Oscillating neurons in the cochlear nucleus (Bahmer Langner 2006a, b, and 2007)

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Accession:87454
"Based on the physiological and anatomical data, we propose a model consisting of a minimum network of two choppers that are interconnected with a synaptic delay of 0.4 ms (Bahmer and Langner 2006a) . Such minimum delays have been found in different systems and in various animals (e.g. Hackett, Jackson, and Rubel 1982; Borst, Helmchen, and Sakmann 1995). The choppers receive input from both the auditory nerve and an onset neuron. This model can reproduce the mean, standard deviation, and coefficient of variation of the ISI and the dynamic features of AM coding of choppers."
Reference:
1 . Bahmer A, Langner G (2006) Oscillating neurons in the cochlear nucleus: II. Simulation results. Biol Cybern 95:381-92 [PubMed]
2 . Bahmer A, Langner G (2006) Oscillating neurons in the cochlear nucleus: I. Experimental basis of a simulation paradigm. Biol Cybern 95:371-9 [PubMed]
3 . Bahmer A, Langner G (2007) Simulation of oscillating neurons in the cochlear nucleus: a possible role for neural nets, onset cells, and synaptic delays Hearing - from basic research to applications (Proc. of International Symp. of Hearing), Kollmeier B, Klump G, Hohmann V, Langemann U, Mauermann M, Uppenkamp S, Verhey J, ed.
4 . Bahmer A, Langner G (2009) A simulation of chopper neurons in the cochlear nucleus with wideband input from onset neurons. Biol Cybern 100:21-33 [PubMed]
5 . Bahmer A, Langner G (2010) Parameters for a model of an oscillating neuronal network in the cochlear nucleus defined by genetic algorithms. Biol Cybern 102:81-93 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Cochlear ganglion cell Type II; CN stellate cell; Ventral cochlear nucleus T stellate (chopper) neuron; Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Audition;
Implementer(s): Bahmer, Andreas [Andreas.Bahmer at kgu.de]; 
% Topology II from Bahmer and Langner 2006 Biol Cybern
% 14.3.2007
% Andreas Bahmer

clear global
clear functions


f_s=40000;   % Sampling rate (25µs)
dt=1./f_s;     %// in s


AP5_realNerv=(anft==5);
AP4_realNerv=bitor(anft==4,AP5_realNerv);
AP3_realNerv=bitor(anft==3,AP4_realNerv);
AP2_realNerv=bitor(anft==2,AP3_realNerv);
AP1_realNerv=bitor(anft==1,AP2_realNerv);
onsetneuron=+onsett;

Timeaxis=0:dt:length(AP1_realNerv)*dt;
sim_steps=length(AP1_realNerv);

%-----Variables----------------------
%Somata
Loss=3000;
sigma=0;  

%Synapse
Loss_Syn=4000;
Weight=0.75;
Loss_Syn_Nerve=1300;
Weight_Nerve=0.1;
Latency_Syn_A=0.00018;
sigmasyn=0.00026;
Latency_Syn_fast=0.00018;

global Ausschuettung2
Ausschuettung2=Ausschuettung(dt,sim_steps);

threshold=schwellwert(0.0006,0.0001,dt); % fast chopper (absolute refractory period, relative refractory period, dt)
threshold2=schwellwert(0.0017,0.0002,dt); % slow chopper (absolute refractory period, relative refractory period, dt)
AP_2A=0;

for s=1:sim_steps%-----START SIMULATION---------------------------------------------------------------

[PSC_realNervAP1]=Synapse1(dt,sim_steps,Loss_Syn_Nerve,Weight_Nerve/5,+AP1_realNerv(s),Latency_Syn_A);
[PSC_realNervAP2]=Synapse2(dt,sim_steps,Loss_Syn_Nerve,Weight_Nerve/5,+AP2_realNerv(s),Latency_Syn_A);
[PSC_realNervAP3]=Synapse3(dt,sim_steps,Loss_Syn_Nerve,Weight_Nerve/5,+AP3_realNerv(s),Latency_Syn_A);
[PSC_realNervAP4]=Synapse4(dt,sim_steps,Loss_Syn_Nerve,Weight_Nerve/5,+AP4_realNerv(s),Latency_Syn_A);
[PSC_realNervAP5]=Synapse5(dt,sim_steps,Loss_Syn_Nerve,Weight_Nerve/5,+AP5_realNerv(s),Latency_Syn_A);
PSC_realNerv=PSC_realNervAP1+PSC_realNervAP2+PSC_realNervAP3+PSC_realNervAP4+PSC_realNervAP5;

%------Two fast Chopper Neurons 1 and 2------------------------
%-------------- Synapse for Onset Neuron ----------------------------
[PSC_Onset]=Synapse6(dt,sim_steps,Loss_Syn,0.8,+onsetneuron(s),Latency_Syn_A);  

%-------------- Synapse1A2-------------------von Soma2A--------------------
[PSC_1A]=Synapselatenztrauschen1(dt,sim_steps,4000,Weight,AP_2A,Latency_Syn_fast,sigmasyn);

%--------------Soma1A------------------------------------------------------
PSC_1Agesamt=PSC_Onset+PSC_realNerv+PSC_1A;
[AP_1A,Spannung_1A]=ScopeSomarausch1(dt,Loss,PSC_1Agesamt,threshold,0,sigma);


%--------------Synapse2A---------------------------------------------------
[PSC_2A]=Synapselatenztrauschen2(dt,sim_steps,4000,Weight,AP_1A,Latency_Syn_fast,sigmasyn);

%---------------Soma2A-----------------------------------------------------
PSC_2Agesamt=PSC_2A+PSC_realNerv;
[AP_2A]=Somarausch2(dt,Loss,PSC_2Agesamt,threshold,0,sigma);


%------Slow Chopper Neuron 3--------------
%--------------Synapse3A---------------------------------------------------
[PSC_3A]=Synapselatenztrauschen3(dt,sim_steps,Loss_Syn,Weight,AP_2A,0,0);
[PSC_4A]=Synapselatenztrauschen4(dt,sim_steps,Loss_Syn,Weight,AP_1A,0,0);

%--------------Soma3A------------------------------------------------------
PSC_3Agesamt=PSC_3A+PSC_4A+PSC_realNerv;
[AP_3A]=Somarausch3(dt,Loss,PSC_3Agesamt,threshold2,0,sigma);


%--------------SCOPES-------------------------------------------------
scope3(s+1)=AP_1A;
scope5(s+1)=AP_3A;

end;%----------ENDE SIMULATION---------------------------------------