Germer, C. M. | Search bioRxiv

bioRxiv2024

NeuroNella: A Robust Unsupervised Algorithm for Identification of Neural Activity from Multielectrode Arrays

We introduce NeuroNella, an automated algorithm developed for the identification of neuronal activity from multichannel electrode arrays. In evaluations conducted on recordings from implanted probes in the nervous system of rodents and primates, the algorithm demonstrated remarkable accuracy, showcasing an error rate of less than 1% compared to ground-truth patch clamp signals. Notably, the proposed algorithm handles large datasets efficiently without the necessity of a GPU system. The results highlighted the algorithms efficacy in detecting sources in a wide amplitude range and its adaptability in accommodating minor probe shifts. Moreover, the high robustness exhibited by the algorithm in decomposing recordings lasting up to 30 minutes underscores its potential for enabling longitudinal studies and prolonged recording sessions, thus opening new avenues for future brain/machine interface applications.

neuroscience↗

bioRxiv2022

Common synaptic inputs are not distributed homogeneously among the motor neurons that innervate synergistic muscles

The force generated by the muscles involved in an action is produced by common synaptic inputs received by the engaged motor neurons. The purpose of our study was to identify the low-dimensional latent components, defined hereafter as neural modules, underlying the discharge rates of the motor units from two knee extensors (vastus medialis and lateralis) and two hand muscles (index and thumb muscles) during isometric contractions. The neural modules were extracted by factor analysis from the pooled motor units and no assumptions were made regarding the orthogonality of the modules or the association between the modules and each muscle. Factor analysis identified two independent neural modules that captured most of the covariance in the discharge rates of the motor units in the synergistic muscles. Although the neural modules were strongly correlated with the discharge rates of motor units in each of the synergistic pair of muscles, not all motor units in a muscle were correlated with the neural module for that muscle. The distribution of motor units across the pair of neural modules differed for each muscle: 80% of the motor units in first dorsal interosseous were more strongly correlated with the neural module for that muscle, whereas the proportion was 70%, 60%, and 45% for the thenar, vastus medialis, and vastus lateralis muscles. All other motor units either belonged to both modules or to the module for the other muscle (15% for vastus lateralis). Based on a simulation of 480 integrate-and-fire neurons receiving independent and common inputs, we demonstrate that factor analysis identifies the three neural modules with high levels of accuracy. Our results indicate that the correlated discharge rates of motor units arise from at least two sources of common synaptic input that are not distributed homogeneously among the motor neurons innervating synergistic muscles.

neuroscience↗