How everything started…
Direct link between brain and computer.
1985:
Peter Fromherz,
Max Planck Institute of Biochemistry
How to design a neuron-silicon junction?
Neuro-Transistors– JASS 2005
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Content
1.
Neuronal signaling
i.
ii.
iii.
2.
Neuron architecture
Membrane potential
Action potential
Neuro-Transistors
i.
ii.
iii.
iv.
Point-Contact Model
Transistor recording
Capacitive stimulation
Two neurons and a chip
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Architecture of a neuron
Basic functional units:
Input component
Dendrites
Trigger component
Cell body (soma)
Long-range conducting component
Axon
Output component
Presynaptic terminals
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Signaling
Neurons communicate by electrical signaling.
Action potential:
Brief, invariant and
large electrical pulse
All-or-none signal
Frequency-coded
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The cell membrane
Double layer of hydrophobic lipid molecules
Membrane proteins:
1. voltage-gated ion channels
2. ligand-gated (chemicallycontrolled) ion channels
3. energy consuming ion pumps
Control transport of ions through
the cell membrane.
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Membrane Potential
Separation of charges across membrane
Membrane potential: VM  Vin  Vout
Potential determined by:
Ionic conductances of the cell membrane
Distribution of ions across the membrane
(mainly potassium K  and sodium Na  )
Reduction in charge separation: Depolarization
Increase in charge separation: Hyperpolarization
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Membrane Potential
K  -ions concentrated
Charge separation
across the membrane.
inside the cell.
Chemical force driving
them outside down
concentration gradient.
Fchem
Fel
Potential difference across
the membrane driving
K  -ions back into the cell.
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Membrane Potential
Equilibrium:
Chemical force = Electrical force
Equilibrium potential:
Giant squid axon:
VK 
VK 
k B  T  K o 

 ln 
z e
 K i 
Nernst Equation
25mV  20 
 ln 
  75mV
1
 400 
Passive process, consumes no energy.
Energy is needed to set up initial concentration gradients.
Ion pumps: Proteins in cell membrane.
Ion transport through hydrolysis of ATP to ADP.
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Equilibrium with several ion species:
Ion flux = (electrical force + chemical force) x membrane conductance
Influence of each ion species by concentration gradient
and permeability of membrane
Membrane potential described by Goldman-Hodgkin-Katz equation:
 
 
 
 
 
 
k B  T  PK  K  o  PNa  Na  o  PCl  Cl  i 

Vm 
 ln 



e
 PK  K i  PNa  Na i  PCl  Cl o 
Vm
GHK Equation
usually around -60mV.
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Generation of an action potential
Membrane potential and ionic conductances
computed from the Hodgkin-Huxley model.
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Recording electrical signals from neurons:
Small glass micropipettes (d < 1µm) filled with concentrated salt solution
are inserted into the cell.
Connection via an amplifier to an oscilloscope.
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Voltage-clamp technique:
Difficult to examine ion conductances g K and g Na
because of their strong voltage dependence.
Holding (clamping) the potential
in the cell at a certain value.
Opening of voltage-gated ion channels
does not affect membrane potential.
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Patch-Clamp Technique
Allows measurement of currents
through single ion channels.
Suction
Seal between electrode and membrane.
Reduction of electronic noise.
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Signal Propagation along the Axon
Axon can be described
as one-dimensional cable.
Cell membrane: insulating coat
Intracellular fluid: conductive core
Conduction velocity depends on:
Diameter of axon
(Giant squid axon d=1mm)
Insulation of axonal membrane
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Myelination
Myelin: electrically insulating layer around axons.
Conduction velocity:
Unmyelinated: v = 5 to 10 m
Myelinated: up to v = 150 m
Nodes of Ranvier
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s
s
Synapses
Chemical signal transmission
across the synaptic cleft.
Action potential
Neurotransmitter release
Neural plasticity:
Regulation of synaptic strength.
Learning and memory
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Where to go?
Today: Trying to understand fundamental principles
of neuron-silicon junctions.
Physical rationalization of junction to
optimize neuron-silicon interfacing
Hybrid systems with neuronal networks
and microelectronic devices.
?
………..
Today
Science-Fiction
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Principles of coupling
(a) Electrical field across the membrane
polarizes the silicon dioxide on the chip.
(b) Electrical field across the silicon dioxide
polarizes the membrane affecting voltagegated ion-channels.
Unfortunately: Proteins from cell membrane keep
membrane at certain distance from
the chip.
(c) Neuronal activity leads to ionic and displacement
currents through the membrane.
Current spreads along the cleft.
Transductive Extracellular Potential (TEP)
(d) Voltage transient applied to silicon causes
displacement current through oxide.
Transductive Extracellular Potential (TEP)
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Point-Contact Model
Transductive extracellular potential mediates coupling of neuron and silicon.
TEP is determined by current balance in junction.
Point-Contact Model
Capacitance of the Membrane:
CM , cM 
CM
AJM
Ionic conductances:
G
i
JM
i
, g JM
i
GJM

