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Information Encoding and Decoding in In Vitro Neural Networks on Micro Electrode Arrays through Stimulation Timing.

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Title: Information Encoding and Decoding in In Vitro Neural Networks on Micro Electrode Arrays through Stimulation Timing.
Authors: LINDELL, TRYM A. E.1 trym.a.e.lindell@gmail.com, RAMSTAD, OLA H.1, SANDVIG, IOANNA2, SANDVIG, AXEL2, NICHELE, STEFANO3
Source: International Journal of Unconventional Computing. 2025, Vol. 20 Issue 3, p155-195. 41p.
Subjects: Encoding, Decoders & decoding, Action potentials, Artificial neural networks, Microelectrodes, Computational neuroscience
Abstract: A primary challenge in utilizing in vitro biological neural networks for computations is finding good encoding and decoding schemes for inputting and decoding data to and from the networks. Furthermore, identifying the optimal parameter settings for a given combination of encoding and decoding schemes adds additional complexity to this challenge. In this study we explore stimulation timing as an encoding method, i.e. we encode information as the delay between stimulation pulses and identify the bounds and acuity of stimulation timings which produce linearly separable spike responses. We also examine the optimal readout parameters for a linear decoder in the form of epoch length, time bin size and epoch offset. Our results suggest that stimulation timings between 36 and 436ms may be optimal for encoding and that different combinations of readout parameters may be optimal at different parts of the evoked spike response. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Unconventional Computing is the property of Old City Publishing, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: A primary challenge in utilizing in vitro biological neural networks for computations is finding good encoding and decoding schemes for inputting and decoding data to and from the networks. Furthermore, identifying the optimal parameter settings for a given combination of encoding and decoding schemes adds additional complexity to this challenge. In this study we explore stimulation timing as an encoding method, i.e. we encode information as the delay between stimulation pulses and identify the bounds and acuity of stimulation timings which produce linearly separable spike responses. We also examine the optimal readout parameters for a linear decoder in the form of epoch length, time bin size and epoch offset. Our results suggest that stimulation timings between 36 and 436ms may be optimal for encoding and that different combinations of readout parameters may be optimal at different parts of the evoked spike response. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Unconventional Computing is the property of Old City Publishing, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.32908/ijuc.v20.300924
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      – Code: eng
        Text: English
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        PageCount: 41
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      – SubjectFull: Encoding
        Type: general
      – SubjectFull: Decoders & decoding
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      – SubjectFull: Action potentials
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      – SubjectFull: Artificial neural networks
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      – SubjectFull: Microelectrodes
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      – SubjectFull: Computational neuroscience
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      – TitleFull: Information Encoding and Decoding in In Vitro Neural Networks on Micro Electrode Arrays through Stimulation Timing.
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            NameFull: LINDELL, TRYM A. E.
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              Text: 2025
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