIlya Klabukov
Вычислительный эксперимент в молекулярной биофизике белков и биомембран
Ефремов Роман Гербертович, доктор физико-математических наук, профессор, заместитель директора по науке Института биоорганической химии имени академиков М.М.Шемякина и Ю.А.Овчинникова РАН, руководитель Лаборатории моделирования биомолекулярных систем
synbio2012.ru
AACIMP 2010 Summer School lecture by Anton Chizhov. "Physics, Chemistry and Living Systems" stream. "Neuron-Computer Interface in Dynamic-Clamp Experiments. Models of Neuronal Populations and Visual Cortex" course. Part 2.
More info at http://summerschool.ssa.org.ua
[L'angolo del PhD] Sara Borroni - XXIII Ciclo - 2010accatagliato
This thesis is focused on the study of the Z -> µ+ µ- process. This process is quite interesting. From the detector performance point of view, it can be used to measure from data muon trigger and reconstruction efficiencies. To extract these efficiencies, in the past three years I developed and optimized a method, called Tag&Probe, using Monte Carlo simulation. In the past few months, with the first ATLAS data, it allowed to measure the muon efficiencies from data for the first time. A data sample of 1.3 pb^-1 of integrated luminosity has been used and the results have been compared with the MC expectations.
The efficiencies estimation is also relevant for the cross-section measurement of all processes involving muons. In fact, when comparing the measured cross-section from data with the theoretical expectations, one has to correct for the detector inefficiencies, which at the start-ip are not perfectly reproduced in the simulation. In this thesis, these muon efficiencies have been used for a first data/MC comparison of the Z -> µ+ µ- cross-section, both inclusive and differential as a function of the jet multiplicity.
There is nowadays a growing need for sensing devices offering rapid and portable analytical functionality in real-time as well as massively parallel capabilities with very high sensitivity at the molecular level. Such devices are essential to facilitate research and foster advances in fields such as drug discovery, proteomics, medical diagnostics, systems biology or environmental monitoring.
In this context, an ideal solution is an ion-sensitive field-effect transistor sensor platform based on silicon nanowires to be integrated in a CMOS architecture. Indeed, in addition to the expected high sensitivity and superior signal quality, such nanowire sensors could be mass manufactured at reasonable costs, and readily integrated into electronic diagnostic devices to facilitate bed-site diagnostics and personalized medicine. Moreover, their small size makes them ideal candidates for future implanted sensing devices. While promising biosensing experiments based on silicon nanowire field-effect transistors have been reported, real-life applications still require improved control, together with a detailed understanding of the basic sensing mechanisms. For instance, it is crucial to optimize the geometry of the wire, a still rather unexplored aspect up to now, as well as its surface functionalization or its selectivity to the targeted analytes.
This project seeks to develop a modular, scalable and integrateable sensor platform for the electronic detection of analytes in solution. The idea is to integrate silicon nanowire field-effect transistors as a sensor array and combine them with state-of-the-art microfabricated interface electronics as well as with microfluidic channels for liquid handling. Such sensors have the potential to be mass manufactured at reasonable costs, allowing their integration as the active sensor part in electronic point-of-care diagnostic devices to facilitate, for instance, bed-side diagnostics and personalized medicine. Another important field is systems biology, where many substances need to be quantitatively detected in parallel at very low concentrations: in these situations, the platform being developed fulfills the requirements ideally and will have a strong impact and provide new insights, e.g. into the metabolic processes of cells, organisms or organs.
Вычислительный эксперимент в молекулярной биофизике белков и биомембранIlya Klabukov
Вычислительный эксперимент в молекулярной биофизике белков и биомембран
Ефремов Роман Гербертович, доктор физико-математических наук, профессор, заместитель директора по науке Института биоорганической химии имени академиков М.М.Шемякина и Ю.А.Овчинникова РАН, руководитель Лаборатории моделирования биомолекулярных систем
synbio2012.ru
AACIMP 2010 Summer School lecture by Anton Chizhov. "Physics, Chemistry and Living Systems" stream. "Neuron-Computer Interface in Dynamic-Clamp Experiments. Models of Neuronal Populations and Visual Cortex" course. Part 2.