AJM
Capacitance of the chip: CS , cS 
CS
AJM
Conductance of the cleft: G J , g J 
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GJ
AJM
Point-Contact Model
i
I Mionic   g JM
 VM  VJ  V0i 
dV 
 dV
I Mcap  cM   M  J 
dt 
 dt
i
dV 
 dV
I Scap  cS   S  J 
dt 
 dt
ohmic
I cleft
 g J  VJ  VE 
Kirchhoff‘s law: I cleft  I S
ohmic
cap
 I Mcap  I Mionic
 dV dV 
g J  VJ  VE   cS   S  J   cM
dt 
 dt
dV 
 dV
i
  M  J    g JM
 VM  VJ  V0i 
dt  i
 dt
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Point-Contact Model
AFM :Area of the free membrane.
AJM :Area of the attached membrane.
Current balance in the cell:
AJM

 dVM dVJ 
i
i 
 cM  

   g JM  VM  VJ  V0 
dt  i
 dt






dV 
 dV
i
  AFM  cM   M  E    g FM
 VM  VE  V0i 
dt  i
 dt



Neuro-Transistors– JASS 2005

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Point-Contact Model
Remarks:
i
ionic conductances g JM depend on voltage difference
across the membrane (Hodgkin-Huxley-Model).
point-contact model assumes that all currents flow
through one point in the membrane.
Parameters g J , cM , cS represent average values.
point-contact model is a simplification of an area-contact model
where VJ x, y  depends on the position x, y  in the junction.
Efficient recording and stimulation:
small distance d J
high specific resistance  J
large radius a J
small g J
high ionic conductances
high capacitance of the chip
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Transistor recording
Stimulation voltage
Electrolyte-source-voltage
VES
VDS
Source-drain-voltage
Source-drain current I D is controlled by gate-source voltage VGS  VJ  VS
Resulting current is changed to a voltage, amplified and watched on an osciloscope.
Calibration measurement without cell to determine voltage-current characteristic I D VGS 
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Ac-stimulation with transistor recording:
Leech neuron on FET contacted with patch-pipette in whole cell configuration.
Ac-voltage VM t  is amplified.
Response VJ t  recorded with transistor.
VJ
Plot of transfer spectrum
:
VM
Two different types of spectra observed:
A-type: - small amplitude at low frequencies.
- increase of phase around 10Hz.
- increase of amplitude above 1000Hz.
B-type: - high amplitude at low frequencies.
- just minor change in phase.
- further increase of amplitude above 1000Hz.
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Interpretation using the point-contact-model:
Insert intracellular stimulation VM t   V M  e and extracellular response
it
i
VJ t   V J  eit in g J  VJ  VE   cS   dVS  dVJ   cM   dVM  dVJ    g JM
 VM  VJ  V0i 
 dt
with
VE  0
dVS
0
dt
and
i
g JM
 dt
dt 
i
g JM
No ion channels, just leak conductance.
dV
dV
VJ   g J  g JM   J  cS  cM   VM  g JM  cM  M
dt
dt
VJ
Low frequency limit:
VM
dt 

