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State Space Representation

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...

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Related Experiment Video

Updated: May 24, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Dynamic state and parameter estimation applied to neuromorphic systems.

Emre Ozgur Neftci¹, Bryan Toth, Giacomo Indiveri

¹Institute of Informatics, University of Zurich and ETH Zurich, Zurich CH-8057, Switzerland. emre@ini.phys.ethz.ch

Neural Computation

|March 21, 2012

Summary

This summary is machine-generated.

We developed a new method to estimate parameters for computational neural models and neuromorphic hardware. This dynamic state and parameter estimation (DSPE) technique uses synchronization to link experimental data with models, enabling accurate parameter extraction.

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Area of Science:

Computational neuroscience
Neuromorphic engineering
Systems neuroscience

Background:

Computational models are crucial for understanding neural systems.
Neuromorphic engineering creates hardware analogs of biological neural systems.
Parameter estimation for complex models, especially those with unmeasurable states, is challenging.

Purpose of the Study:

To develop a procedure for estimating parameters in interacting neural population models.
To validate neuromorphic very large-scale integration (VLSI) chip network models.
To systematically extract inaccessible network parameters.

Main Methods:

Utilized a dynamic state and parameter estimation (DSPE) technique.
Employed synchronization as a tool to couple experimental data with computational models.
Applied the DSPE technique to both biological neural systems and neuromorphic hardware.

Main Results:

The DSPE technique successfully estimated parameters for interacting neural population models.
Demonstrated the efficiency of DSPE in validating neuromorphic spike-based VLSI chip network models.
Showcased the ability to systematically extract network parameters like synaptic weights and time constants, even when not directly observable.

Conclusions:

The developed DSPE procedure effectively addresses parameter estimation challenges in complex neural models.
This method is efficient for validating neuromorphic hardware models and extracting key network parameters.
The technique offers a valuable tool for model-based identification and configuration of neuromorphic multichip VLSI systems.