Biological Modeling of Neural Networkslcn...Biological Modeling of Neural Networks 1.1 Neurons and...
Transcript of Biological Modeling of Neural Networkslcn...Biological Modeling of Neural Networks 1.1 Neurons and...
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Neuronal Dynamics: Computational Neuroscience
of Single Neurons
Week 1 – neurons and mathematics:
a first simple neuron model
Wulfram Gerstner
EPFL, Lausanne, Switzerland
1.1 Neurons and Synapses:
Overview
1.2 The Passive Membrane - Linear circuit
- Dirac delta-function
1.3 Leaky Integrate-and-Fire Model
1.4 Generalized Integrate-and-Fire
Model
1.5. Quality of Integrate-and-Fire
Models
Biological Modeling of Neural Networks
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Biological Modeling of Neural Networks
1.1 Neurons and Synapses:
Overview
1.2 The Passive Membrane - Linear circuit
- Dirac delta-function
1.3 Leaky Integrate-and-Fire Model
1.4 Generalized Integrate-and-Fire
Model
1.5. Quality of Integrate-and-Fire
Models
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Neuronal Dynamics – 1.1. Neurons and Synapses/Overview
visual
cortex
motor
cortex
frontal
cortex
to motor
output
How do we recognize?
Models of cogntion
Weeks 10-14
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Neuronal Dynamics – 1.1. Neurons and Synapses/Overview
motor
cortex
frontal
cortex
to motor
output
10 000 neurons
3 km wires 1mm
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Neuronal Dynamics – 1.1. Neurons and Synapses/Overview
10 000 neurons
3 km wire 1mm
Signal:
action potential (spike)
action
potential
Ramon y Cajal
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Neuronal Dynamics – 1.1. Neurons and Synapses/Overview
Signal:
action potential (spike)
action
potential
Ca2+
Na+
K+
-70mV
Ions/proteins
Hodgkin-Huxley type models:
Biophysics, molecules, ions
(week 2)
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Neuronal Dynamics – 1.1. Neurons and Synapses/Overview
Signal:
action potential (spike)
action
potential
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Neuronal Dynamics – 1.1. Neurons and Synapses/Overview
u
Spike reception
Spike emission
-spikes are events
-triggered at threshold
-spike/reset/refractoriness
Postsynaptic
potential
synapse t
Integrate-and-fire models:
Formal/phenomenological
(week 1 and week 6+7)
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Noise and variability in integrate-and-fire models
iui j
Output
-spikes are rare events
-triggered at threshold
Subthreshold regime:
-trajectory of potential shows fluctuations
Spike emission
Random spike arrival
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Brain
electrode
Crochet et al., 2011
awake mouse, cortex, freely whisking,
Spontaneous activity in vivo
Neuronal Dynamics – membrane potential fluctuations
What is noise?
What is the neural code?
(week 8+9)
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Neuronal Dynamics – Quiz 1.1
A cortical neuron sends out signals
which are called:
[ ] action potentials
[ ] spikes
[ ] postsynaptic potential
In an integrate-and-fire model, when the
voltage hits the threshold:
[ ] the neuron fires a spike
[ ] the neuron can enter a state of
refractoriness
[ ] the voltage is reset
[ ] the neuron explodes
The dendrite is a part of the neuron
[ ] where synapses are located
[ ] which collects signals from other
neurons
[ ] along which spikes are sent to other
neurons
In vivo, a typical cortical neuron exhibits
[ ] rare output spikes
[ ] regular firing activity
[ ] a fluctuating membrane potential
Multiple answers possible!
