Linear vs non-linear integration in CA1 oblique dendrites (Gómez González et al. 2011)

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Accession:144450
The hippocampus in well known for its role in learning and memory processes. The CA1 region is the output of the hippocampal formation and pyramidal neurons in this region are the elementary units responsible for the processing and transfer of information to the cortex. Using this detailed single neuron model, it is investigated the conditions under which individual CA1 pyramidal neurons process incoming information in a complex (non-linear) as opposed to a passive (linear) manner. This detailed compartmental model of a CA1 pyramidal neuron is based on one described previously (Poirazi, 2003). The model was adapted to five different reconstructed morphologies for this study, and slightly modified to fit the experimental data of (Losonczy, 2006), and to incorporate evidence in pyramidal neurons for the non-saturation of NMDA receptor-mediated conductances by single glutamate pulses. We first replicate the main findings of (Losonczy, 2006), including the very brief window for nonlinear integration using single-pulse stimuli. We then show that double-pulse stimuli increase a CA1 pyramidal neuron’s tolerance for input asynchrony by at last an order of magnitude. Therefore, it is shown using this model, that the time window for nonlinear integration is extended by more than an order of magnitude when inputs are short bursts as opposed to single spikes.
Reference:
1 . Gómez González JF, Mel BW, Poirazi P (2011) Distinguishing Linear vs. Non-Linear Integration in CA1 Radial Oblique Dendrites: It's about Time. Front Comput Neurosci 5:44 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Na,p; I CAN; I Sodium; I Calcium; I Potassium; I_AHP;
Gap Junctions:
Receptor(s): NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Active Dendrites; Detailed Neuronal Models; Synaptic Integration;
Implementer(s):
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; NMDA; I Na,p; I CAN; I Sodium; I Calcium; I Potassium; I_AHP;
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CA1_Gomez_2011
lib
basic-graphics.hoc *
choose-secs.hoc *
current-balance.hoc
cut-sections.hoc *
deduce-ratio.hoc *
find-gmax.hoc
histographBP_TP02a.hoc
histographBP_TP02b.hoc
histographBP_TP02b_button.hoc
jose.hoc
map-segments-to-3d.hoc *
morphology-lib.hoc
Oblique-lib.hoc *
Oblique-lib2.hoc
salloc.hoc *
spikecount.hoc *
TP-lib.hoc *
tune-epsps.hoc
tune-epspsN128.hoc
tune-epspsSOMA.hoc
vector-distance.hoc
vector-distanceORIGINAL.hoc *
verbose-system.hoc *
                            
// set the pointers for AMPA, NMDA, GABA_A or GABA_B synapses
// to a defined location

econ.xopen_library("Terrence","find-gmax")

proc salloc() { local nmdaR
// $o1 and $o2 are the first 2 arguments to this function
// They should be an AMPA and NMDA synapse or a
// GABA_A and GABA_B synapse

// .loc() places them at a normalized and segment-resolved position [0..1] 
// along the section	 

  $o1.loc($3)
  $o2.loc($3)

// here we call a library function to figure out the maximum AMPA
// conductance which should be used at this synapse based on synapse
// tunings calculated from a previously run tuning experiments

// this number will be anywhere from 0.5nS to 5nS depending on what
// was required to generate a 5mV local depolarization
  $o1.gmax = find_gmax($3) 

// next we decide what the NMDA-AMPA gmax ratio should be --- ie,
// a Mainen-Sejnowski article suggested that maximum NMDA should be
// (0.6)*gbar_AMPA. 

  nmdaR = deduce_ratio()

// Assuming (and expecting and hoping!) that the first object passed in
// was an AMPA synapse and the second object passed in was an NMDA
// synapse, then the NMDA synapses' gbar_NMDA is now changed by a factor
// of nmdaR

  $o2.gmax=$o1.gmax*nmdaR
//  printf("ampa: %g nmda: %g * %g\n", $o1.gmax, $o1.gmax, nmdaR)
}

// This is a variation of the above designed to work with
// GABA syns as well as AMPA syns.

// It is a work in progress.
proc SALLOC() { local ratio, ampa_flag
        ampa_flag=0
        ampa_flag=$4
  $o1.loc($3)
  $o2.loc($3)
        if (ampa_flag) {
                $o1.gmax=find_gmax($3) 
                nmdaR=deduce_ratio()
                $o2.gmax=$o1.gmax*nmdaR
//                printf("ampa: %g nmda: %g * %g\n", $o1.gmax, $o1.gmax, nmdaR)
        } else {
                $o1.gmax=GABA_AMPA_RATIO*find_gmax($3) 
                nmdaR=deduce_ratio()
                $o2.gmax=GABAB_GABAA_RATIO*$o1.gmax
//                printf("gabaa: %g gabab: %g * %g\n", $o1.gmax, $o1.gmax, GABAB_GABAA_RATIO)
        }
}


// same for GABAa synapses

proc SALLOC_GABAa() { local flag
        flag=0
        flag=$3
        $o1.loc($2)

        if (flag){  
            $o1.gmax=GABA_AMPA_RATIO*find_gmax($2) // as a function of ampa conductance 
        } else {
            $o1.gmax = $4
        }
//        printf("gabaa: %g\n", $o1.gmax)
        
}

// same for GABAb synapses

proc SALLOC_GABAb() { local flag
        flag=0
        flag=$3
        $o1.loc($2)

        if (flag){  
            $o1.gmax=GABAb_GABAa_RATIO*(GABA_AMPA_RATIO*find_gmax($2)) // as a function of ampa conductance 
        } else {
            $o1.gmax = $4
        }
//        printf("gabab: %g\n", $o1.gmax)
        
}

// display synaptic locations on a graph

proc salloc2() {
/*
  $o1.loc($3)
  $o2.loc($3)
  $o1.gmax=find_gmax($3) 
  $o2.gmax=$o1.gmax*deduce_ratio()
  print $o1, $o1.gmax
  print $o2, $o2.gmax
*/
  salloc($o1,$o2,$3)    
  if ($4) {
    $o5.point_mark($o1,$6)
  }
}


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