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High-sensitivity memristor-based threshold detection

High-sensitivity memristor-based threshold detection
High-sensitivity memristor-based threshold detection

The ability to read brain activity across large swathes of cortex at very high resolution both spatially and temporally is a holy grail objective of modern neuroscience. In this endeavour, the minuteness of neural signals arriving from needle probes (10s to 100s of μV) poses a significant challenge, typically solved using high spec amplifiers. However, when the objective is to detect neural spikes the input signals of interest are inherently sparse, and much energy is spent amplifying data points that will be ultimately discarded. In this work we propose that a possible solution is to distance ourselves from the need to amplify the neural waveforms, and instead opt for performing threshold detection directly on the input signal; which is often sufficient to detect neural spiking. We thus present a high sensitivity threshold detection circuit concept that uses its offset voltage as the reference threshold and thus directly transforms differential input signal samples into digital values. The use of memristive devices within the design allows us to finely tune the detector's offset voltage, thus ensuring sufficient operational flexibility. Using SPICE simulations we demonstrate an exemplar design built using our concept. First we shown its functionality and then we proceed to examine how: i) mismatch at strategically chosen devices affects the amplifier's offset voltage and ii) changing the resistive state of the memristive devices involved helps the designer control the offset voltage.

2158-1525
Institute of Electrical and Electronics Engineers Inc.
Serb, Alexander
30f5ec26-f51d-42b3-85fd-0325a27a792c
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Serb, Alexander
30f5ec26-f51d-42b3-85fd-0325a27a792c
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf

Serb, Alexander and Prodromakis, Themistoklis (2018) High-sensitivity memristor-based threshold detection. In 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings. vol. 2018-May, Institute of Electrical and Electronics Engineers Inc.. (doi:10.1109/ISCAS.2018.8351250).

Record type: Conference or Workshop Item (Paper)

Abstract

The ability to read brain activity across large swathes of cortex at very high resolution both spatially and temporally is a holy grail objective of modern neuroscience. In this endeavour, the minuteness of neural signals arriving from needle probes (10s to 100s of μV) poses a significant challenge, typically solved using high spec amplifiers. However, when the objective is to detect neural spikes the input signals of interest are inherently sparse, and much energy is spent amplifying data points that will be ultimately discarded. In this work we propose that a possible solution is to distance ourselves from the need to amplify the neural waveforms, and instead opt for performing threshold detection directly on the input signal; which is often sufficient to detect neural spiking. We thus present a high sensitivity threshold detection circuit concept that uses its offset voltage as the reference threshold and thus directly transforms differential input signal samples into digital values. The use of memristive devices within the design allows us to finely tune the detector's offset voltage, thus ensuring sufficient operational flexibility. Using SPICE simulations we demonstrate an exemplar design built using our concept. First we shown its functionality and then we proceed to examine how: i) mismatch at strategically chosen devices affects the amplifier's offset voltage and ii) changing the resistive state of the memristive devices involved helps the designer control the offset voltage.

Full text not available from this repository.

More information

e-pub ahead of print date: 4 May 2018
Published date: 2018
Venue - Dates: 2018 IEEE International Symposium on Circuits and Systems, Florence, Italy, 2018-05-27 - 2018-05-30

Identifiers

Local EPrints ID: 426996
URI: https://eprints.soton.ac.uk/id/eprint/426996
ISSN: 2158-1525
PURE UUID: 7dd3407c-0e44-4788-ad23-19f1f8a08a74
ORCID for Themistoklis Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

Catalogue record

Date deposited: 20 Dec 2018 17:30
Last modified: 09 Aug 2019 00:32

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