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Neural Networks and Biological Modeling – 1.1. Overview
action
potential
Week 1: A first simple neuron model/
neurons and mathematics
Week 2: Hodgkin-Huxley models and
biophysical modeling
Week 3: Two-dimensional models and
phase plane analysis
Week 4: Two-dimensional models
Dendrites
Week 5,6,7: Associative Memory,
Learning, Hebb, Hopfield
Week 8,9: Noise models, noisy neurons
and coding
Week 10: Estimating neuron models for
coding and decoding
Week 11-14: Networks and cognitions
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Neural Networks and Biological modeling
http://lcn.epfl.ch/
Course: Monday : 9:15-13:00
A typical Monday:
1st lecture 9:15-9:50
1st exercise 9:50-10:00
2nd lecture 10:15-10:35
2nd exercise 10:35-11:00
3rd lecture 11:15 – 11:40
3rd exercise 12:15-13:00
moodle.eplf.ch
paper and pencil
paper and pencil
paper and pencil
OR interactive toy
examples on computerCourse of 4 credits = 6 hours of work per week
4 ‘contact’ + 2 homework
have your laptop
with you
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Neural Networks and Biological Modeling
Questions?
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Neuronal Dynamics: Computational Neuroscience
of Single Neurons
Week 1 – neurons and mathematics:
a first simple neuron model
Wulfram Gerstner
EPFL, Lausanne, Switzerland
1.1 Neurons and Synapses:
Overview
1.2 The Passive Membrane - Linear circuit
- Dirac delta-function
1.3 Leaky Integrate-and-Fire Model
1.4 Generalized Integrate-and-Fire
Model
1.5. Quality of Integrate-and-Fire
Models
Week 1 – part 2: The Passive Membrane
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Neuronal Dynamics – 1.2. The passive membrane
u
Spike emission
potential
synapse t
Integrate-and-fire model
electrode
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iui
Spike reception
I
j
Subthreshold regime
- linear
- passive membrane
- RC circuit
Neuronal Dynamics – 1.2. The passive membrane
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ui
I(t)
I(t)
Time-dependent input
u Math development:
Derive equation
Neuronal Dynamics – 1.2. The passive membrane
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Passive Membrane Model
I(t)
u
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Passive Membrane Model
iui
)()( tRIuuudt
drest
I
j
)();( restuuVtRIVVdt
d
Math Development:
Voltage rescaling
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Passive Membrane Model
)()( tRIuuudt
drest
( ); ( )rest
dV V RI t V u u
dt
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Passive Membrane Model/Linear differential equation
( );d
V V RI tdt
Free solution:
exponential decay
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( )u t
)()( tRIuuudt
drest
)();( restuuVtRIVVdt
d
( )I t
1( )I t
Step current input:
2( )I tPulse current input:
arbitrary current input: 3( )I t
Calculate the voltage,
for the
3 input currents
Neuronal Dynamics – Exercises NOW Start Exerc. at 9:47.
Next lecture at
10:15
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Passive Membrane Model – exercise 1 now
iui Step current input:
)()( tRIuuudt
drest
Linear equation
I(t)
impulse reception:
impulse response
function
Start Exerc. at 9:47.
Next lecture at
10:15
TA’s:
Carlos Stein
Hesam Setareh
Samuel Muscinelli
Alex Seeholzer
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Triangle: neuron – electricity - math
ui
)()( tRIuuudt
drest
( )I t
I(t)
u
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Pulse input – charge – delta-function
u
)()( tRIuuudt
drest
( )I t
( )I t
Pulse current input 0( ) ( )I t q t t
( )u t
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Dirac delta-function
u
)()( tRIuuudt
drest
( )I t
0( ) ( )I t q t t
0
0
0 0( ) ( ) ( )
t a
t a
f t f t t t dt
0
0
01 ( )
t a
t a
t t dt
( )I t
t
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Neuronal Dynamics – Solution of Ex. 1 – arbitrary input
)()( tRIuuudt
drest
0( )/( )
t tqu t e
c
( )/1( ) ( ') '
t
t t
restu t u e I t dtc
Single pulse
Arbitrary input
you need to know the solutions
of linear differential equations!
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Passive membrane, linear differential equation
u
)()( tRIuuudt
drest
( )I t
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Passive membrane, linear differential equation
u
)()( tRIuuudt
drest
( )I t
If you have difficulties,
watch lecture 1.2detour.
