Search Results for author:
Found 30 papers, 7 papers with code
A Large-Scale Simulation Method for Neuromorphic Circuits
Splitting algorithms are well-established in convex optimization and are designed to solve large-scale problems.
Neuromorphic Control of a Pendulum
We illustrate the potential of neuromorphic control on the simple mechanical model of a pendulum, with both event-based actuation and sensing.
Kernel Modelling of Fading Memory Systems
The paper introduces a kernel-based framework to model and identify time-invariant systems with the fading memory property.
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.
Differential geometry with extreme eigenvalues in the positive semidefinite cone
Differential geometric approaches to the analysis and processing of data in the form of symmetric positive definite (SPD) matrices have had notable successful applications to numerous fields including computer vision, medical imaging, and machine learning.
Open-loop contraction design
This paper stresses the analogy of this question with the classical question of feedback stabilization.
Loop Shaping with Scaled Relative Graphs
The Scaled Relative Graph (SRG) is a generalization of the Nyquist diagram that may be plotted for nonlinear operators, and allows nonlinear robustness margins to be defined graphically.
On the incremental form of dissipativity
Following the seminal work of Zames, the input-output theory of the 70s acknowledged that incremental properties (e. g. incremental gain) are the relevant quantities to study in nonlinear feedback system analysis.
Rapid and robust synchronization via weak synaptic coupling Extended arXiv version
This paper examines how weak synaptic coupling can achieve rapid synchronization in heterogeneous networks.
Adaptive Conductance Control
Neuromodulation is central to the adaptation and robustness of animal nervous systems.
Distributed online estimation of biophysical neural networks
In this work, we propose a distributed adaptive observer for a class of nonlinear networked systems inspired by biophysical neural network models.
Reliability of Event Timing in Silicon Neurons
Analog, low-voltage electronics show great promise in producing silicon neurons (SiNs) with unprecedented levels of energy efficiency.
Spiking Control Systems
The central thesis is that the mixed nature of spiking results from a mixed feedback principle, and that a control theory of mixed feedback can be grounded in the operator theoretic concept of maximal monotonicity.
Monotone one-port circuits
Maximal monotonicity is explored as a generalization of the linear theory of passivity, aiming at an algorithmic input/output analysis of physical models.
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.
Graphical Nonlinear System Analysis
We use the recently introduced concept of a Scaled Relative Graph (SRG) to develop a graphical analysis of input-output properties of feedback systems.
Oscillations in Mixed-Feedback Systems
The calculation of the limit cycle is reformulated as the zero finding of a mixed-monotone relation, that is, of the difference of two maximally monotone relations.
Scaled relative graphs for system analysis
Scaled relative graphs were recently introduced to analyze the convergence of optimization algorithms using two dimensional Euclidean geometry.
Monotone RLC Circuits
The circuit-theoretic origins of maximal monotonicity are revisited using modern optimization algorithms for maximal monotone operators.
System identification of biophysical neuronal models
After sixty years of quantitative biophysical modeling of neurons, the identification of neuronal dynamics from input-output data remains a challenging problem, primarily due to the inherently nonlinear nature of excitable behaviors.
Neuromorphic Control
Neuromorphic engineering is a rapidly developing field that aims to take inspiration from the biological organization of neural systems to develop novel technology for computing, sensing, and actuating.
Inductive Geometric Matrix Midranges
Covariance data as represented by symmetric positive definite (SPD) matrices are ubiquitous throughout technical study as efficient descriptors of interdependent systems.
Differential dissipativity analysis of reaction-diffusion systems
This note shows how classical tools from linear control theory can be leveraged to provide a global analysis of nonlinear reaction-diffusion models.
Feedback Identification of conductance-based models
This paper applies the classical prediction error method (PEM) to the estimation of nonlinear discrete-time models of neuronal systems subject to input-additive noise.
Neuromodulation of Neuromorphic Circuits
We present a novel methodology to enable control of a neuromorphic circuit in close analogy with the physiological neuromodulation of a single neuron.
Conal Distances Between Rational Spectral Densities
The paper generalizes Thompson and Hilbert metric to the space of spectral densities.
Optimization and Control
Scaled stochastic gradient descent for low-rank matrix completion
The paper looks at a scaled variant of the stochastic gradient descent algorithm for the matrix completion problem.
On the Projective Geometry of Kalman Filter
Convergence of the Kalman filter is best analyzed by studying the contraction of the Riccati map in the space of positive definite (covariance) matrices.
Sparse plus low-rank autoregressive identification in neuroimaging time series
This paper considers the problem of identifying multivariate autoregressive (AR) sparse plus low-rank graphical models.
Manopt, a Matlab toolbox for optimization on manifolds
Optimization on manifolds is a rapidly developing branch of nonlinear optimization.
