Identification of dynamical cascade models using input-output data from sensory processing circuits
What is it about?
The cascade model, comprising a filter in series with a spiking neuron, have been widely used as representation for spiking neural circuits. In this chapter, two new identification methodologies are proposed for neural circuits comprising a linear or nonlinear filter in cascade with a spiking neuron. A [Nonlinear Filter]-[Ideal IF] circuit is reformulated as a scaled nonlinear filter in series with a modified ideal IF neuron. The identification is subsequently carried out by employing the NARMAX nonlinear system identification methodology to infer the structure and parameters of a discrete-time representation for the scaled nonlinear filter. An equivalent [Linear Filter]-[Leaky IF] circuit is identified, assuming that input-output measurements of the spiking neuron are not available and that all parameters are unknown. The leaky IF model is identified by solving an equation whose solution is proven to be unique. An algorithm is provided that computes the solution with arbitrary precision. Subsequently, the structure and parameters of the filter are inferred using the NARMAX identification methodology. Numerical simulations are given to test the performance of the new methods.
Why is it important?
Although the state-of-the-art identification methods for cascade models can accommodate a wide range of filters and spiking neurons, the assumptions proposed can in some cases be considered restrictive. Specifically, for [Filter]-[IF] circuits, it is assumed that the IF model is known, or that the filter output (IF input) is available for measurement. Here we propose two identification methodologies that are not subject to these restrictive assumptions.
The following have contributed to this page: Dorian Florescu
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