Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010)

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Accession:136095
"... We sought to measure how the activity of the network alters information flow from inputs to output patterns. Information handling by the network reflected the degree of internal connectivity. ... With greater connectivity strength, the recurrent network translated activity and information due to contribution of activity from intrinsic network dynamics. ... At still higher internal synaptic strength, the network corrupted the external information, producing a state where little external information came through. The association of increased information retrieved from the network with increased gamma power supports the notion of gamma oscillations playing a role in information processing."
Reference:
1 . Neymotin SA, Jacobs KM, Fenton AA, Lytton WW (2011) Synaptic information transfer in computer models of neocortical columns. J Comput Neurosci. 30(1):69-84 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; Neocortex fast spiking (FS) interneuron; Neocortex spiny stellate cell; Neocortex spiking regular (RS) neuron; Neocortex spiking low threshold (LTS) neuron;
Channel(s): I Na,t; I A; I K;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Activity Patterns; Information transfer;
Implementer(s): Lytton, William [billl at neurosim.downstate.edu]; Neymotin, Sam [samn at neurosim.downstate.edu];
Search NeuronDB for information about:  Neocortex V1 pyramidal corticothalamic L6 cell; Neocortex V1 pyramidal intratelencephalic L2-5 cell; Neocortex V1 interneuron basket PV cell; GabaA; AMPA; NMDA; I Na,t; I A; I K;
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ncdemo
readme.txt
A.mod
AMPA.mod *
AMPAr.mod
clampex.mod *
cp.mod *
cp2.mod *
field.mod
GABAa.mod
GABAar.mod
GABAb.mod
GABAbr.mod
H.mod
Iahp.mod *
Ican.mod *
IL.mod
IL3.mod *
infot.mod *
intf_.mod
intfsw.mod *
kdr2.mod *
kmbg.mod
misc.mod *
naf2.mod *
nap.mod *
NMDA.mod *
NMDAr.mod
nthh.mod *
ntIh.mod *
ntt.mod *
OFThpo.mod
OFThresh.mod
pregencv.mod
stats.mod
updown.mod *
vecst.mod
bg_cvode.inc
misc.h *
mosinit.hoc
netcon.inc *
netrand.inc
ofc.inc
                            
: $Id: bg_cvode.inc,v 1.21 2010/08/30 14:50:46 billl Exp $
TITLE Kevin's Cvode modification to Borg Graham Channel Model

COMMENT

Modeling the somatic electrical response of hippocampal pyramidal neurons, 
MS thesis, MIT, May 1987.

Each channel has activation and inactivation particles as in the original
Hodgkin Huxley formulation.  The activation particle mm and inactivation
particle hh go from on to off states according to kinetic variables alpha
and beta which are voltage dependent.  The form of the alpha and beta
functions were dissimilar in the HH study.  The BG formulae are:
	alpha = base_rate * Exp[(v - v_half)*valence*gamma*F/RT]
	beta = base_rate * Exp[(-v + v_half)*valence*(1-gamma)*F/RT]
where,
	baserate : no affect on Inf.  Lowering this increases the maximum
		    value of Tau
	basetau : (in msec) minimum Tau value.
	chanexp : number for exponentiating the state variable; e.g.
		  original HH Na channel use m^3, note that chanexp = 0
		  will turn off this state variable
	erev : reversal potential for the channel
	gamma : (between 0 and 1) does not affect the Inf but makes the
		Tau more asymetric with increasing deviation from 0.5
	celsius : temperature at which experiment was done (Tau will
		      will be adjusted using a q10 of 3.0)
	valence : determines the steepness of the Inf sigmoid.  Higher
		  valence gives steeper sigmoid.
	vhalf : (a voltage) determines the voltage at which the value
		 of the sigmoid function for Inf is 1/2
        vrest : (a voltage) voltage shift for vhalf

ENDCOMMENT

INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}

NEURON {
  RANGE gmax, g, i
  GLOBAL erev, Inf, Tau, vrest, ki, mininf
} : end NEURON

CONSTANT {
  FARADAY = 96489.0	: Faraday's constant
  R= 8.31441		: Gas constant
} : end CONSTANT

UNITS {
  (mA) = (milliamp)
  (mV) = (millivolt)
  (umho) = (micromho)
} : end UNITS


COMMENT
** Parameter values should come from files specific to particular channels
PARAMETER {
	erev 		= 0    (mV)
	gmax 		= 0    (mho/cm^2)
        vrest           = 0    (mV)

	mvalence 	= 0
	mgamma 		= 0
	mbaserate 	= 0
	mvhalf 		= 0
	mbasetau 	= 0
	mtemp 		= 0
	mq10		= 3
	mexp 		= 0

	hvalence 	= 0
	hgamma		= 0
	hbaserate 	= 0
	hvhalf 		= 0
	hbasetau 	= 0
	htemp 		= 0
	hq10		= 3
	hexp 		= 0