0
VJ
g JM  icM

V M g J  g JM  i cS  cM 
g JM
g J  g JM
A-type: Small amplitude at low frequencies
low membrane conductance g JM
B-type: Enhanced amplitude at low frequencies
larger membrane conductance
Further increase at high frequencies
large conductance g J
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Ac-stimulation with transistor recording:
With the values cM  5 F cm 2 and cS  0.3 F cm 2 which are known,
data fitting gives us:
2
g J  217 mS cm 2
A-type: g JM  0.36 mS cm
2
B-type: g JM  38.5 mS cm
g J  40.8 mS cm 2
Crucial difference:
leak conductance of the attached
membrane differs by two orders
of magnitude.
A-type = Capacitive junction
B-type = Ohmic junction
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Transistor recording of neuronal activity:
Small signal approximation:
VJ  VM  V0i , dVJ  dVM
Small extracellular potential
dV 
 dV
cS   S  J   0
dt 
 dt
No capacitive current to the chip
g J VJ  cM 
1   M cM




dVM
i
  g JM
 VM  V0i
dt
i

dVM
i
i
  g FM
  M  g JM
 VM  V0i  1   M  jINJ
dt
i
M 
AFM
AJM
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Transistor recording of neuronal activity:
A-, B- and C-type response:
No voltage-gated ion channels in the membrane: g J VJ  g JM VM  V0   cM
Negligible leak conductance
dVM
dt
TEP proportional to first derivative
A-type junction
Dominating ohmic leak conductance
TEP reflects intracellular waveform
B-type junction
Insert cM
dVM
1
i
i



g FM
  M  g JM
 VM  V0i   jINJ

1   M  i
dt
in g J VJ 


 g JMi VM  V0i  cM
i
dVM
dt
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Transistor recording of neuronal activity:
g J VJ   g
i
i
JM
1
i
i
VM  V  1     g FM
 VM  V0i  jINJ
  M g JM
i
M
g J VJ 
i
0
1
i
i


g JM
 g FM
VM  V0i   jINJ

1   M  i
TEP of an action potential relies on inhomogeneity of the membrane.
Wide spectrum of waveforms VJ t  depending on distribution of
voltage-gated ion channels.
Details must be treated by numerical simulation.
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Transistor records of a leech neuron
Two positions of the neuron:
(a) Cell body right on the transistor.
(b) Axon stump on transistor array.
Action potential elicited by current
injection with a micropipette.
Intracellular potential measured with pipette, extracellular potential with transistor.
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Transistor records of a leech neuron
Three types of records:
A: first derivative of waveform
capacitive junction
B: waveform itself
ohmic junction
C: numerical simulation:


