Energy-efficient network activity from disparate circuit parameters

Neural circuits can produce similar activity patterns from vastly different combinations of channel and synaptic conductances. These conductances are tuned for specific activity patterns but might also reflect additional constraints, such as metabolic cost or robustness to perturbations. How do such...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2022-11, Vol.119 (44), p.e2207632119-e2207632119
Main Authors: Deistler, Michael, Macke, Jakob H, Gonçalves, Pedro J
Format: Article
Language:English
Subjects:
Activity patterns
Biological Sciences
Circuits
Cost analysis
Energy
Energy efficiency
Machine learning
Mathematical models
Metabolism
Neural networks
Parameter identification
Perturbation
Pylorus
Robustness
Temperature
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Summary:Neural circuits can produce similar activity patterns from vastly different combinations of channel and synaptic conductances. These conductances are tuned for specific activity patterns but might also reflect additional constraints, such as metabolic cost or robustness to perturbations. How do such constraints influence the range of permissible conductances? Here we investigate how metabolic cost affects the parameters of neural circuits with similar activity in a model of the pyloric network of the crab . We present a machine learning method that can identify a range of network models that generate activity patterns matching experimental data and find that neural circuits can consume largely different amounts of energy despite similar circuit activity. Furthermore, a reduced but still significant range of circuit parameters gives rise to energy-efficient circuits. We then examine the space of parameters of energy-efficient circuits and identify potential tuning strategies for low metabolic cost. Finally, we investigate the interaction between metabolic cost and temperature robustness. We show that metabolic cost can vary across temperatures but that robustness to temperature changes does not necessarily incur an increased metabolic cost. Our analyses show that despite metabolic efficiency and temperature robustness constraining circuit parameters, neural systems can generate functional, efficient, and robust network activity with widely disparate sets of conductances.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.2207632119