Stretch Reflex Arc (Preliminary Results)
Results: Middle Figure shows the transient response of the system under a step load for various amounts of descending control. Bottom Figure shows steady state force-displacement curves as a function of amount of descending control. The numbers along the curve show the amount of loading for that datapoint.
Characterization of the Neuron Microelectrode Interface
-- Low signal to noise ratio - signal attenuation due to counter-ion diffusion from the bulk extracellular medium
-- A modification of the shape of the cell-generated potentials due to the presence of a highly dispersive dielectric medium in the cell-electrode cleft.
Solution: Treat this as a nonlinear system identification problem. The following experiments and analyses were performed:
-- Stimulate system with microelectrode (See Figure: Top) under voltage clamp with white noise in amplitude and frequency range of interest.
-- Record signal on microelectrode, appropriate signal processing to eliminate drift etc.
-- Compute the Volterra or Wiener kernels (See Figures for computed first and second Wiener kernels) ; to reduce computation, discretize each kernel using Orthogonal Basis Functions (OBFs). Optimize number of OBFs for each order of the nonlinearity.
-- Stimulate system under current clamp to generate an action potential, record AP and microlectrode signal, run the AP through the kernels and compare with experimental output to validate model. (See Figure: Bottom)
Analysis of Results:
-- Output signal is attenuated by 3 orders of magnitude, lots of counter ion diffusion.
-- System looks linear for the most part.
-- Has a nonlinearity that is substantial at higher amplitude.
-- Nonlinearity looks like inward Na and outward K currents seen during an action potential! Is it all membrane nonlinearity or is it a combination of membrane and interface nonlinearity?
Static/Dynamic Motoneuron Modeling