Design and fitting of neural network transfer functions.
An algorithm is presented which (a) allows construction of mathematical models involving arbitrary combinations of linear cascades, parallel pathways, and feedback loops, (b) computes a total transfer function of the system, (c) performs a least-squares optimization of model parameters to best fit the model to experimental data, and (d) provides a measure of goodness-of-fit to the data. The technique has been employed to construct and test models of neural networks which mimic a class of responses observed in the cat vestibular nuclei in response to tilt, namely responses which show both a gain increase and progressive phase lag as the stimulation frequency goes from 0.01 to 2 Hz. A network consisting of a simple gain element in parallel with an inhibitory high-pass filtered version of the input provided a satisfactory fit to these data.
D000818:Animals; D002415:Cats; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009424:Nervous System Physiological Phenomena; D009474:Neurons / Q000502:physiology; D011187:Posture; D014726:Vestibular Nuclei / Q000502:physiology