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Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
Neuron-computer interface in Dynamic-Clamp experiments
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Neuron-computer interface in Dynamic-Clamp experiments

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AACIMP 2010 Summer School lecture by Anton Chizhov. "Physics, Chemistry and Living Systems" stream. "Neuron-Computer Interface in Dynamic-Clamp Experiments. Models of Neuronal Populations and Visual …

AACIMP 2010 Summer School lecture by Anton Chizhov. "Physics, Chemistry and Living Systems" stream. "Neuron-Computer Interface in Dynamic-Clamp Experiments. Models of Neuronal Populations and Visual Cortex" course. Part 1.
More info at http://summerschool.ssa.org.ua

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  • 1. Neuron-computer interface in Dynamic-Clamp experiments Anton V. Chizhov A.F.Ioffe Physico-Technical Institute of Russian Academy Sciences, St.-Petersburg, Russia
  • 2. Leaky integrate-and-fire model Hodgkin-Huxley neuron model Control parameters of neuron Dynamic-clamp • Artificial synaptic current • Artificial voltage-dependent current • Synaptic conductance estimation
  • 3. Leaky Integrate-and-Fire neuron dV C   g L (V ( t )  V L )  i S V is the membrane potential; I is the input (synaptic) current, dt C is the membrane capacity; gL is the membrane conductance; Vrest is the rest potential; VT is the threshold If V  VT then V  Vreset potential; Vreset is the reset potential. C m  gL
  • 4. Firing rate dependence on current (F-I-curve) gL   V  i / g V  C ln L S L T  V  i / g V    L S L reset 
  • 5. V(x) r Внутри V(x+Δx) jm C im  g S (V  VL )  iS φ≈0 Снаружи h VNa V gNa gK Vrest VK [Покровский, 1978]
  • 6. Set of experimental data for Hodgkin-Huxley approximations
  • 7. Model of a pyramidal neuron dV C   I Na  I DR  I A  I M  I H  I L  I AHP  iS dt p q E X P Е R I М Е N Т I ...  g... x (t ) y (t ) (V (t )  V... ) dx x (U )  x  , dt  x (U ) dy y (U )  y  dt  y (U ) Approximations for I Na , I DR , I A , I M , I H are taken from [L.Graham, 1999]; IAHP is from [N.Kopell et al., 2000] Model with noise Color noise model for synaptic current IS is the Ornstein-Uhlenbeck process: diS 0   iS (t )  iS  2  (t ) dt
  • 8. E X P E R I M E N T Control parameters of a neuron dV ( t ) C   g Na m 3 (V , t )h (V , t )(V (t )  VNa )  dt 4  2V  g K n (V , t )(V (t )  VK )  g L (V ( t )  VL )  iS  k 2 x Voltage-gated channels kinetics: dm m (V )  m   MODEL dt  m (V ) dh h (V )  h [Hodgkin, Huxley, 1952]   dt  h (V ) dn n (V )  n   dt  n (V ) Property: Neuron is controlled by two parameters [Покровский, 1978] iS  GE (V  VE )  GI (V  VI )  I electrode   s (V  V0 )  u u  GE (VE  V0 )  GI (VI  V0 )  I electrode s  GE  GI
  • 9. The case of many voltage-independent synapses dV C   I ionic channels (V , t )   g S (t ) (V (t )  VS )  I el (t ) dt S s(t )   g S (t ) S u (t )   g S (t ) (VS  V0 )  I el (t ) S , dV C   I ionic channels (V , t )  s(t ) (V (t )  V0 )  u(t ), dt
  • 10. Warning! The input in current clamp corresponds to negative synaptic conductance! Cur rent -c lam p is her e !
