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Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
Published on: June 1, 2015
Locomotor primitives in newborn babies and their development
Nadia Dominici1, Yuri P Ivanenko, Germana Cappellini
1Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy.
Science (New York, N.Y.)
|November 19, 2011
Summary
Rudimentary movements evolve with development, retaining primitive neural patterns instead of being replaced. These basic locomotion patterns are conserved across diverse species, suggesting a common evolutionary origin.
Area of Science:
- Neuroscience
- Developmental Biology
- Comparative Physiology
Background:
- The developmental trajectory of motor control from rudimentary to sophisticated movements is not well understood.
- Existing theories suggest primitive neural control patterns are replaced during development, not retained.
Purpose of the Study:
- To identify and characterize the fundamental patterns of lumbosacral motoneuron activity during locomotion across different developmental stages.
- To investigate the evolutionary conservation of these motor control patterns across diverse animal species.
Main Methods:
- Multimuscle recordings of lumbosacral motoneuron activity were performed in human neonates, toddlers, preschoolers, and adults.
- Similar recordings were conducted in rat, cat, macaque, and guineafowl models.
Main Results:
- Two basic motoneuron activity patterns identified in neonates were found to persist throughout human development.
- Two additional patterns emerged in toddlers, augmenting the neonatal patterns.
- Remarkably similar motor control patterns were observed across all studied species, including humans, rodents, felines, primates, and birds.
Conclusions:
- Locomotion development involves the retention and augmentation of primitive neural patterns, rather than their complete replacement.
- The conservation of these basic motor patterns across species suggests a common evolutionary origin for locomotion, potentially linked to ancestral neural networks.
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