A supervised machine learning approach to characterize spinal network function.

Spontaneous activity is a common feature of immature neuronal networks throughout the central nervous system and plays an important role in network development and consolidation. In postnatal rodents, spontaneous activity in the spinal cord exhibits complex, stochastic patterns that have historically proven challenging to characterize. We developed a software tool for quickly and automatically characterizing and classifying episodes of spontaneous activity generated from developing spinal networks. We recorded spontaneous activity from in vitro lumbar ventral roots of 16 neonatal [postnatal day (P)0-P3] mice. Recordings were DC coupled and detrended, and episodes were separated for analysis. Amplitude-, duration-, and frequency-related features were extracted from each episode and organized into five classes. Paired classes and features were used to train and test supervised machine learning algorithms. Multilayer perceptrons were used to classify episodes as rhythmic or multiburst. We increased network excitability with potassium chloride and tested the utility of the tool to detect changes in features and episode class. We also demonstrate usability by having a novel experimenter use the program to classify episodes collected at a later time point (P5). Supervised machine learning-based classification of episodes accounted for changes that traditional approaches cannot detect. Our tool, named SpontaneousClassification, advances the detail in which we can study not only developing spinal networks, but also spontaneous networks in other areas of the nervous system. NEW & NOTEWORTHY Spontaneous activity is important for nervous system network development and consolidation. Our software uses machine learning to automatically and quickly characterize and classify episodes of spontaneous activity in the spinal cord of newborn mice. It detected changes in network activity following KCl-enhanced excitation. Using our software to classify spontaneous activity throughout development, in pathological models, or with neuromodulation, may offer insight into the development and organization of spinal circuits.

Retracing your footsteps: developmental insights to spinal network plasticity following injury.

During development of the spinal cord, a precise interaction occurs between descending projections and sensory afferents, with spinal networks that lead to expression of coordinated motor output. In the rodent, during the last embryonic week, motor output first occurs as regular bursts of spontaneous activity, progressing to stochastic patterns of episodes that express bouts of coordinated rhythmic activity perinatally. Locomotor activity becomes functionally mature in the 2nd postnatal wk and is heralded by the onset of weight-bearing locomotion on the 8th and 9th postnatal day. Concomitantly, there is a maturation of intrinsic properties and key conductances mediating plateau potentials. In this review, we discuss spinal neuronal excitability, descending modulation, and afferent modulation in the developing rodent spinal cord. In the adult, plastic mechanisms are much more constrained but become more permissive following neurotrauma, such as spinal cord injury. We discuss parallel mechanisms that contribute to maturation of network function during development to mechanisms of pathological plasticity that contribute to aberrant motor patterns, such as spasticity and clonus, which emerge following central injury.