Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008)

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Neocortical neurons are classified by current–frequency relationship. This is a static description and it may be inadequate to interpret neuronal responses to time-varying stimuli. Theoretical studies (Brunel et al., 2001; Fourcaud-Trocmé et al. 2003; Fourcaud-Trocmé and Brunel 2005; Naundorf et al. 2005) suggested that single-cell dynamical response properties are necessary to interpret ensemble responses to fast input transients. Further, it was shown that input-noise linearizes and boosts the response bandwidth, and that the interplay between the barrage of noisy synaptic currents and the spike-initiation mechanisms determine the dynamical properties of the firing rate. In order to allow a reader to explore such simulations, we prepared a simple NEURON implementation of the experiments performed in Köndgen et al., 2008 (see also Fourcaud-Trocmé al. 2003; Fourcaud-Trocmé and Brunel 2005). In addition, we provide sample MATLAB routines for exploring the sandwich model proposed in Köndgen et al., 2008, employing a simple frequdency-domain filtering. The simulations and the MATLAB routines are based on the linear response properties of layer 5 pyramidal cells estimated by injecting a superposition of a small-amplitude sinusoidal wave and a background noise, as in Köndgen et al., 2008.
1 . Koendgen H, Geisler C, Wang XJ, Fusi S, Luescher HR, Giugliano M (2004) The dynamical response of single cells to noisy time-varying currents Soc Neurosci Abstr :640
2 . Köndgen H, Geisler C, Fusi S, Wang XJ, Lüscher HR, Giugliano M (2008) The dynamical response properties of neocortical neurons to temporally modulated noisy inputs in vitro. Cereb Cortex 18:2086-97 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Axon;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Neocortex L5/6 pyramidal GLU cell; Abstract Wang-Buzsaki neuron;
Channel(s): I Na,t; I K;
Gap Junctions:
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Parameter Fitting; Methods; Rate-coding model neurons;
Implementer(s): Giugliano, Michele [mgiugliano at]; Delattre, Vincent;
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; I Na,t; I K;
% This is the main fit routine..

global time input A EE H E;
global pars E_best p_best Nz Np;

% load_data;            <-- somewhere data should be made available

% Init values..
ZEROS = [0.1 20];
POLES = [10 250 250 300];
GAIN  = 3.4245;
DELAY = 0.001;
Nz    = length(ZEROS);
Np    = length(POLES);

%options = optimset('Display', 'on', 'TolX', 1e-9, 'TolFun', 1e-3, 'MaxFunEvals',10000, 'MaxIter', 10000);                       
%pars    = fminsearch(@cost, pars, options);

minZ = zeros(size(ZEROS));
minP = zeros(size(POLES));
maxZ = 10000*ones(size(ZEROS));
maxP = 20000*ones(size(POLES));
dpZ  = 20*ones(size(ZEROS));
dpP  = 20*ones(size(POLES));

pars  = [0.9366  172.2660    6.1414   -2.7866 GAIN DELAY ZEROS  POLES];
dpmax = [0.1    1     0.01      0.1 0.4     0.001   dpZ dpP];
p_min = [-5     0.    0.0001   -200. 0.0001 0    minZ minP];
p_max = [5      3000  10.       200. 1e3    0.002  maxZ maxP];

[E_best, p_best] = anneal(@cost, pars, dpmax, p_min, p_max, 100);


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