Ambiguous Encoding and Distorted Perception (Carlson and Kawasaki 2006)

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"... In the weakly electric fish Eigenmannia, P- and T-type primary afferent fibers are specialized for encoding the amplitude and phase, respectively, of electrosensory stimuli. We used a stimulus estimation technique to quantify the ability of P- and T-units to encode random modulations in amplitude and phase. As expected, P-units exhibited a clear preference for encoding amplitude modulations, whereas T-units exhibited a clear preference for encoding phase modulations. Surprisingly, both types of afferents also encoded their nonpreferred stimulus attribute when it was presented in isolation or when the preferred stimulus attribute was sufficiently weak. Because afferent activity can be affected by modulations in either amplitude or phase, it is not possible to unambiguously distinguish between these two stimulus attributes by observing the activity of a single afferent fiber. Simple model neurons with a preference for encoding either amplitude or phase also encoded their nonpreferred stimulus attribute when it was presented in isolation, suggesting that such ambiguity is unavoidable. ... " See paper for more and details.
1 . Carlson BA, Kawasaki M (2006) Ambiguous encoding of stimuli by primary sensory afferents causes a lack of independence in the perception of multiple stimulus attributes. J Neurosci 26:9173-83 [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): Electric fish P- and T-type primary afferent fibers;
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Oscillations; Sensory coding;
function [spk] = liandfrn(sig,dt,thold,sigma,C,R,ref)

%   liandrn.m
%       Simulates a leaky integrate-and-fire neuron with noise
%   USAGE:
%       [spk] = liandfrn(sig,dt,thold,sigma,C,R,ref)
%   WHERE:
%       sig = input current signal (nA)
%       dt = samping step
%       thold = threshold (mV)
%       sigma = standard deviation of noise term (mV)
%       C = capacitance (nF)
%       R = resistance (MOhm)
%       ref = refractory period (ms)
%       spk = binned output spike train

% Ensure stability
if ((R*C)<dt),
    disp(' ');
    disp(' ');
    disp('Error: the time constant RC computed from the resistance');
    disp('and the capacitance of the model is smaller than the time');
    disp('step. Execution aborted.');

% Run model
Vm = 0;                     % Initial resting potential
spk = zeros(1,length(sig)); % Initialize spike variable
i = 1;
while i<=length(spk),
    Vm = Vm*(1-dt/(R*C)) + (dt/C) * sig(i) + sigma*randn;   % Integrates the voltage
    if Vm>=thold,                                           % Above threshold
        spk(i) = 1;                                         % Generates a spike
        Vm = 0;                                             % Reset to resting potential
        i = i+(ref/dt);                                     % No spikes during refractory period
        i = i+1;

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