accumulation of K and Na -channels
in attached membrane.
Depletion of ion-channels in cell body.
High density of ion-channels in axon
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Transistor records of neurons from rat hippocampus
Action potentials are elicited by current injection.
Records with signal averaging of transistor signals.
Observation:
Two positive transients in VJ t  ,
one in rising phase of AP and
one in falling phase.
Interpretation:
Positive peak in rising phase is related with sodium current.
Na
With VM  V0  0
Na
Na
g JM
 g FM
0
Sodium inward current through free membrane gives rise
to capacitive outward current through attached membrane.
Positive peak in falling phase is related with outward
potassium current through attached membrane.
K
K
g JM
 g FM
0
VM  V0K  0
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Capacitive stimulation of neuronal activity
Changing voltage
VS t  applied to stimulation spot
Capacitive current through insulating oxide
Current along the cleft
Transductive extracellular potential TEP
Voltage-gated ion channels in the membrane may open
Action potential
VM t  may arise.
A-type stimulation:
Voltage step VS t   VS  t 
Exponential response of membrane potential.
0
VM t   V  e
0
M
 t
J
with very short time constant  J  3s
for mammalian neurons.
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Capacitive stimulation of neuronal activity
B-type stimulation: Voltage step VS t   VS  t 
0
Exponential response due
to capacitive effects.
VM  Vrest  V  e
0
M
 t
Also stationary change
of intracellular potential.
J
 Vstat
C-type stimulation:
VM t  depends on channel sorting.
Must be treated with numerical simulation.
Step stimulation of a leech neuron:
Voltage step with VS = 4.8, 4.9, 5.0V
0
Stimulation below threshold cannot
elicit an action potential (4.8V).
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Burst stimulation of snail neuron:
Excitation is achieved only when a
burst of voltage pulses is applied.
After each pulse VM t  responds with short
capacitive transients at rising and falling edge of VS t 
After the third pulse the intracellular potential
rises so that an action potential is elicited.
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Circuits with two neurons on a chip
Neuronal activity VM t 
Probing VJ t  with FET
Action potential
Signal processing
Capacitive stimulation
(i) Transformation of
and amplification.
(ii) Identification of an action potential
with a threshold device.
(iii) Delay line
(iv) Generation of a train of voltage
pulses.
(v) Suppression of crosstalk from
stimulator to transistor by
refractory unit.
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Circuits with two neurons on a chip
Connection between a spontaneously firing neuron A along the chip to a
separate neuron B.
After each action potential in neuron A a burst of voltage pulses is generated
and applied to neuron B.
Neuron B fires in correlation to neuron A.
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Signaling chip-neuron-neuron-chip
Action potential
Synaptic connection
Neuronal activity VM t 
Probing VJ t  with FET
Capacitive stimulation
Problem:
Growing neurites exert strong forces on the
cell bodies of neurons.
They pull them of their contacts.
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Mechanical fixation of the cell bodies
Picket fences made out of
polyimid (plastic) around
each contact.
Two neurons from an
immobilized network of
snail neurons.
Stimulation with burst of
seven voltage pulses.
Third action potential in neuron 1
leads to a postsynaptic excitation
in neuron 2.
Perturbations in transistor signal
due to capacitive coupling with
stimulator.
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Towards defined neuronal nets
Systematic experiments on network dynamics require:
(i) Noninvasive, long term supervision and stimulation of neurons
(ii) Fabrication of neuronal nets with defined topology of synaptic
connections.
Control of neuronal outgrowth:
1.) Chemical guidance:
Motion of neuronal outgrowth is
guided by chemical patterns.
Linear patterns of extracellular
matrix proteins are able to guide
neuronal outgrowth and let them
form synapses.
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Towards defined neuronal nets
2.) Topographical guidance
Grown neurites are immobilized by
microscopic grooves.
Cell bodies are placed in into the pits of
a polymer structure.
Neurites grow along the grooves and
split at bifurcations.
Problem: Neuritic tree is not uniquely
defined by the guiding pattern.
Alternative: Disordered growth of neuronal nets on closely packed
transistor arrays.
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Transistor arrays
12 neurons cultured on an array of 128x128 transistors ( 1mm 2).
Neurons I, II and III are connected by synapses.
Burst of action potentials elicited at Neuron I with a micropipette.
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Alternative materials
Drawbacks of silicon:
(i) Electrochemical instability of silicon dioxide
Long-term shift of electrical properties of the FETs.
(ii) High noise-level of Si-based devices.
Difficult to observe small signals from neurons.
Realization of EOFETs with AlGaN/GaN
heterostructure FETs.
These materials are stable under
physiological conditions.
Much higher signal-to-noise ratio than
Si-based devices.
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Alternative materials
Cardiac myocyte cells cultivated on surface of a AlGaN/GaN array
50m
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Summary and Outlook
Basic principles of neuron-silicon junctions are fairly understood.
Properties of the cleft
Transistor recording
Capacitive stimulation
Optimization of neuron-silicon contact:
Larger capacity of stimulation contacts
Lower noise of transistors
Deeper understanding of electrical properties of the cell membrane
Neuronal networks:
Small defined networks of neurons with learning synapses
Large networks of neurons on closely packed transistor arrays
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The End
Thank You for your Attention!
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FLIC Microscopy
Fluorescence interference contrast microscopy
Membrane labelled with fluorescent dye molecules.
Excitation and fluorescence of the dye depend on
the distance between membrane and silicon.
Membrane and silicon
dioxide are not in close
contact.
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