Global InformationSynthetic nervous system information
 | This article has multiple issues. Please help improve it or discuss these issues on the talk page. (Learn how and when to remove these template messages)
|
Synthetic Nervous System (SNS) is a computational neuroscience model that may be developed with the Functional Subnetwork Approach (FSA) to create biologically plausible models of circuits in a nervous system.[1] The FSA enables the direct analytical tuning of dynamical networks that perform specific operations within the nervous system without the need for global optimization methods like genetic algorithms and reinforcement learning. The primary use case for a SNS is system control, where the system is most often a simulated biomechanical model or a physical robotic platform.[2][3][4][5][6][7][8] An SNS is a form of a neural network much like artificial neural networks (ANNs), convolutional neural networks (CNN), and recurrent neural networks (RNN). The building blocks for each of these neural networks is a series of nodes and connections denoted as neurons and synapses. More conventional artificial neural networks rely on training phases where they use large data sets to form correlations and thus “learn” to identify a given object or pattern. When done properly this training results in systems that can produce a desired result, sometimes with impressive accuracy. However, the systems themselves are typically “black boxes” meaning there is no readily distinguishable mapping between structure and function of the network. This makes it difficult to alter the function, without simply starting over, or extract biological meaning except in specialized cases.[9][10] The SNS method differentiates itself by using details of both structure and function of biological nervous systems. The neurons and synapse connections are intentionally designed rather than iteratively changed as part of a learning algorithm.
As in many other computational neuroscience models (Rybak,[11] Eliasmith[12]), the details of a neural model are informed by experimental data wherever possible. Not every study can measure every parameter of the network under investigation, requiring the modeler to make assumptions regarding plausible parameter values. Rybak uses a sampling method where each node is composed of many neurons and each particular neuron’s parameters are pulled from a probability distribution.[11] Eliasmith uses what they call the Neural Engineering Framework (NEF) in which the user specifies the functions of the network and the synaptic and neural properties are learned over time.[12] SNS follows a similar approach via the Functional Subnetwork Approach (FSA). FSA allows parameters within the network (e.g., membrane conductances, synaptic conductances) to be designed analytically based on their intended function. As a result, it is possible to use this approach to directly assemble networks that perform basic functions, like addition or subtraction, as well as dynamical operations like differentiation and integration.
- ^ a b Szczecinski, Nicholas S.; Hunt, Alexander J.; Quinn, Roger D. (2017). "A Functional Subnetwork Approach to Designing Synthetic Nervous Systems That Control Legged Robot Locomotion". Frontiers in Neurorobotics. 11: 37. doi:10.3389/fnbot.2017.00037. ISSN 1662-5218. PMC 5552699. PMID 28848419.
- ^ Szczecinski, Nicholas S.; Quinn, Roger D.; Hunt, Alexander J. (2020). "Extending the Functional Subnetwork Approach to a Generalized Linear Integrate-and-Fire Neuron Model". Frontiers in Neurorobotics. 14: 577804. doi:10.3389/fnbot.2020.577804. ISSN 1662-5218. PMC 7691602. PMID 33281592.
- ^ Szczecinski, Nicholas S.; Hunt, Alexander J.; Quinn, Roger D. (February 2017). "Design process and tools for dynamic neuromechanical models and robot controllers". Biological Cybernetics. 111 (1): 105–127. doi:10.1007/s00422-017-0711-4. ISSN 1432-0770. PMID 28224266. S2CID 253889279.
- ^ Rybak, Ilya A.; Shevtsova, Natalia A.; Lafreniere-Roula, Myriam; McCrea, David A. (2006-12-01). "Modelling spinal circuitry involved in locomotor pattern generation: insights from deletions during fictive locomotion". The Journal of Physiology. 577 (Pt 2): 617–639. doi:10.1113/jphysiol.2006.118703. ISSN 0022-3751. PMC 1890439. PMID 17008376.
- ^ Hunt, Alexander; Szczecinski, Nicholas; Quinn, Roger (2017). "Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot". Frontiers in Neurorobotics. 11: 18. doi:10.3389/fnbot.2017.00018. ISSN 1662-5218. PMC 5378996. PMID 28420977.
- ^ Szczecinski, Nicholas S.; Quinn, Roger D. (2017-06-08). "Template for the neural control of directed stepping generalized to all legs of MantisBot". Bioinspiration & Biomimetics. 12 (4): 045001. Bibcode:2017BiBi...12d5001S. doi:10.1088/1748-3190/aa6dd9. ISSN 1748-3190. PMID 28422047. S2CID 46824463.
- ^ Rubeo, Scott; Szczecinski, Nicholas; Quinn, Roger (January 2018). "A Synthetic Nervous System Controls a Simulated Cockroach". Applied Sciences. 8 (1): 6. doi:10.3390/app8010006. ISSN 2076-3417.
- ^ Riddle, Shane; Nourse, William R. P.; Yu, Zhuojun; Thomas, Peter J.; Quinn, Roger D. (2022). "A Synthetic Nervous System with Coupled Oscillators Controls Peristaltic Locomotion". In Hunt, Alexander; Vouloutsi, Vasiliki; Moses, Kenneth; Quinn, Roger; Mura, Anna; Prescott, Tony; Verschure, Paul F. M. J. (eds.). Biomimetic and Biohybrid Systems. Lecture Notes in Computer Science. Vol. 13548. Cham: Springer International Publishing. pp. 249–261. doi:10.1007/978-3-031-20470-8_25. ISBN 978-3-031-20470-8.
- ^ Castelvecchi, Davide (2016-10-06). "Can we open the black box of AI?". Nature News. 538 (7623): 20–23. Bibcode:2016Natur.538...20C. doi:10.1038/538020a. PMID 27708329. S2CID 4465871.
- ^ Beniaguev, David; Segev, Idan; London, Michael (2021-09-01). "Single cortical neurons as deep artificial neural networks". Neuron. 109 (17): 2727–2739.e3. doi:10.1016/j.neuron.2021.07.002. ISSN 0896-6273. PMID 34380016.
- ^ a b Rybak, Ilya A.; Shevtsova, Natalia A.; Kiehn, Ole (2013-11-15). "Modelling genetic reorganization in the mouse spinal cord affecting left-right coordination during locomotion". The Journal of Physiology. 591 (22): 5491–5508. doi:10.1113/jphysiol.2013.261115. ISSN 1469-7793. PMC 3853491. PMID 24081162.
- ^ a b Eliasmith, Chris (June 2005). "A unified approach to building and controlling spiking attractor networks". Neural Computation. 17 (6): 1276–1314. doi:10.1162/0899766053630332. ISSN 0899-7667. PMID 15901399. S2CID 2588747.