A model for impulse frequency modulation used in neural encoding.

The impulse rate at the output of a neural encoder can be interpreted as the sum of the mean impulse rate plus a noise component. From literature models are known which describe the transient phenomena of the encoder as far as the mean impulse rate is concerned. In this paper in addition the noise phenomenon is treated by a model which is in agreement with results derived from measurements. This model consists of two parts, a multiplicative and an additive estimator. The first one is similar to the automatic gain control system known from literature. This system estimates the amplification of the impulse rate due to the step input of the neural encoder. Multiplying the impulse rate with the inverse of this factor inhibits the change of the impulse rate. The second estimator calculates the step size of the impulse rate which is subtracted from the output of the encoder. Again the change of the impulse rate is inhibited. The comparison of the impulse rates simulated by the model and given by published measurements shows a good agreement for the properties of the mean impulse rate and the variance of the imposed noise.