Three prerequisits:
-Analysis 1-3
-Probability/Statistics
-Differential Equations or Physics 1-3 or Electrical Circuits
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Neuronal Dynamics: Computational Neuroscience
of Single Neurons
Week 1 – neurons and mathematics:
a first simple neuron model
Wulfram Gerstner
EPFL, Lausanne, Switzerland
1.1 Neurons and Synapses:
Overview
1.2 The Passive Membrane - Linear circuit
- Dirac delta-function
- Detour: solution of 1-dim linear
differential equation
1.3 Leaky Integrate-and-Fire Model
1.4 Generalized Integrate-and-Fire
Model
1.5. Quality of Integrate-and-Fire
Models
Week 1 – part 3: Leaky Integrate-and-Fire Model
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u
)()( tRIuuudt
drest
Neuronal Dynamics – 1.3 Leaky Integrate-and-Fire Model
)()( tRIuuudt
drest
( )I t
u
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-output spikes are events
-generated at threshold
-after spike: reset/refractoriness
Input spike causes an EPSP
= excitatory postsynaptic potential
Spike emission
uu
Neuronal Dynamics – Integrate-and-Fire type Models
Simple
Integate-and-Fire Model:
passive membrane
+ threshold
Leaky Integrate-and-Fire Model
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ui
)()( tRIuuudt
drest
u t Fire+reset
linear
threshold
Spike emission
reset I
j
ru u
Neuronal Dynamics – 1.3 Leaky Integrate-and-Fire Model
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ui
-spikes are events
-triggered at threshold
-spike/reset/refractoriness
I(t)
I(t)
Time-dependent input
Math development:
Response to step current
Neuronal Dynamics – 1.3 Leaky Integrate-and-Fire Model
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ui
I(t)
I(t)
CONSTANT input/step input
I(t)
Neuronal Dynamics – 1.3 Leaky Integrate-and-Fire Model
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Leaky Integrate-and-Fire Model (LIF)
0)( RIuuudt
drest
LIF ru uIf
‘Firing’
u
Repetitive, current I0
t
u
Repetitive, current I1> I0
t
T
I
1/T frequency-current
relation
u t
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Neuronal Dynamics – First week, Exercise 2
)()( tRIuuudt
drest
I
1/T
frequency-current
relation
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0)( RIuuudt
drest LIF ru uIf firing:
u
repetitive
t
Exercise!
Calculate the
interspike interval T
for constant input I.
Firing rate is f=1/T.
Write f as a function of I.
What is the
frequency-current curve
f=g(I) of the LIF?
EXERCISE 2 NOW: Leaky Integrate-and-fire Model (LIF)
Start Exerc. at 10:55.
Next lecture at
11:15
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Neuronal Dynamics: Computational Neuroscience
of Single Neurons
Week 1 – neurons and mathematics:
a first simple neuron model
Wulfram Gerstner
EPFL, Lausanne, Switzerland
1.1 Neurons and Synapses:
Overview
1.2 The Passive Membrane - Linear circuit
- Dirac delta-function
1.3 Leaky Integrate-and-Fire Model
1.4 Generalized Integrate-and-Fire
Model
1.5. Quality of Integrate-and-Fire
Models
Week 1 – part 4: Generalized Integrate-and-Fire Model
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Neuronal Dynamics – 1.4. Generalized Integrate-and Fire
Integrate-and-fire model
Spike
emission
reset
LIF: linear + threshold
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Neuronal Dynamics – 1.4. Leaky Integrate-and Fire revisited
LIF
ru u
If firing:
I=0 u
dt
d
u
I>0 u
dt
d
u
resting
t
u repetitive
t
)()( tRIuuudt
drest
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Neuronal Dynamics – 1.4. Nonlinear Integrate-and Fire
)()( tRIuFudt
d
)()( tRIuuudt
drest
LIF
NLIF
resetuu
If firing:
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Neuronal Dynamics – 1.4. Nonlinear Integrate-and Fire
)()( tRIuFudt
d
