Diversified physiological sensory input connectivity questions the existence of distinct classes of spinal interneurons

The Bcm rule allows a spinal cord model to learn rhythmic movements

Currently, it is accepted that animal locomotion is controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current central pattern generator models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the Bienenstock-Cooper-Munro learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to central pattern generator models, because rhythm generating neurons and genetically defined connectivity are not required in our model. Though our results are not in contradiction to current models, as existing neural mechanism and structures, not used in our model, can be expected to facilitate the kind of learning demonstrated here. Therefore, our model could be used to augment existing models.

Distinguishing subtypes of spinal locomotor neurons to inform circuit function and dysfunction

Locomotion is a complex motor task executed by spinal neurons. Given the diversity of spinal cord neurons, linking neuronal cell type to function is a challenge. Molecular identification of broad spinal interneuronal classes provided a great advance. Recent studies have used other classifiers, including location, electrophysiological properties, and connectivity, in addition to gene profiling, to narrow the acuity with which groups of neurons can be related to specific functions. However, there are also functional populations without a clear identifier, as exemplified by rhythm generating neurons. Other considerations, including experience or plasticity, add a layer of complexity to the definition of functional subpopulations of spinal neurons, but spinal cord injury may provide insight.

Basic principles of processing of afferent information by spinal interneurons

Integrative functions of spinal interneurons are well recognized but the relative role of different interneuronal populations in this process continues to be investigated. It therefore appeared useful to review the principles of integration of afferent information by the interneurons analyzed so far as these principles should apply also to those remaining to be analyzed. Considering the results of both functional and morphological studies of spinal interneurons and of the morphology and immunochemistry of afferent fibres that provide input to them, the following five basic principles of processing of afferent information by them will be outlined; (i) afferent information of any origin is forwarded to several neuronal populations, (ii) information from any sources of input is distributed unevenly, (iii) input from several sources is integrated by individual neurons as well as by their populations, (iv) specific combinations of input are integrated by different neuronal populations and (v) afferent input to spinal interneurons is only one of the features distinguishing their functional populations. As the spinal neuronal organization and properties of neurons and afferent fibres in the so far investigated species (cat, rodents, primates) have been found to resemble, future studies utilizing molecular techniques in the mouse should allow the new data to integrate with those of the preceding studies and the principles outlined above as well as any new ones should apply also in humans.

A Model for Self-Organization of Sensorimotor Function: Spinal Interneuronal Integration

Control of musculoskeletal systems depends on integration of voluntary commands and somatosensory feedback in the complex neural circuits of the spinal cord. Particular connectivity patterns have been identified experimentally, and it has been suggested that these may result from the wide variety of transcriptional types that have been observed in spinal interneurons. We ask instead whether the details of these connectivity patterns (and perhaps many others) can arise as a consequence of Hebbian adaptation during early development. We constructed an anatomically simplified model plant system with realistic muscles and sensors and connected it to a recurrent, random neuronal network consisting of both excitatory and inhibitory neurons endowed with Hebbian learning rules. We then generated a wide set of randomized muscle twitches typical of those described during fetal development and allowed the network to learn. Multiple simulations consistently resulted in diverse and stable patterns of activity and connectivity that included subsets of the interneurons that were similar to 'archetypical' interneurons described in the literature. We also found that such learning led to an increased degree of cooperativity between interneurons when performing larger limb movements on which it had not been trained. Hebbian learning gives rise to rich sets of diverse interneurons whose connectivity reflects the mechanical properties of the plant. At least some of the transcriptomic diversity may reflect the effects of this process rather than the cause of the connectivity. Such a learning process seems better suited to respond to the musculoskeletal mutations that underlie the evolution of new species.