Basic Models in Theoretical Neuroscience Oren Shriki 2010 Differential Equations.

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Basic Models in Theoretical Neuroscience Oren Shriki 2010 Differential Equations

Transcript of Basic Models in Theoretical Neuroscience Oren Shriki 2010 Differential Equations.

Basic Models in Theoretical Neuroscience

Oren Shriki

2010

Differential Equations

Two Types of Dynamical Systems

• Differential equations:Describe the evolution of systems in continuous time.

• Difference equations / Iterated maps:Describe the evolution of systems in discrete time.

What is a Differential Equation?• Any equation of the form:

• For example:

0,,,2

2

dx

yd

dx

dyyF

0644

4

dx

ydy

dx

dy

Order of a Differential Equation

• The order of a differential equation is the order of the highest derivative in the equation.

• A differential equation of order n has the form:

0,,,,2

2

n

n

dx

yd

dx

yd

dx

dyyF

1st Order Differential Equations

• A 1st order differential equation has the form:

• For example:

yxfdx

dy,

yxdx

dy 2

Separable Differential Equations

• Separable equations have the form:

• For example:

yhxgdx

dy

yxdx

dy 2

Separable Differential Equations

• How to solve separable equations?

• If h(y)≠0 we can write:

• Integrating both sides with respect to x we obtain:

xgyh

xy

'

dxxgdxxyyh

'1

Separable Differential Equations

• By substituting:

• We obtain:

dxxydy

xyx

'

dxxgyh

dy

Example 1

xeydx

dy 21

Cey

Cey

dxey

dy

x

x

x

tan

tan

1

1

2

Example 2

xyydx

dye x sinln

xdxeyy

dy x sinln

1lnlnln

Cyyy

dy

Integrating the left side:

Example 2 (cont.)

2cossin2

1sin

sincossincossinsin

Cxxexdxe

xdxexexexdxexexdxe

xx

xxxxxx

Cxxey

Cxxey

x

x

cossin2

1expexp

cossin2

1lnln

Integrating the right side:

Thus:

Linear Differential Equations

• The standard form of a 1st order linear differential equation is:

• For example:

xQyxPdx

dy

xyxdx

dysin

Linear Differential Equations

General solution:

• Suppose we know a function v(x) such that:

• Multiplying the equation by v(x) we obtain:

yxvxPdx

dyvyxv

dx

d

dxxQxvxv

y

xQxvyxvdx

d

xQxvyxvxPdx

dyxv

1

)(

Linear Differential Equations

• The condition on v(x) is:

• This leads to:

yxvxPdx

dyvyxv

dx

d

yxvxPdx

dvy

yxvxPdx

dyv

dx

dvy

dx

dyv

Linear Differential Equations

• The last equation will be satisfied if:

• This is a separable equation:

xvxPdx

dv

dxxPev

dxxPv

dxxPv

dv

ln

Linear Differential Equations

• To sum up:

• Where:

dxxQxvxv

y

xQyxPdx

dy

1

dxxPexv

Example

• Solution:

xeyxdx

dyx sinhcosh

xeeexv xdxxdxxPcosh)cosh(ln)tanh()(

x

eyx

dx

dy x

coshtanh

)cosh(x

exQ

x

Example (cont.)

dxx

ex

x

dxxQxvxv

y

x

cosh)cosh(

)cosh(

1

1

Cex

xy x

cosh

1)(

Derivative with respect to time

• We denote (after Newton):

dt

dxx