Acta Scientific Computer Sciences

Research Article Volume 4 Issue 12

Brainopy: A Biologically Relevant SQLite-Based Artificial Neural Network Library

Jensen ZH Tan, Nicholas TF Tan and Maurice HT Ling*

School of Applied Sciences, Temasek Polytechnic, Singapore

*Corresponding Author: Maurice HT Ling, School of Applied Sciences, Temasek Polytechnic, Singapore.

Received: October 27, 2022; Published: November 15, 2022

Abstract

Artificial neural network (ANN) is a computing system inspired by biological neural networks but recently, there is a move towards studying biological neural networks using neuronal simulations. Hence, ANN can be a tool to study biological neural networks. However, most ANN libraries only cater to one signal (equivalent to one neurotransmitter) and generally requires neurons to be organized into layers, which may not have direct biological equivalence. Here, we present Brainopy as a biologically relevant Python-based ANN library as it enables multiple neurotransmitters and allow each neuron to connect to any other neurons. The constructed neural network is persisted as an SQLite database file. Despite focusing on biological relevancy over computational efficiency, we built and simulated neural networks of up to 15000 neurons (within the neuronal complexity of Caenorhabditis elegans, a well-studied organism in neuroscience) using a retail laptop.

Keywords: Artificial Neural Network; Python Library; Biologically Relevant Representation; SQLite Persistence

