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SUMMARY:System Identification of Neuronal Behaviors - Thiago Burghi\, Univ
 ersity of Cambridge
DTSTART:20200604T130000Z
DTEND:20200604T140000Z
UID:TALK141814@talks.cam.ac.uk
CONTACT:Alberto Padoan
DESCRIPTION:In order to estimate reliable models from noisy input-output d
 ata\, system identification techniques usually require that the data be ge
 nerated by a process with a fading memory. Non-equilibrium systems such as
  neuronal and chaotic models lack a fading memory. Their identification is
  challenging\, in particular in the presence of process noise.\nTo approac
 h the problem of neuronal system identification\, we build on the fundamen
 tal  observation that neuronal systems have a global relative degree of on
 e\, and a contracting  internal dynamics. In particular\, while a neuronal
  system does not have a fading memory\, its inverse dynamics does\; this a
 llows endowing neuronal systems with fading memory by output feedback.  Ba
 sed on these properties\, we first analyze the asymptotic behavior of the 
 prediction-error method when applied to the identification of single-compa
 rtment conductance-based models subject to input-additive noise.  We show 
 that consistent estimates can be obtained by gathering data under voltage-
 clamp\, when measurement noise  (but not process noise) is neglected.  We 
 then approach the multiple-input-multiple-output problem of identifying a 
 biophysical neuronal network from a black-box perspective. We propose iden
 tifying the MIMO system's internal dynamics using a universal approximator
  model structure\, given by a filter bank of orthogonal basis transfer fun
 ctions cascaded with multilayer static artificial neural networks. We show
  that identification of single neurons and half-center oscillators is succ
 essful\, and that the identified models capture the localized regions of n
 egative conductance that are characteristic of biophysical neuronal system
 s.\n\nThe seminar can be accessed following the link: https://zoom.us/j/92
 226434758?pwd=c1J3WWhHK3AyTkdxcUlHNzRsSmtDQT09
LOCATION:Cambridge University Engineering Department
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