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

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.