} : end PARAMETER
ENDCOMMENT

PARAMETER {
  ki = 1e-3  (mM)
  mininf = 1e-4
  cao                (mM)
  cai                (mM)
}

ASSIGNED {
  i (mA/cm^2)		
  g (mho/cm^2)
  Inf[2]		: 0 = m and 1 = h
  Tau[2]		: 0 = m and 1 = h
  mexp_val
  hexp_val
} : end ASSIGNED 

STATE { m h }

INITIAL { 
  mh(v)
  m = Inf[0] h = Inf[1]
}

BREAKPOINT {
  if (gmax==0.0) { g=0. } else {

  SOLVE states METHOD cnexp

  hexp_val = 1
  mexp_val = 1

  : Determining h's exponent value
  if (hexp > 0) {
    FROM index=1 TO hexp {
      hexp_val = h * hexp_val
    }
  }

  : Determining m's exponent value
  if (mexp > 0) {
    FROM index = 1 TO mexp {
      mexp_val = m * mexp_val
    }
  }

  :		       	         mexp			      hexp
  : Note that mexp_val is now = m      and hexp_val is now = h 
  g = gmax * mexp_val * hexp_val
  }
  iassign()
} : end BREAKPOINT

: ASSIGNMENT PROCEDURES
: Must be overwritten by user routines in parameters.multi
: PROCEDURE iassign () { i = g*(v-erev) ina=i }
: PROCEDURE iassign () { i = g*ghkca(v) ica=i }

:-------------------------------------------------------------------

DERIVATIVE states {
  mh(v)
  m' = (-m + Inf[0]) / Tau[0] 
  h' = (-h + Inf[1]) / Tau[1]
}
:-------------------------------------------------------------------
: NOTE : 0 = m and 1 = h
PROCEDURE mh (v) {
  LOCAL a, b, j, mqq10, hqq10

  mqq10 = mq10^((celsius-mtemp)/10.)	
  hqq10 = hq10^((celsius-htemp)/10.)	

  : Calculater Inf and Tau values for h and m
  FROM j = 0 TO 1 {
    a = alpha (v, j)
    b = beta (v, j)

    Inf[j] = a / (a + b)
    if (Inf[j]<mininf) { Inf[j]=0 }

    VERBATIM
    switch (_lj) {
      case 0:
      /* Make sure Tau is not less than the base Tau */
if ((Tau[_lj] = 1 / (_la + _lb)) < mbasetau) {
  Tau[_lj] = mbasetau;
}
Tau[_lj] = Tau[_lj] / _lmqq10;
break;
case 1:
if ((Tau[_lj] = 1 / (_la + _lb)) < hbasetau) {
  Tau[_lj] = hbasetau;
}
Tau[_lj] = Tau[_lj] / _lhqq10;
if (hexp==0) {
  Tau[_lj] = 1.; }
  break;
    }

    ENDVERBATIM
  }
} : end PROCEDURE mh (v)
:-------------------------------------------------------------------
FUNCTION alpha(v,j) {
  if (j == 1) {
    if (hexp==0) {
      alpha = 1
    } else {
      alpha = hbaserate * exp((v - (hvhalf+vrest)) * hvalence * hgamma * FRT(htemp)) }
  } else {
    alpha = mbaserate * exp((v - (mvhalf+vrest)) * mvalence * mgamma * FRT(mtemp))
  }
} : end FUNCTION alpha (v,j)

:-------------------------------------------------------------------
FUNCTION beta (v,j) {
  if (j == 1) {
    if (hexp==0) {
      beta = 1
    } else {
      beta = hbaserate * exp((-v + (hvhalf+vrest)) * hvalence * (1 - hgamma) * FRT(htemp)) }
  } else {
    beta = mbaserate * exp((-v + (mvhalf+vrest)) * mvalence * (1 - mgamma) * FRT(mtemp))
  }
} : end FUNCTION beta (v,j)

:-------------------------------------------------------------------
FUNCTION FRT(temperature) {
	FRT = FARADAY * 0.001 / R / (temperature + 273.15)
} : end FUNCTION FRT (temperature)

:-------------------------------------------------------------------
FUNCTION ghkca (v) { : Goldman-Hodgkin-Katz eqn
  LOCAL nu, efun

  nu = v*2*FRT(celsius)
  if(fabs(nu) < 1.e-6) {
    efun = 1.- nu/2.
  } else {
    efun = nu/(exp(nu)-1.) }

    ghkca = -FARADAY*2.e-3*efun*(cao - cai*exp(nu))
} : end FUNCTION ghkca()

: Ca-mediated inactivation of Ca channels, see Hille, Ed2 p179-180
: use eg iassign () { i = g*faco()*ghkca(v) ica=i }
FUNCTION faco() {
  faco = ki/(ki+cai)
}

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