Southern Federal University Laboratory of neuroinformatics of sensory and motor systems A.B.Kogan Research Institute for Neurocybernetics Ruben A. Tikidji – Hamburyan [email protected]Introduction to modern methods and tools for biologically plausible modeling of neural structures of brain Part II
34
Embed
Introduction to Modern Methods and Tools for Biologically Plausible Modelling of Neural Structures of Brain. Part 2
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Southern Federal University
Laboratory of neuroinformatics ofsensory and motor systems
Introduction to modern methods and tools for biologically plausible modeling
of neural structures of brain
Part II
Previous lecture 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 lecture in a nutshell
Phenomenological models of neuronIs it possible to model only phenomena of neuronal activity
without detailed consideration of electrical genesis?
Hodjkin-Huxley style models
Integrate-and-Fire style models
Acc
urac
y ne
uron
des
crip
tion
Sim
plifi
catio
n
Sop
hist
icat
ion
Reduction of base equations or/and number of compartments
or/and simplification of equations for currents
Spe
ed u
p an
d di
men
sion
of
net
wor
k
Description of neuron dynamics by formal function
FitzHugh-Nagumo's modelR. FitzHugh«Impulses and physiological states in models of nerve membrane» Biophys. J., vol. 1, pp. 445-466, 1961.
v '=ab vc v2d v3
−u u'= e v−u
Izhikevich's model
v '=0.04 v25v140−u
u '=a bv−uif v30 then v=c ,u=ud
where a,b,c,d – model parameters
Eugene M. Izhikevich«Which Model to Use for Cortical Spiking Neurons?»IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 5, SEPTEMBER 2004
Izhikevich's model
Integrate-and-Fire model
⌠│dt⌡
dudt
=∑ I syn−u t
Simple integrator:
Threshold function – short circuit of membrane:
if u thenu=0
Integrate-and-Fire model
⌠│dt⌡
dudt
=∑ I syn−u t
Simple integrator:
Threshold function – short circuit of membrane:
if u thenu=0
Master and slave integrators
du t dt
=rI t rrap
uap t −u t −ut ap
duap
dt=
1ap
u t −uap t
Adaptive threshold
dui t
dt={
ar
ut −uit if u t ui t
a f
u t −ui t if u t ui t =uit cth
−−
+=
<−<−−−
+=
<−+−−
+=
случаяхостальныхвсехвоtu
CR
tututI
Cdt
tdu
ttеслиUtu
CR
tututI
Cdt
tdu
ttеслиUtu
CR
tututI
Cdt
tdu
ap
ap
firefire
fire
s
sap
ap
fire
fire
s
sap
ap
τ
)()()()(
1)(
τ'2τ
τ
2
τ
)()()()(
1)(
2τ
'τ
2
τ
)()()()(
1)(
Pulse generator:
Modified Integrate-and-Fire model
Modified Integrate-and-Fire model
Modified Integrate-and-Fire model
Com
para
tive
char
acte
ristic
s of
ne
uron
mod
els
by
Izhi
kevi
ch
Synapses: chemical and electrical
Synapses: chemical and electrical
Chemical synapse models (ion model)
I s=g s u−E s g st =g st s−t
g st =g su ps , t
Phenomenological models
u ps , t =1−1
1expups−
u ps , t =1−1
1exp upst− t −
g st =g su ps , t ,[Ma2+]o ,
u ps , t =P u ps ,t
u ps , t ,[Ma2+]o=u ps , t g∞
g∞=1expu [Ma2+]o
−1
Chemical synapse models (Phenomenological models)
I s={ 0 if tt s
e t s−t if otherI s={
0 if tt s
t s−t
exp1− t s−t if other
I s={0 if tt s
e
t s−t1 −e
t s−t2
1−2
if other
dmi t
dt={
ms
r
−mi
f
if t−t sr
−mi
f if t−t sr
I s=mi t
Learning, memory and neural networksGerald M. Edelman
The brain is hierarchy of non-degenerate neural group
The Group-Selective Theory of Higher Brain
Function
Learning, memory and neural networks
Sporns O., Tononi G., Edelman G.M.
Theoretical Neuroanatomy: Relationg Anatomical and Functional Connectivity in Graphs and Cortical Connection Matrices
Cerebral Cortex, Feb 2000; 10: 127 - 141
Learning, memory and neural networks
Gerald M. Edelman – Brain Based Device (BBD)
Krichmar J.L., Edelman G.M. Machine Psychology: Autonomous Behavior, Perceptual Categorization and Conditioning in a Brain-based Device Cerebral Cortex Aug. 2002; v12: n8 818-830
Learning, memory and neural networks
Gerald M. Edelman – Brain Based Device (BBD)
McKinstry J.L., Edelman G.M., Krichmar J.K.
An Embodied Cerebellar Model for Predictive Motor Control Using Delayed Eligibility Traces
Computational Neurosci. Conf. 2006
Learning, memory and single neuron
Donald O. Hebb
Learning, memory and single neuron
Guo-qiang Bi and Mu-ming Poo
Synaptic Modifications in Cultured Hippocampal Neurons:Dependence on Spike Timing, Synaptic Strength, andPostsynaptic Cell Type
The Journal of Neuroscience, 1998, 18(24):10464–1047
Long Term Depression(LTD)
Long-Term Potentiation(LTP)
Spike Time-Dependent Plasticity(STDP)
Learning, memory and single neuron
Gerald M. Edelman – Experimental research
Vanderklish P.W., Krushel L.A., Holst B.H., Gally J. A., Crossin K.L., Edelman G.M.
Marking synaptic activity in dendritic spines with a calpain substrate exhibiting fluorescence resonance energy transfer
PNAS, February 29, 2000, vol. 97, no. 5, p.2253 2258
Learning and local calcium dynamicsFeldman D.E.
Timing-Based LTP and LTD at Vertical Inputsto Layer II/III Pyramidal Cells in Rat Barrel Cortex
Neuron, Vol. 27, 45–56, (2000)
Learning and local calcium dynamicsShouval H.Z., Bear M.F.,Cooper L.N.
A unified model of NMDA receptor-dependentbidirectional synaptic plasticity
PNAS August 6, 2002 vol. 99 no. 16 10831–10836
Learning and local calcium dynamicsMizuno T., KanazawaI., Sakurai M.
Differential induction of LTP and LTD is not determinedsolely by instantaneous calcium concentration: anessential involvement of a temporal factor
European Journal of Neuroscience, Vol. 14, pp. 701-708, 2001
Kitajima T., Hara K.
A generalized Hebbian rule for activity-dependent synaptic modification
Neural Network, 13(2000) 445 - 454
Learning and local calcium dynamics
Learning and local calcium dynamics
Urakubo H., Honda M., Froemke R.C., Kuroda S.
Requirement of an Allosteric Kinetics of NMDA Receptors for Spike Timing-Dependent Plasticity
The Journal of Neuroscience, March 26, 2008 v. 28(13):3310 –3323
Learning and local calcium dynamics
Letzkus J.J., Kampa B.M., Stuart G.J.
Learning Rules for Spike Timing-Dependent PlasticityDepend on Dendritic Synapse Location
The Journal of Neuroscience, 2006 26(41):10420 –1042
Learning and local calcium dynamics
Letzkus J.J., Kampa B.M., Stuart G.J.
Learning Rules for Spike Timing-Dependent PlasticityDepend on Dendritic Synapse Location
The Journal of Neuroscience, 2006 26(41):10420 –1042