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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.
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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

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Implementing spiking neural networks for real-time signal-processing and control applications: a model-validated FPGA

Martin J Pearson¹, A G Pipe, B Mitchinson

¹University of the West of England, Intelligent Autonomous Systems Laboratory, Frenchay, Bristol BS16 1QY, UK. martin.pearson@uwe.ac.uk

IEEE Transactions on Neural Networks

|January 29, 2008

Summary

This study introduces two hardware architectures for simulating large networks of leaky-integrate-and-fire (LIF) neurons on FPGAs. These systems enable real-time, bio-inspired neural processing for robotic control applications.

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Area of Science:

Neuroscience
Robotics
Computer Engineering

Background:

Hardware architectures are crucial for simulating large-scale neural networks.
Existing systems face challenges in real-time performance for closed-loop robotic control.
Biologically plausible neural networks (NNs) offer advanced control capabilities.

Purpose of the Study:

To present two FPGA-based hardware architectures for modeling leaky-integrate-and-fire (LIF) neuron networks.
To enhance performance for real-time applications in mobile robotic vehicles.
To facilitate the integration of bio-inspired neural processing into real-world control systems.

Main Methods:

Implementation of two fixed-point arithmetic hardware architectures on a single FPGA.
Simulation of neural networks comprising over 1000 neurons using biologically plausible models.
Development of a system for porting floating-point models to fixed-point FPGA representations.

Main Results:

Successful simulation of large neural networks (>1000 neurons) with biologically plausible models.
Demonstrated real-time performance suitable for closed-loop robotic control systems.
Validation of the hardware architecture through a development system and the Whiskerbot project.

Conclusions:

The developed FPGA architectures effectively model large LIF neural networks.
The neuroprocessor is suitable for mobile robotic applications requiring real-time neural processing.
The development system aids collaboration between neuroscientists and engineers for embodied systems.