Motor pattern generation is robust to neural network anatomical imbalance favoring inhibition but not excitation

De Graaf, Myriam Lauren; Mochizuki, Luis; Thies, Frederik; Wagner, Heiko; Le Mouel, Charlotte

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Zusammenfassung

Animals display rich and coordinated motor patterns during walking and running. Previous modelling as well as experimental results suggest that the balance between excitation and inhibition in neural networks may be critical for generating such structured motor patterns. However, biological neural networks have an anatomical imbalance between excitatory and inhibitory neural populations. We explore the influence of such an anatomical imbalance on the ability of a reservoir computing artificial neural network to learn human locomotor patterns for slow walking, fast walking and running. We varied the numbers of neurons, connections percentages and connection strengths of excitatory and inhibitory populations. We showed that performance depended on the network anatomy. First, it deteriorated when the total number of neurons was too small or the total connection strength was too large. Second, performance was critically dependent on the balance between excitation and inhibition. Imbalance towards excitation caused a reduction in the richness of internal network dynamics, leading to a stereotypical motor output and poor overall performance. In contrast, rich internal dynamics and good overall performance were found when the network anatomy was either balanced or imbalanced towards inhibition. This suggests that motor pattern generation may be robust to increased inhibition but not increased excitation in neural networks.

Details zur Publikation

Veröffentlichungsjahr: 2022
Sprache, in der die Publikation verfasst ist: Englisch
Link zum Volltext: https://www.biorxiv.org/content/10.1101/2022.04.21.489087v1.full.pdf