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Switched-capacitor neuromorphs with wide-range variable dynamics.

J G Elias¹, D M Northmore

¹Dept. of Electr. Eng., Delaware Univ., Newark, DE.

IEEE Transactions on Neural Networks

|January 1, 1995

Summary

Switched capacitors enable artificial dendritic trees (ADTs) to mimic biological neurons. These silicon neuro-morphs offer tunable, long-lasting impulse responses, crucial for advanced neuromorphic computing.

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Area of Science:

Neuroscience
Electrical Engineering
Materials Science

Background:

Artificial dendritic trees (ADTs) are computational models inspired by biological neurons.
Developing adaptable and programmable resistive elements for ADTs is essential for advanced neuromorphic systems.
Existing resistive elements often lack the wide dynamic range and tunability required for complex neural simulations.

Purpose of the Study:

To describe the use of switched capacitors as programmable resistive elements in ADTs.
To demonstrate the capability of silicon neuro-morphs with ADTs to generate prolonged and tunable impulse responses.
To investigate the programmable resistance range and dynamic response characteristics of these novel ADT configurations.

Main Methods:

Implementation of switched capacitors as resistive elements within spatially extensive artificial dendritic trees.
Fabrication of silicon neuro-morphs incorporating these ADT structures.
Experimental characterization of impulse response duration, shape tunability, and frequency selectivity.

Main Results:

Demonstrated impulse responses lasting millions of times longer than the initiating impulse.
Achieved wide-range tunability of dynamical responses in both shape and duration.
Established an indirect programmable resistance range for switched-capacitor resistors between 500 KΩ and 1000 GΩ.
Observed variable impulse response functions, tunable frequency selectivity, and rate-invariance in spatiotemporal pattern responses.

Conclusions:

Switched capacitors serve as effective wide-range, programmable resistive elements for artificial dendritic trees.
Silicon neuro-morphs utilizing these ADTs exhibit remarkable long-duration and tunable impulse responses.
The developed technology offers significant potential for creating more sophisticated and adaptable neuromorphic computing systems.