Computational principles of neural adaptation for binaural signal integration
Fig 3
Effectiveness of GABA receptor on LSO response.
(A) Adaptation range of model LSO neuron for inverse GABAb receptor effectiveness. Parameters λE and λI describe the inhibition effectiveness on excitatory and inhibitory inputs respectively. Normal (dark blue line) and shifted (light blue line) responses are shown. For λE = λI = 0 the ILD response curve is the same as the default response (Fig 2B) and no dynamic adaptation process takes place. For λE = 0.70 and λI = 1.0 (inverse ratio) the response curve’s slope and consequently its sensitivity is decreased while the dynamic coding range is increased. (B) Adaptation range of model LSO neuron for increasing parameter values. Normal (dark blue line) and shifted (light blue line) responses are shown. Again, for λE = λI = 0 the ILD response function is similar to the default response. Light blue area indicates the adaptation range. (C) Exhaustive parameter evaluation of the GABA parameters λE, λI. Each point in the heat map indicates the coding precision value for a certain combination of λE (abscissa) and λI (ordinate). We assume that the neuron’s task is to achieve a preferably high coding precision value. Black dots depict these values (the maxima of the map). A linear function (dashed line) was fitted to those points and the slope (m = 0.80) and bias (b = − 0.06) calculated. (D) Influence of model parameters κr and γr on the gain (left panel) and bias (right panel) of the linear regression.