204 research outputs found
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Feedback for neuronal system identification
In order to estimate reliable models from noisy input-output data, system identification techniques usually require that the data be generated 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 input noise. In this thesis, we propose a methodology based on the prediction-error method for the identification of neuronal systems subject to input-additive noise. We build on the fundamental observation that while a neuronal model does not have a fading memory, it can be transformed into a fading memory system by output feedback. Our ideas can be generalized to any non-equilibrium system sharing this property.
At the core of the methodology is the use of output feedback in experiment design. We provide a theoretical justification for this design choice, which has been exploited in neurophysiology since the invention of the voltage-clamp experiment. To investigate the problem of feedback for identification, we first address the estimation of simple non-equilibrium systems in Lure form, and show that feedback allows estimating the nonlinearity in a static experiment. We then address the estimation of conductance-based models. Assuming that an informed choice can be made on the elements of the model structure, we show that consistent parameter estimates can be obtained when noise is only present at the system input. Finally, we approach the problem from a black-box perspective, and propose identifying the neuronal internal dynamics using a universal approximator with Generalized Orthogonal Basis Functions.Coordenação de Aperfeiçoamento de Pessoal de NÃvel Superior (CAPES) – Brasil (Finance Code 001
Adaptive observers for biophysical neuronal circuits
This paper presents adaptive observers for online state and parameter
estimation of a class of nonlinear systems motivated by biophysical models of
neuronal circuits. We first present a linear-in-the-parameters design that
solves a classical recursive least-squares problem. Then, building on this
simple design, we present an augmented adaptive observer for models with a
nonlinearly parameterized internal dynamics, the parameters of which we
interpret as structured uncertainty. We present a convergence and robustness
analysis based on contraction theory, and illustrate the potential of the
approach in neurophysiological applications by means of numerical simulations.Comment: 16 pages. The Julia code used in this paper can be found in
https://github.com/thiagoburghi/online-learnin
Feedback for nonlinear system identification
Motivated by neuronal models from neuroscience, we consider the system
identification of simple feedback structures whose behaviors include nonlinear
phenomena such as excitability, limit-cycles and chaos. We show that output
feedback is sufficient to solve the identification problem in a two-step
procedure. First, the nonlinear static characteristic of the system is
extracted, and second, using a feedback linearizing law, a mildly nonlinear
system with an approximately-finite memory is identified. In an ideal setting,
the second step boils down to the identification of a LTI system. To illustrate
the method in a realistic setting, we present numerical simulations of the
identification of two classical systems that fit the assumed model structure.Comment: 18th European Control Conference (ECC), Napoli, Italy, June 25-28
201
Robust online estimation of biophysical neural circuits
The control of neuronal networks, whether biological or neuromorphic, relies
on tools for estimating parameters in the presence of model uncertainty. In
this work, we explore the robustness of adaptive observers for neuronal
estimation. Inspired by biology, we show that decentralization and redundancy
help recover the performance of a centralized recursive mean square algorithm
in the presence of uncertainty and mismatch on the internal dynamics of the
model.Comment: 6 pages, 5 figures, accepted at the 62nd IEEE Conference on Decision
and Contro
Distributed online estimation of biophysical neural networks
In this work, we propose a distributed adaptive observer for a class of
networked systems inspired by biophysical conductance-based neural network
models. Neural systems learn by adjusting intrinsic and synaptic weights in a
distributed fashion, with neuronal membrane voltages carrying information from
neighbouring neurons in the network. Using contraction analysis, we show that
this learning principle can be used to design an adaptive observer based on a
decentralized learning rule that greatly reduces the number of observer states
required for consistent exponential convergence of parameter estimates. This
novel design is relevant for biological, biomedical and neuromorphic
applications
Kinematic control design for wheeled mobile robots with longitudinal and lateral slip
The motion control of wheeled mobile robots at high speeds under adverse
ground conditions is a difficult task, since the robots' wheels may be subject
to different kinds of slip. This work introduces an adaptive kinematic
controller that is capable of solving the trajectory tracking problem of a
nonholonomic mobile robot under longitudinal and lateral slip. While the
controller can effectively compensate for the longitudinal slip, the lateral
slip is a more involved problem to deal with, since nonholonomic robots cannot
directly produce movement in the lateral direction. To show that the proposed
controller is still able to make the mobile robot follow a reference trajectory
under lateral and longitudinal time-varying slip, the solutions of the robot's
position and orientation error dynamics are shown to be uniformly ultimately
bounded. Numerical simulations are presented to illustrate the robot's
performance using the proposed adaptive control law
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