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Synthetic biological neural networks: From current implementations to future perspectives.

Ana Halužan Vasle1, Miha Moškon1

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Synthetic biological neural networks (SYNBIONNs) mimic the brain for applications in medicine and biosensing. Current SYNBIONN implementations are limited, but future research aims for scalable, in vivo networks with online learning capabilities.

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Area of Science:

  • Synthetic biology
  • Computational neuroscience
  • Bioengineering

Background:

  • Artificial neural networks (ANNs) are powerful computational models inspired by the human brain.
  • Synthetic biology aims to create biological counterparts to ANNs, termed synthetic biological neural networks (SYNBIONNs).
  • SYNBIONNs hold potential for medicine, biosensors, and biotechnology.

Purpose of the Study:

  • To review current implementations and models of SYNBIONNs.
  • To explore biological platforms suitable for constructing SYNBIONNs.
  • To discuss future possibilities and challenges for in vivo SYNBIONNs.

Main Methods:

  • Review of existing SYNBIONN literature and models.
  • Identification of biological platforms for SYNBIONN design.
  • Analysis of challenges for scalable, in vivo SYNBIONN implementation.

Main Results:

  • SYNBIONN implementations are currently sparse and heavily reliant on in silico pretraining and human input.
  • Various biological platforms show promise for developing perceptron and multilayer SYNBIONNs.
  • Significant challenges remain in achieving scalable, in vivo biological neural networks with online learning.

Conclusions:

  • Despite current limitations, SYNBIONNs offer exciting possibilities for diverse applications.
  • Further research is needed to overcome technical hurdles for practical in vivo SYNBIONN deployment.
  • Developing scalable, self-learning biological neural networks is a key future goal.