References

  1. Basheer IA and Hajmeet M. “Artificial Neural Networks: Fundamentals, Computing, Design, and Application”. Journal of Microbiological Methods 43 (2000): 3-31.
  2. Bhagya Raj GVS and Dash KK. “Comprehensive Study on Applications of Artificial Neural Network in Food Process Modeling”. Critical Reviews in Food Science and Nutrition10 (2022): 2756-2783.
  3. Feng F., et al. “Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art”. IEEE Transactions on Microwave Theory and Techniques (2022): 1-23.
  4. Nagy B., et al. “Application of Artificial Neural Networks in the Process Analytical Technology of Pharmaceutical Manufacturing - A Review”. The AAPS Journal4 (2022): 74.
  5. Mumali F. “Artificial Neural Network-Based Decision Support Systems in Manufacturing Processes: A Systematic Literature Review”. Computers and Industrial Engineering 165 (2022): 107964.
  6. Padma KR and Don KR. “Artificial Neural Network Applications in Analysis of Forensic Science”. Cyber Security and Digital Forensics, eds Ghonge MM, Pramanik S, Mangrulkar R, Le D (Wiley), 1st Ed (2022): 59-72.
  7. Wiener N. Cybernetics: or, Control and Communication in the Animal and the Machine (MIT Press, Cambridge, Massachusetts), 2nd Ed (1965).
  8. Anokhin PK. “Systems Analysis of the Integrative Activity of the Neuron (1974)”. The Pavlovian Journal of Biological Science2 (1974): 43-101.
  9. Shen Y., et al. “The Emergence of Molecular Systems Neuroscience”. Molecular Brain1 (2022): 7.
  10. Dennis EJ., et al. “Systems Neuroscience of Natural Behaviors in Rodents”. The Journal of Neuroscience5 (2021): 911-919.
  11. Tolomeo S and Yu R. “Brain Network Dysfunctions in Addiction: A Meta-Analysis of Resting-State Functional Connectivity”. Translational Psychiatry1 (2022): 41.
  12. Brennan AR and Arnsten AFT. “Neuronal Mechanisms Underlying Attention Deficit Hyperactivity Disorder: The Influence of Arousal on Prefrontal Cortical Function”. Annals of the New York Academy of Sciences 1129 (2008): 236-245.
  13. Bi B., et al. “Neural Network of Bipolar Disorder: Toward Integration of Neuroimaging and Neurocircuit-Based Treatment Strategies”. Translational Psychiatry1 (2022): 143.
  14. Lee TY., et al. “Distinct Neural Networks Associated with Obsession and Delusion: A Connectome-Wide Association Study”. Psychological Medicine8 (2021): 1320-1328.
  15. Yankouskaya A., et al. “Neural Connectivity Underlying Reward and Emotion-Related Processing: Evidence From a Large-Scale Network Analysis”. Frontiers in Systems Neuroscience 16 (2022): 833625.
  16. Glaser JI., et al. “The Roles of Supervised Machine Learning in Systems Neuroscience”. Progress in Neurobiology 175 (2019): 126-137.
  17. Savage N. “How AI and Neuroscience Drive Each Other Forwards”. Nature7766 (2019): S15-S17.
  18. Ito T., et al. “Constructing Neural Network Models from Brain Data Reveals Representational Transformations Linked to Adaptive Behavior”. Nature Communications1 (2022): 673.
  19. Güçlü U., et al. “Modeling the Dynamics of Human Brain Activity with Recurrent Neural Networks”. Frontiers in Computational Neuroscience 11 (2017).
  20. Anand A., et al. “Quantifying the Brain Predictivity of Artificial Neural Networks With Nonlinear Response Mapping”. Frontiers in Computational Neuroscience 15 (2021): 609721.
  21. Fakhar K and Hilgetag CC. “Systematic Perturbation of an Artificial Neural Network: A Step Towards Quantifying Causal Contributions in the Brain”. PLoS Computational Biology6 (2022): e1010250.
  22. van Nifterick AM., et al. “A Multiscale Brain Network Model Links Alzheimer’s Disease-Mediated Neuronal Hyperactivity to Large-Scale Oscillatory Slowing”. Alzheimer’s Research and Therapy1 (2022): 101.
  23. Gruzenkin DV., et al. “Neural Networks to Solve Modern Artificial Intelligence Tasks”. Journal of Physics: Conference Series3 (2019): 033058.
  24. Wiedemann S., et al. “Compact and Computationally Efficient Representation of Deep Neural Networks”. IEEE Transactions on Neural Networks and Learning Systems3 (2020): 772-785.
  25. Cuevas J. “Neurotransmitters and Their Life Cycle”. Reference Module in Biomedical Sciences (Elsevier) (2019): B9780128012383113000.
  26. Snyder SH. “A Life of Neurotransmitters”. Annual Review of Pharmacology and Toxicology1 (2017): 1-11.
  27. Syms S., et al. “Survey on Neural Network Architectures with Deep Learning”. Journal of Soft Computing Paradigm3 (2020): 186-194.
  28. Elsken T., et al. “Neural Architecture Search: A Survey”. Journal of Machine Learning Research1 (2019): 1997-2017.
  29. Suhaimi A., et al. “Representation Learning in the Artificial and Biological Neural Networks Underlying Sensorimotor Integration”. Science Advances22 (2022): eabn0984.
  30. González C and Couve A. “The Axonal Endoplasmic Reticulum and Protein Trafficking: Cellular Bootlegging South of the Soma”. Seminars in Cell and Developmental Biology 27 (2014): 23-31.
  31. Gormal RS and Meunier FA. “Nanoscale Organization of the Pre-Synapse: Tracking the Neurotransmitter Release Machinery”. Current Opinion in Neurobiology 75 (2022): 102576.
  32. Bruinsma TJ., et al. “The Relationship Between Dopamine Neurotransmitter Dynamics and the Blood-Oxygen-Level-Dependent (BOLD) Signal: A Review of Pharmacological Functional Magnetic Resonance Imaging”. Frontiers in Neuroscience 12 (2018): 238.
  33. Gomis Perez C., et al. “Rapid Propagation of Membrane Tension at Retinal Bipolar Neuron Presynaptic Terminals”. Science Advances1 (2022): eabl4411.
  34. Wang X., et al. “The Krüppel-Like Factor Dar1 Determines Multipolar Neuron Morphology”. The Journal of Neuroscience42 (2015): 14251-14259.
  35. Santos J and Shlizerman E. “Closing the Loop: Optimal Stimulation of elegans Neuronal Network via Adaptive Control to Exhibit Full Body Movements”. BMC Neuroscience 16.S1 (2015): O14.
  36. Sengupta P and Samuel ADT. “Caenorhabditis elegans: A Model System for Systems Neuroscience”. Current Opinion in Neurobiology6 (2009): 637-643.
  37. Rapti G. “A Perspective on elegans Neurodevelopment: From Early Visionaries to a Booming Neuroscience Research”. Journal of Neurogenetics 34.3-4 (2020): 259-272.
  38. Kaiser M and Hilgetag CC. “Nonoptimal Component Placement, But Short Processing Paths, Due to Long-Distance Projections in Neural Systems”. PLoS Computational Biology7 (2006): e95.
  39. Cook SJ., et al. “Whole-Animal Connectomes of Both Caenorhabditis elegans Sexes”. Nature7763 (2019): 63-71.

Citation

Citation: Maurice HT Ling., et al. “Brainopy: A Biologically Relevant SQLite-Based Artificial Neural Network Library". Acta Scientific Computer Sciences 4.12 (2022): 13-22.

Copyright

Copyright: © 2022 Maurice HT Ling., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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