NLIF
ru u
firing:
I=0
u
udt
dI>0 u
dt
d
u tu
Nonlinear
Integrate-and-Fire
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Nonlinear Integrate-and-fire Model
iui
r
)()( tRIuFudt
d
ru t Fire+reset
NONlinear
threshold
Spike emission
reset I
j
F
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Nonlinear Integrate-and-fire Model
)()( tRIuFudt
d
ru t Fire+reset
NONlinear
threshold
I=0 u
dt
d
u
r
I>0 u
dt
d
u
r
Quadratic I&F:
02
12 )()( ccucuF
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Nonlinear Integrate-and-fire Model
)()( tRIuFudt
d
Fire+reset
I=0 u
dt
d
u
r
I>0 u
dt
d
u
r
Quadratic I&F:
)exp()()( 0 ucuuuF rest
exponential I&F:
02
12 )()( ccucuF
ru t
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Nonlinear Integrate-and-fire Model
)()( tRIuFudt
d
ru t Fire+reset
NONlinear
threshold
I=0 u
dt
d
u
r
)()( tRIuuudt
drest
exponential I&F:
)exp(
)()(
0
uc
uuuF rest
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Nonlinear Integrate-and-fire Model
)()( tRIuFudt
d
I=0 u
dt
d
u
I>0 u
dt
d
u
rr
Where is the firing threshold?
u
resting
t
t
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Neuronal Dynamics: Computational Neuroscience
of Single Neurons
Week 1 – neurons and mathematics:
a first simple neuron model
Wulfram Gerstner
EPFL, Lausanne, Switzerland
1.1 Neurons and Synapses:
Overview
1.2 The Passive Membrane - Linear circuit
- Dirac delta-function
1.3 Leaky Integrate-and-Fire Model
1.4 Generalized Integrate-and-Fire
Model - where is the firing threshold?
1.5. Quality of Integrate-and-Fire
Models - Neuron models and experiments
Week 1 – part 5: How good are Integrate-and-Fire Model?
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Neuronal Dynamics – 1.5.How good are integrate-and-fire models?
u
( ) ( )d
u F u RI tdt
u
r rif u then u u Can we compare neuron models
with experimental data?
( )I t
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Neuronal Dynamics – 1.5.How good are integrate-and-fire models?
Can we compare neuron models
with experimental data?
What is a good neuron model?
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Neuronal Dynamics – 1.5.How good are integrate-and-fire models?
I(t)
Spike
Integrate-and-fire model
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Nonlinear Integrate-and-fire Model
)()( tRIuFudt
d
Fire+reset
I=0 u
dt
d
u
r
Quadratic I&F:
)exp()()( 0 ucuuuF rest
exponential I&F:
02
12 )()( ccucuF
ru t
Can we measure
the function F(u)?
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Neuronal Dynamics – 1.5.How good are integrate-and-fire models?
Badel et al., J. Neurophysiology 2008
voltage
u [mV]
)exp()()( u
restuuuFI(t)
1)()(
1uFtI
Cdt
du
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Neuronal Dynamics – 1.5.How good are integrate-and-fire models?
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Neuronal Dynamics – 1.5.How good are integrate-and-fire models?
Nonlinear integrate-and-fire models
are good
Mathematical description prediction
Need to add
- adaptation
- noise
- dendrites/synapses
Computer ecercises:
Python
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Neural Networks and Biological - Exercise 3
Homework!
)()( tRIuFudt
d
I=0 u
dt
d
u
r
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Neuronal Dynamics – References and Suggested Reading Reading: W. Gerstner, W.M. Kistler, R. Naud and L. Paninski,
Neuronal Dynamics: from single neurons to networks and
models of cognition. Chapter 1: Introduction. Cambridge Univ. Press, 2014
Selected references to linear and nonlinear integrate-and-fire models
- Lapicque, L. (1907). Recherches quantitatives sur l'excitation electrique des nerfs traitee
comme une polarization. J. Physiol. Pathol. Gen., 9:620-635.
