circuit models of neurons

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Bo Deng University of Nebraska-Lincoln ines: gkin-Huxley Model Circuit Models --- Elemental Characteristics --- Ion Pump Dynamics xamples of Dynamics --- Bursting Spikes --- Metastability and Plasticity --- Chaos --- Signal Transduction AMS Regional Meeting at KU 03-30-12

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Circuit Models of Neurons. Bo Deng University of Nebraska-Lincoln. Outlines : Hodgkin-Huxley Model Circuit Models --- Elemental Characteristics --- Ion Pump Dynamics Examples of Dynamics --- Bursting Spikes - PowerPoint PPT Presentation

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Page 1: Circuit Models of Neurons

Bo DengUniversity of Nebraska-Lincoln

Outlines: Hodgkin-Huxley Model Circuit Models --- Elemental Characteristics --- Ion Pump Dynamics Examples of Dynamics --- Bursting Spikes --- Metastability and Plasticity --- Chaos --- Signal Transduction

AMS Regional Meeting at KU 03-30-12

Page 2: Circuit Models of Neurons

Hodgkin-Huxley Model (1952)

Pros: The first system-wide model for excitable membranes. Mimics experimental data. Part of a Nobel Prize work. Fueled the theoretical neurosciences for the last 60 years and counting.

Page 3: Circuit Models of Neurons

Hodgkin-Huxley Model (1952)

Pros: The first system-wide model for excitable membranes. Mimics experimental data. Part of a Nobel Prize work. Fueled the theoretical neurosciences for the last 60 years and counting. Cons: It is not entirely mechanistic but phenomenological. Different, ad hoc, models can mimic the same data. It is ugly. Fueled the theoretical neurosciences for the last 60 years and counting.

Page 4: Circuit Models of Neurons

Hodgkin-Huxley Model --- Passive vs. Active Channels

80/

10/1

4

125.0

)1(10010

)1(

)(

Vn

Vn

nn

KKK

e

eV

nndtdn

VVngI

Page 5: Circuit Models of Neurons

Hodgkin-Huxley Model

11 ,07.0

4 ,)1(10

25

)1(

)1(

)(

10/320/

18/10/5.2

3

VhV

h

VmVm

hh

mm

NaNaNa

ee

ee

V

hhdtdh

mmdtdm

VVhmgI

Page 6: Circuit Models of Neurons

Hodgkin-Huxley Model

)( LLL VVgI

Page 7: Circuit Models of Neurons

Hodgkin-Huxley Model

dtdVCIC

Page 8: Circuit Models of Neurons

-I (t)CThe only mechanistic part ( by Kirchhoff’s Current Law)

+

Page 9: Circuit Models of Neurons

Hodgkin-Huxley Model --- A Useful Clue

VIdt

dIK

K

:conversionenergy mechanical tolbiochemica todue channel theofpower theis which voltage, theandcurrent

theofproduct ofsort someon dependscurrent theof change The channels active and passiveboth of aggregatean iscurrent Each

80/

10/1

4

125.0

)1(10010

)1(

)(

Vn

Vn

nn

KKK

e

eV

nndtdn

VVngI

Page 10: Circuit Models of Neurons

• Morris, C. and H. Lecar, Voltage oscillations in the barnacle giant muscle fiber, Biophysical J., 35(1981), pp.193--213.

• Hindmarsh, J.L. and R.M. Rose, A model of neuronal bursting using three coupled first order differential equations, Proc. R. Soc. Lond. B. 221(1984), pp.87--102.

• Chay, T.R., Y.S. Fan, and Y.S. Lee Bursting, spiking, chaos, fractals, and universality in biological rhythms, Int. J. Bif. & Chaos, 5(1995), pp.595--635.

H-H Type Models for Excitable Membranes

Page 11: Circuit Models of Neurons

Our Circuit Models Elemental Characteristics -- Resistor

gVI

Page 12: Circuit Models of Neurons

Our Circuit Models Elemental Characteristics -- Diffusor

funcitons decreasingboth )(or )( IhVVfI

Page 13: Circuit Models of Neurons

Our Circuit Models Elemental Characteristics -- Ion Pump

pumpway -onefor

AVdtdA

Page 14: Circuit Models of Neurons

Dynamics of Ion Pump as Battery Charger

Page 15: Circuit Models of Neurons

Na

K

' with

0:LawCurrent sKirchhoff’

C

extCApK,pNa,

CVCI

IIIII

Page 16: Circuit Models of Neurons

Equivalent IV-Characteristics --- for parallel channels

Passive sodium current can be explicitly expressed as

2

Na

NaNa2

12

21NaNaNa

21

NaNa

Na12

11NaNa

NapNa,

)(||2

))((||)('

gintegratin from derived

2 ,

||2with

tantan

)(

m

m

mm

vVg

dgvvvVvVdgVf

vvvdg

gvv

μvvVdVg

VfI

Page 17: Circuit Models of Neurons

Equivalent IV-Characteristics --- for serial channels

Passive potassium current can be implicitly expressed as

A standard circuit technique to represent the hysteresis is to turn it into a singularly perturbed equation

