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: nthh.mod,v 1.6 1998/08/14 20:52:37 billl Exp $
TITLE Hippocampal HH channels
:
: Fast Na+ and K+ currents responsible for action potentials
: Iterative equations.  final check on save
:
: Equations modified by Traub, for Hippocampal Pyramidal cells, in:
: Traub & Miles, Neuronal Networks of the Hippocampus, Cambridge, 1991
:
: range variable vtraub adjust threshold
:
: Written by Alain Destexhe, Salk Institute, Aug 1992
:

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

NEURON {
	SUFFIX hh2
	USEION na READ ena WRITE ina
	USEION k READ ek WRITE ik
	RANGE gnabar, gkbar, vtraub, inaf, ikf
	GLOBAL m_inf, h_inf, n_inf
	GLOBAL tau_m, tau_h, tau_n
	GLOBAL m_exp, h_exp, n_exp, exptemp
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
}

PARAMETER {
	gnabar	= .135 	(mho/cm2)
	gkbar	= .270 	(mho/cm2)

	ena	= 50	(mV)
	ek	= -95	(mV)
	celsius = 36    (degC)
        exptemp = 36
	dt              (ms)
	v               (mV)
	vtraub	= -63	(mV)
}

STATE {
	m h n
}

INITIAL {
	tadj = 3.0 ^ ((celsius-exptemp)/ 10 )
	evaluate_fct(v)
	m = m_inf
        h = h_inf
	n = n_inf
}

ASSIGNED {
	ina	(mA/cm2)
	ik	(mA/cm2)
	inaf	(mA/cm2)
	ikf	(mA/cm2)
        il	(mA/cm2)
	m_inf
	h_inf
	n_inf
	tau_m
	tau_h
	tau_n
	m_exp
	h_exp
	n_exp
	tadj
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	inaf = gnabar * m*m*m*h * (v - ena)
	ikf  = gkbar * n*n*n*n * (v - ek)
        ina = inaf
        ik  = ikf
}

DERIVATIVE states {   : exact Hodgkin-Huxley equations
	evaluate_fct(v)
	m' = (m_inf - m) / tau_m
	h' = (h_inf - h) / tau_h
	n' = (n_inf - n) / tau_n
}

:   PROCEDURE states() {	: exact when v held constant
:           evaluate_fct(v)
:           m = m + m_exp * (m_inf - m)
:           h = h + h_exp * (h_inf - h)
:           n = n + n_exp * (n_inf - n)
:           VERBATIM
:           return 0;
:           ENDVERBATIM
:   }

UNITSOFF

PROCEDURE evaluate_fct(v(mV)) { LOCAL a,b,v2


	v2 = v - vtraub : convert to traub convention

	a = 0.32 * (13-v2) / ( exp((13-v2)/4) - 1)
	b = 0.28 * (v2-40) / ( exp((v2-40)/5) - 1)
	tau_m = 1 / (a + b) / tadj
	m_inf = a / (a + b)

	a = 0.128 * exp((17-v2)/18)
	b = 4 / ( 1 + exp((40-v2)/5) )
	tau_h = 1 / (a + b) / tadj
	h_inf = a / (a + b)

	a = 0.032 * (15-v2) / ( exp((15-v2)/5) - 1)
	b = 0.5 * exp((10-v2)/40)
	tau_n = 1 / (a + b) / tadj
	n_inf = a / (a + b)

	m_exp = 1 - exp(-dt/tau_m)
	h_exp = 1 - exp(-dt/tau_h)
	n_exp = 1 - exp(-dt/tau_n)
}

UNITSON









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