More info at http://summerschool.ssa.org.ua
[L'angolo del PhD] Sara Borroni - XXIII Ciclo - 2010accatagliato
This thesis is focused on the study of the Z -> µ+ µ- process. This process is quite interesting. From the detector performance point of view, it can be used to measure from data muon trigger and reconstruction efficiencies. To extract these efficiencies, in the past three years I developed and optimized a method, called Tag&Probe, using Monte Carlo simulation. In the past few months, with the first ATLAS data, it allowed to measure the muon efficiencies from data for the first time. A data sample of 1.3 pb^-1 of integrated luminosity has been used and the results have been compared with the MC expectations.
The efficiencies estimation is also relevant for the cross-section measurement of all processes involving muons. In fact, when comparing the measured cross-section from data with the theoretical expectations, one has to correct for the detector inefficiencies, which at the start-ip are not perfectly reproduced in the simulation. In this thesis, these muon efficiencies have been used for a first data/MC comparison of the Z -> µ+ µ- cross-section, both inclusive and differential as a function of the jet multiplicity.
There is nowadays a growing need for sensing devices offering rapid and portable analytical functionality in real-time as well as massively parallel capabilities with very high sensitivity at the molecular level. Such devices are essential to facilitate research and foster advances in fields such as drug discovery, proteomics, medical diagnostics, systems biology or environmental monitoring.
In this context, an ideal solution is an ion-sensitive field-effect transistor sensor platform based on silicon nanowires to be integrated in a CMOS architecture. Indeed, in addition to the expected high sensitivity and superior signal quality, such nanowire sensors could be mass manufactured at reasonable costs, and readily integrated into electronic diagnostic devices to facilitate bed-site diagnostics and personalized medicine. Moreover, their small size makes them ideal candidates for future implanted sensing devices. While promising biosensing experiments based on silicon nanowire field-effect transistors have been reported, real-life applications still require improved control, together with a detailed understanding of the basic sensing mechanisms. For instance, it is crucial to optimize the geometry of the wire, a still rather unexplored aspect up to now, as well as its surface functionalization or its selectivity to the targeted analytes.
This project seeks to develop a modular, scalable and integrateable sensor platform for the electronic detection of analytes in solution. The idea is to integrate silicon nanowire field-effect transistors as a sensor array and combine them with state-of-the-art microfabricated interface electronics as well as with microfluidic channels for liquid handling. Such sensors have the potential to be mass manufactured at reasonable costs, allowing their integration as the active sensor part in electronic point-of-care diagnostic devices to facilitate, for instance, bed-side diagnostics and personalized medicine. Another important field is systems biology, where many substances need to be quantitatively detected in parallel at very low concentrations: in these situations, the platform being developed fulfills the requirements ideally and will have a strong impact and provide new insights, e.g. into the metabolic processes of cells, organisms or organs.
Tutorial in calculation of IR & NMR spectra (i.e. measuring nuclear vibrations and spins) using the GAUSSIAN03 computational chemistry package.
Following an introduction to spectroscopy in general, each of the two measurement types is presented in sequence. For each one, we review the theory before presenting the calculation scheme. We then present the relative strengths and limitations (with respect to other measurements), and then compare the calculation method with experimentation. We close each of the two subjects with an advanced topic: Raman IR spectroscopy (and depolarization ratio), and indirect dipole coupling (a.k.a. spin-spin coupling). I've also made the last part available as a standalone presentation: http://www.slideshare.net/InonSharony/nmr-spinspin-splitting-using-gaussian03.
This presentation discusses the basic principles governing EEG Rhythm Generation, and discusses the various circuits that generate and maintain cerebral oscillations.
Introduction to modern methods and tools for biologically plausible modeling ...SSA KPI
AACIMP 2010 Summer School lecture by Ruben Tikidji-Hamburyan. "Physics, Chemistry and Living Systems" stream. "Introduction to Modern Methods and Tools for Biologically Plausible Modeling of Neurons and Neural Networks" course. Part 2.
