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Related Experiment Video

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Designing and Implementing Nervous System Simulations on LEGO Robots

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Published on: May 25, 2013

Silicon-Neuron Design: A Dynamical Systems Approach.

John V Arthur¹, Kwabena Boahen

¹Stanford University, Stanford, CA.

IEEE Transactions on Circuits and Systems. I, Regular Papers : a Publication of the IEEE Circuits and Systems Society

|May 28, 2011

Summary

This summary is machine-generated.

We developed a method to design spiking silicon neurons using dynamical systems theory. This approach allows for creating neuron models with specific dynamics and simplifies circuit implementation for advanced neural processing.

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Published on: April 15, 2015

Area of Science:

Neuroscience
Electrical Engineering
Computational Neuroscience

Background:

Dynamical systems theory provides a framework for understanding complex behaviors in neural systems.
Designing artificial neurons with specific dynamic properties is crucial for neuromorphic computing.

Purpose of the Study:

To present a systematic approach for designing spiking silicon neurons using dynamical systems theory.
To translate theoretical neuron models into practical subthreshold current-mode circuits.
To demonstrate the functionality and adaptability of a fabricated neuron circuit.

Main Methods:

Applied dynamical systems theory to define desired neuron dynamics and abstraction levels.
Developed a procedure to convert neuron model equations into subthreshold current-mode circuit designs.
Fabricated a positive-feedback integrate-and-fire neuron circuit using 0.25 micrometer CMOS technology.
Analyzed and characterized the fabricated circuit's performance.

Main Results:

Successfully designed and fabricated a spiking silicon neuron circuit.
Demonstrated that the circuit can be configured to exhibit specific neural behaviors.
Confirmed the circuit's ability to perform spike-frequency adaptation and two types of bursting.

Conclusions:

Dynamical systems theory offers an effective framework for designing spiking silicon neurons.
The presented method enables the creation of versatile artificial neurons with tunable dynamics.
The fabricated circuit serves as a proof-of-concept for advanced neuromorphic hardware design.