Skip navigation
Sayles Laboratory

Neural systems identification

We apply engineering-inspired quantitative analyses to neural data to characterize the transfer functions of elements in complex neural circuits. A simple example is the use of the Wiener series to characterize a neuron's "filtering operation" based on a white-noise input. From this approach, it is possible to estimate the impulse response of the system through a process of reverse correlation. The magnitude and phase response of filters derived in this manner have been fundamental in developing computational models of the auditory periphery.

 

Video 1: Auditory-nerve fiber spikes in response to broadband Gaussian noise

Spike trains in response to broadband Gaussian noise at a fixed input sound level are useful for characterizing the impulse response of neural systems.

 

Figure 1: Reverse-correlation function ("revcor")

The first-order cross correlation of the neuron's response with the Gaussian-noise input yields the first-order Wiener kernel, similar to the reverse correlation ("revcor") function.

 

Figure 2: Second-order Wiener kernel analyses

Second-order correlation can be used to reveal the even-order non-linearities of the system. Furthermore, singular-value decomposition can reveal both excitatory and suppressive components of these non-linearities.

 

(Some of) our (broad) questions in systems identification

  • What can these approaches tell us about the function of the auditory efferent control systems?
  • How can we leverage systems identification approaches to efficiently study the non-linear level-dependence of peripheral filtering?
  • Can we dissect binaural neural circuits using these techniques?

 

Further reading

  • Carney LH & Yin TC (1988). Temporal coding of resonances by low-frequency auditory nerve fibers: single-fiber responses and a population model. J. Neurophysiol. 60: 1653-1677.
  • Recio-Spinoso A et al. (2005). Wiener-kernel analysis of responses to noise of chinchilla auditory nerve fibers. J. Neurophysiol. 93: 3615-3634.
  • Henry KS et al. (2016). Distorted tonotopic coding of temporal envelope and fine structure with noise-induced hearing loss. J. Neurosci. 36: 2227-2237.
  • Sayles M et al. (2016). Suppression measured from chinchilla auditory-nerve fibers following noise-induced hearing loss: adaptive tracking and systems identification approaches. Adv. Exp. Med. Biol. 894: 285-295.