-Stein, R. B. (1965). A theoretical analysis of neuronal variability. Biophys. J., 5:173-194.
-Ermentrout, G. B. (1996). Type I membranes, phase resetting curves, and synchrony.
Neural Computation, 8(5):979-1001.
-Fourcaud-Trocme, N., Hansel, D., van Vreeswijk, C., and Brunel, N. (2003). How spike
generation mechanisms determine the neuronal response to fluctuating input.
J. Neuroscience, 23:11628-11640.
-Badel, L., Lefort, S., Berger, T., Petersen, C., Gerstner, W., and Richardson, M. (2008).
Biological Cybernetics, 99(4-5):361-370.
- Latham, P. E., Richmond, B., Nelson, P., and Nirenberg, S. (2000). Intrinsic dynamics in
neuronal networks. I. Theory. J. Neurophysiology, 83:808-827.
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Neuronal Dynamics –
THE END
MATH DETOUR SLIDES
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Neuronal Dynamics: Computational Neuroscience
of Single Neurons
Week 1 – neurons and mathematics:
a first simple neuron model
Wulfram Gerstner
EPFL, Lausanne, Switzerland
1.1 Neurons and Synapses:
Overview
1.2 The Passive Membrane - Linear circuit
- Dirac delta-function
- Detour: solution of 1-dim linear
differential equation
1.3 Leaky Integrate-and-Fire Model
1.4 Generalized Integrate-and-Fire
Model
1.5. Quality of Integrate-and-Fire
Models
Week 1 – part 2: Detour/Linear differential equation
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u
)()( tRIuuudt
drest
( )I t
Neuronal Dynamics – 1.2Detour – Linear Differential Eq.
)()( tRIuuudt
drest
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( )I t
( )u t
Neuronal Dynamics – 1.2Detour – Linear Differential Eq.
I(t)
u
)()( tRIuuudt
drest
t
Math development:
Response to step current
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( )I t
( )u t
Neuronal Dynamics – 1.2Detour – Step current input
I(t)
u
)()( tRIuuudt
drest
t
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( )I t
( )u t
Neuronal Dynamics – 1.2Detour – Short pulse input
I(t)
u
)()( tRIuuudt
drest
t
0( )/
0( ) 1t t
restu t u RI e
0t
short pulse: 0( )t t
restu
0( ) ( )I t q t t
Math development:
Response to short
current pulse
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( )I t
( )u t
Neuronal Dynamics – 1.2Detour – Short pulse input
I(t)
u
)()( tRIuuudt
drest
t
0( )/
0( ) 1t t
restu t u RI e
0t
short pulse: 0( )t t
restu
0( )/( )
t t
rest
qu t u e
C
0( ) ( )I t q t t
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( )I t
( )u t
Neuronal Dynamics – 1.2Detour – arbitrary input
)()( tRIuuudt
drest
t
0( )/( )
t t
rest
qu t u e
C
( )/1( ) ( ') '
t
t t
restu t u e I t dtC
Single pulse
Multiple pulses:
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Neuronal Dynamics – 1.2Detour – Greens function
)()( tRIuuudt
drest
0( )/1( )
t tu t q e
C
( )/1( ) ( ') '
t
t t
restu t u e I t dtC
Single pulse
Multiple pulses:
0
( )/
0
1( ) [ ( ) ] ( ') '
t
t t
rest rest
t
u t u u t u e I t dtC
( )I t
( )u t
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Neuronal Dynamics – 1.2Detour – arbitrary input
)()( tRIuuudt
drest
0( )/( )
t tqu t e
c
( )/1( ) ( ') '
t
t t
restu t u e I t dtc
Single pulse
Arbitrary input
you need to know the solutions
of linear differential equations!
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Neuronal Dynamics – Exercises 1.2/Quiz 1.2
If you don’t feel at ease yet,
spend 10 minutes on these
mathematical exercises