  • 11. Whole-cell patch-clamp: Current- and Voltage-Clamp modes “Current clamp”, “Voltage clamp”, V(t) is registered I(t) is registered const
  • 12. Whole-cell patch-clamp: Dynamic-Clamp mode Conductance clamp (Dynamic clamp): V(t) is registered, I(V,t) = gDC (V,t) (V(t)-VDC) is injected • For artificial passive leaky channel gDC=const • For artificial synaptic channel gDC(t) reflects the synaptic kinetics • For voltage-gated channel gDC(V(t),t) is described by ODEs
  • 13. Conductance clamp (Dynamic clamp): “Current clamp” I(V(t))=gDC (V(t)-VDC) is injected
  • 14. Dynamic clamp for synaptic current [Sharp AA, O'Neil MB, Abbott LF, Marder E. Dynamic clamp: computer-generated conductances in real neurons. // J.Neurophysiol. 1993, 69(3):992-5] I  g GABA (t ) (V  VGABA )   gGABA (t )  gGABA e  t /  1  e  t /  2 ,  1  5 s,  2  15 s, gGABA  8 nS max max
  • 15. Control Dynamic clamp for spontaneous potassium channels I  g (t )(V  VK ) d 2g dg  1 2 2  ( 1   2 )  g  artificial K-channels dt dt  g max  2 1   (t  ti ) i  1  5ms,  2  200ms VK  70mV g max  1nS
  • 16. Dynamic clamp Model [Graham, 1999] for to study firing properties of CA1 pyramidal neuron neuron Hz 0.6 80 Experiment: pyramidal cell 60 of visual cortex 0.5 40 Hz 0.06 Hz 20 0.4 s, mS/cm2 (2.7; 0.06) 110 0.05 100 0 90 0.04 80 0.3 s, mS/cm2 70 60 0.03 50 40 0.2 (1.7; 0.024) 30 0.02 20 0.01 10 0 0.1 0 1 2 3 0 2 4 6 8 10 2 2 u, A/cm u,mkA/cm
  • 17. Experiment Bottom point Top point u=1.7 mkA/cm2 u=2.7 mkA/cm2 20 S=0.024 mS/cm2 20 S=0.06 mS/cm2 0 0 -20 -2 0 V, mV V, mV -40 -4 0 -6 0 -60 -8 0 -80 0 500 1000 0 500 1000 t, m s t, m s Model u=4 mkA/cm2 u=7.7 mkA/cm2 S=0.15 mS/cm2 S=0.4 mS/cm2
  • 18. Divisive effect of shunting inhibition is due to spike threshold sensitivity to slow inactivation of sodium channels dV T V0T  V T   V T   (t  t i spike ) dt  i
  • 19.  2 Rate Gex Ginh Total Response (all spikes during 500ms-step) Only 1st spikes Only 1st interspike intervals
  • 20. Dynamic clamp for voltage-gated current: compensation of INaP Hippocampal Pyramidal Neuron In Vitro [Vervaeke K, Hu H., Graham L.J., Storm J.F. Contrasting effects of the persistent Na+ current on neuronal excitability and spike timing, Neuron, v49, 2006]
  • 21. Medium electric Dynamic clamp conductance for electric couplings between real and modeled neurons I  g (V exp  V mod ) g  const High electric conductance
  • 22. Dynamic clamp for synaptic conductance estimations in-vivo Preferred direction Null direction V Эксперимент [Lyle Graham et al.]: Внутриклеточные измерения patch-clamp в зрительной коре кошки in vivo. Стимул – движущаяся полоска. V  20 mV GABAA : GI 10 nS AMPA : GE 5 nS 1s
  • 23. Threshold voltage, VT Peak voltage, V P «Firing-Clamp» - method of synaptic conductance estimation Idea: a patched neuron is forced to spike with a constant rate; gE, gI, are estimated from values of subthreshold voltage 1 ms and spike amplitude. τ(V)
  • 24. Conclusions Dynamic Clamp • is needed for measuring firing characteristics of neuron • is needed for estimation input synaptic conductances in-vivo • helps to create artificial ionic intrinsic or synaptic channels

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