0

Page 18: Circuit Models of Neurons

By Ion Pump Characteristics

with substitution and assumption

to get

Equations for Ion Pump

Page 19: Circuit Models of Neurons

2 ,

||||

2

tantan11)(

and

2 ,

||2

tantan)(

21

KK

K12

11

KKK

21

NaNa

Na12

11NaNaNa

iiidg

dii

iiId

Ig

Ih

vvvdg

gvv

μvvVdVgVf

m

mm

m

mm

I Na = fNa (VC – ENa)

VK = hK (IK,p)

Page 20: Circuit Models of Neurons
Page 21: Circuit Models of Neurons

Examples of Dynamics --- Bursting Spikes --- Chaotic Shilnikov Attractor --- Metastability & Plasticity --- Signal Transduction

Geometric Method of Singular Perturbation

Small Parameters: 0 < e << 1 with ideal hysteresis at e = 0 both C and have independent time scales

Page 22: Circuit Models of Neurons

C = 0.005

Bursting Spikes

Page 23: Circuit Models of Neurons

C = 0.005

C = 0.5

Neural ChaosC = 0.5 = 0.05 g = 0.18 e =

0.0005I

in = 0

gK = 0.1515

dK

= -0.1382i1 = 0.14

i2 = 0.52

EK

= - 0.7

gNa

= 1d

Na = - 1.22

v1 = - 0.8

v2 = - 0.1

ENa

= 0.6

Page 24: Circuit Models of Neurons

Griffith et. al. 2009

Page 25: Circuit Models of Neurons

Metastability and Plasticity

Terminology: A transient state which behaves like a steady state is referred to as metastable.

A system which can switch from one metastable state to another metastable state is referred to as plastic.

Page 26: Circuit Models of Neurons

Metastability and Plasticity

Page 27: Circuit Models of Neurons

Metastability and Plasticity

Page 28: Circuit Models of Neurons

All plastic and metastable states are lost with only one ion pump. I.e. when ANa = 0 or AK = 0 we have either Is = IA or Is = -IA and the two ion pump equations are reduced to one equation, leaving the phase space one dimension short for the coexistence of multispike burst or periodic orbit attractors.

With two ion pumps, all neuronal dynamics run on transients, which represents a paradigm shift from basing neuronal dynamics on asymptotic properties, which can be a pathological trap for normal physiological functions.

Metastability and Plasticity

Page 29: Circuit Models of Neurons
Page 30: Circuit Models of Neurons

Saltatory Conduction along Myelinated Axon with Multiple Nodes

Inside the cell

Outside the cell

Joint work with undergraduate and graduate students: Suzan Coons, Noah Weis, Adrienne Amador, Tyler Takeshita, Brittney Hinds, and Kaelly Simpson

Page 31: Circuit Models of Neurons

Coupled Equations for Neighboring Nodes

• Couple the nodes by adding a linear resistor between them

1 2 11 1 1 1

11

1 1 1

11 1 1

11 1 1

2 2 12 2 2 2

12

2 2 2

2

C C Cext Na K KC A

AS C A

SA C A

NaNa Na NaC

C C CNa K KC A

AS C A

S

dV V VC I I f V E I RdtdI

I V IdtdI

I V IdtdI V E h IdtdV V V

C I f V E I RdtdI

I V IdtdI

g

g

e

g

2 2 2

22 2 2

A C A

NaNa Na NaC

I V IdtdI V E h I

dt

g

e

Current between the

nodes

Page 32: Circuit Models of Neurons

The General Case for N Nodes

This is the general equation for the nth node

In and out currents are derived in a similar manner:

1n

n n n n n nCout inNa K KC A

nn n nAS C A

nn n nSA C A

nn n nNa

Na Na NaC

dVC I I f V E I I

dtdI

I V Idt

dII V I

dtdI

V E h Idt

g

g

e

1 1

1

1

if 1

if 1

if 1

0 if

extn n nout C C

n

n nC Cn nin

I nI V V

nR

V Vn NI R

n N

Page 33: Circuit Models of Neurons

C=.1 pF C=.7 pF

(x10 pF)

Page 34: Circuit Models of Neurons

Transmission SpeedC=.01 pFC=.1 pF

Page 35: Circuit Models of Neurons

Closing Remarks: The circuit models can be further improved by dropping the serial connectivity assumption of the passive electrical and diffusive currents. Existence of chaotic attractors can be rigorously proved, including junction-fold, Shilnikov, and canard attractors.

Can be easily fitted to experimental data.

Can be used to build real circuits.

References: [BD] A Conceptual Circuit Model of Neuron, Journal of Integrative

Neuroscience, 8(2009), pp.255-297. Metastability and Plasticity of Conceptual Circuit Models of

Neurons, Journal of Integrative Neuroscience, 9(2010), pp.31-47.

• Kandel, E.R., J.H. Schwartz, and T.M. Jessell Principles of Neural Science, 3rd ed., Elsevier, 1991.• Zigmond, M.J., F.E. Bloom, S.C. Landis, J.L. Roberts, and L.R. Squire Fundamental Neuroscience, Academic Press, 1999.

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