More info at http://summerschool.ssa.org.ua
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For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
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Introduction to Modern Methods and Tools for Biologically Plausible Modelling of Neural Structures of Brain. Part 1
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 I
Ruben A. Tikidji – Hamburyan
rth@nisms.krinc.ru
2. Brain as an object of research
● System level – to research the brain as a
whole
● Structure level:
a) anatomical
b) functional
● Populations, modules and ensembles
● Cellular
● Subcellular
5. Structure level
Anatomical Functional
Methods of research and modeling
use and combine methods of both system and population levels
6. Populations, modules and ensembles
Research methods:
Focal macroelectrode records from intact brain
Marking by selective dyes
Specific morphological methods
7. Populations, modules and ensembles
Modeling methods:
Formal neural networks
Biologically plausible models:
Population or/and dynamical models
Models with single cell accuracy (detailed models)
8. Cellular and subcellular levels
Research methods:
Extra- and intracellular microelectrode records
Dyeing, fluorescence and luminescence microscopy
Slice and culture of tissue
Genetic research
Research with Patch-Clamp methods from cell as a whole up to
selected ion channel
Biochemical methods
9. Cellular and subcellular levels
Modeling methods:
Phenomenological models of single neurons and synapses
Models with segmentation and spatial integration of cell body
Models of neuronal membrane locus
Models of dynamics of biophysical and biochemical processes in
synapses
Models of intracellular components and reactions
Quantum models of single ion channels
10. Cellular and subcellular levels
Ramon-y-Cajal's paradigm.
Camillo Santiago
Golgi Ramon-y-Cajal
1885 1888 – 1891
11. Cellular and subcellular levels
Ramon-y-Cajal's paradigm.
Dendrite tree or arbor of neuron:
the set of neuron inputs
Soma of neuron
Axon hillock,
The impulse generating zone
Axon, the nerve:
output of neuron
27. Hodjkin-Huxley equations
Dynamics of gate variables
du
C =g K u−E K g Na u− E Na g L u− E L
dt
g K = g K n4 g Na = g Na m3 h
df
=1− f f u− f f u
dt
where f – n, m and h respectively
df 1
=− f − f ∞
dt
1 f u f u
u= ; f ∞ u= =
f u f u f u f u u
28. First activation and inactivation
functions.
α(u) β(u)
Hodgkin, A. L. and Huxley, A. F. 0.1−0.01u 2.5−0.1u
(1952). n
e1−0.1u −1 e 2.5− 0.1u −1
A quantitative description of ion
currents and its applications to 2.5−0.1u −u
m 4e 18
conduction and excitation in nerve e2.5−0.1u −1
membranes.
−u 1
J. Physiol. (Lond.), 117:500-544. h 0.07 e 20 3−0.1u
e 1
Citation from:Gerstner and Kistler «Spiking Neuron Models. Single Neurons, Populations, Plasticity» Cambridge University
Press, 2002
29. Non-plausibility of the most biologically
plausible model!
Threshold is depended upon speed of potential raising
Threshold adaptation under prolongated polarization.
31. The Zoo of Ion Channels
Gerstner and Kistler «Spiking Neuron Models. Single Neurons, Populations, Plasticity»
Cambridge University Press, 2002
du
C = I i∑ k I k t
dt
pk qk
I k t= g k m h u−E k
dm
=1−m m u−m m u
dt
dn
=1−n n u−n n u
dt
32. The Zoo of Ion Channels
Gerstner and Kistler «Spiking Neuron Models. Single Neurons, Populations, Plasticity»
Cambridge University Press, 2002
du
C = I i∑ k I k t
dt
pk qk
I k t= g k m h u−E k
dm
=1−m m u−m m u
dt
dn
=1−n n u−n n u
dt
33. Compartment model of neuron
du
C =∑i g i u− E i
dt
g m u− E m g A u−u' I
35. Cable equation
R L i xdx =u t , xdx −ut , x
i xdx −i x =
∂ 1
=C u t , x u t , x −I ext t , x
∂t RT
C = c dx, RL = rL dx, RT-1 = rT-1 dx, Iext(t, x) = iext(t, x) dx.
∂2 ∂ rL
ut , x =c r L ut , x u t , x −r L i ext t , x
∂x
2
∂t rT 2
2 и cr = τ ∂ ∂
rL/rT = λ L u t , x = 2 ut , x − 2 ut , x i ext t , x
∂t ∂x
36. Cell geometry and activity
∂
i xdx −i x =C u t , x ∑ [ g i t , uu t , x −E i ] −I ext t , x
∂t i
2
∂ ∂
ut , x =c r L ut , x r L ∑ [ g i t , uu t , x −E i ] −r L i ext t , x
∂x
2
∂t i
Ion channels from Mainen Z.F., Sejnowski T.J. Influence of dendritic structureon firing pattern in
modelneocortical neurons // Nature, v. 382: 363-366, 1996.
EL= –70, Ena= +50, EK= –90, Eca= +140(mV)
Na: m3h: αm= 0.182(u+30)/[1–exp(–(u+30)/9)] βm= –0.124(u+30)/[1–exp((u+30)/9)]
h∞= 1/[1+exp(v+60)/6.2] αh=0.024(u+45)/[1–exp(–(u+45)/5)]
βh= –0.0091(u+70)/[1–exp((u+70)/5)]
Ca: m2h: αm= 0.055(u + 27)/[1–exp(–(u+27)/3.8)] βm=0.94exp(–(u+75)/17)
αh= 0.000457exp( –(u+13)/50) βh=0.0065/[1+ exp(–(u+15)/28)]
KV: m: αm= 0.02(u – 25)/[1–exp(–(u–25)/9)] βm=–0.002(u – 25)/[1–exp((u–25)/9)]
KM: m: αm= 0.001(u+30)/[1-exp(–(u+30)/9)] βm=0.001 (u+30)/[1-exp((u+30)/9)]
KCa: m: αm= 0.01[Ca2+]i βm=0.02; [Ca2+]i (mM)
[Ca2+]i d[Ca2+]i /dt = –αICa – ([Ca2+]i – [Ca2+]∞)/τ; α=1e5/2F, [Ca2+]∞=0.1μM, τ=200ms
Raxial 150Ώcm (6.66 mScm)
37. Cell geometry and activity
Soma Dendrite
Na 20(pS/μm2) Na 20(pS/μm2)
Ca 0.3(pS/μm2) Ca 0.3(pS/μm2)
KCa 3(pS/μm2) KCa 3(pS/μm2)
KM 0.1(pS/μm2) KM 0.1(pS/μm2)
KV 200(pS/μm2) L 0.03(mS/cm2)
L 0.03(mS/cm2)
41. How to identify the neurons and
connections.
Bannister A.P.
Inter- and intra-laminar connections of
pyramidal cells in the neocortex
Neuroscience Research 53 (2005) 95–103
42. How to identify the neurons and
connections.
D. Schubert, R. Kotter, H.J. Luhmann, J.F. Staiger
Morphology, Electrophysiology and Functional Input
Connectivity of Pyramidal Neurons Characterizes a
Genuine Layer Va in the Primary Somatosensory
Cortex
Cerebral Cortex (2006);16:223--236
43. Neurodynamics and circuit of cortex
connections
West D.C., Mercer A., Kirchhecker S., Morris O.T.,
Thomson A.M.
Layer 6 Cortico-thalamic Pyramidal Cells
Preferentially Innervate Interneurons and
Generate Facilitating EPSPs
Cerebral Cortex February 2006;16:200--211
44. Neurodynamics and circuit of cortex
connections
Somogyi P., Tamas G., Lujan R., Buhl E.H.
Salient features of synaptic organisation in the cerebral cortex
Brain Research Reviews 26 (1998). 113 – 135