<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wang, Yu</style></author><author><style face="normal" font="default" size="100%">Chen, Yi</style></author><author><style face="normal" font="default" size="100%">Chen, Lu</style></author><author><style face="normal" font="default" size="100%">Herron, Bruce J</style></author><author><style face="normal" font="default" size="100%">Chen, Xiang Yang</style></author><author><style face="normal" font="default" size="100%">Wolpaw, Jonathan R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Motor learning changes the axon initial segment of the spinal motoneuron.</style></title><secondary-title><style face="normal" font="default" size="100%">J Physiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Physiol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Ankyrins</style></keyword><keyword><style  face="normal" font="default" size="100%">Axon Initial Segment</style></keyword><keyword><style  face="normal" font="default" size="100%">Axons</style></keyword><keyword><style  face="normal" font="default" size="100%">Conditioning, Operant</style></keyword><keyword><style  face="normal" font="default" size="100%">H-Reflex</style></keyword><keyword><style  face="normal" font="default" size="100%">Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Motor Neurons</style></keyword><keyword><style  face="normal" font="default" size="100%">Neuronal Plasticity</style></keyword><keyword><style  face="normal" font="default" size="100%">Rats</style></keyword><keyword><style  face="normal" font="default" size="100%">Rats, Sprague-Dawley</style></keyword><keyword><style  face="normal" font="default" size="100%">Spinal Cord</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 May</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">602</style></volume><pages><style face="normal" font="default" size="100%">2107-2126</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We are studying the mechanisms of H-reflex operant conditioning, a simple form of learning. Modelling studies in the literature and our previous data suggested that changes in the axon initial segment (AIS) might contribute. To explore this, we used blinded quantitative histological and immunohistochemical methods to study in adult rats the impact of H-reflex conditioning on the AIS of the spinal motoneuron that produces the reflex. Successful, but not unsuccessful, H-reflex up-conditioning was associated with greater AIS length and distance from soma; greater length correlated with greater H-reflex increase. Modelling studies in the literature suggest that these increases may increase motoneuron excitability, supporting the hypothesis that they may contribute to H-reflex increase. Up-conditioning did not affect AIS ankyrin G (AnkG) immunoreactivity (IR), p-p38 protein kinase IR, or GABAergic terminals. Successful, but not unsuccessful, H-reflex down-conditioning was associated with more GABAergic terminals on the AIS, weaker AnkG-IR, and stronger p-p38-IR. More GABAergic terminals and weaker AnkG-IR correlated with greater H-reflex decrease. These changes might potentially contribute to the positive shift in motoneuron firing threshold underlying H-reflex decrease; they are consistent with modelling suggesting that sodium channel change may be responsible. H-reflex down-conditioning did not affect AIS dimensions. This evidence that AIS plasticity is associated with and might contribute to H-reflex conditioning adds to evidence that motor learning involves both spinal and brain plasticity, and both neuronal and synaptic plasticity. AIS properties of spinal motoneurons are likely to reflect the combined influence of all the motor skills that share these motoneurons. KEY POINTS: Neuronal action potentials normally begin in the axon initial segment (AIS). AIS plasticity affects neuronal excitability in development and disease. Whether it does so in learning is unknown. Operant conditioning of a spinal reflex, a simple learning model, changes the rat spinal motoneuron AIS. Successful, but not unsuccessful, H-reflex up-conditioning is associated with greater AIS length and distance from soma. Successful, but not unsuccessful, down-conditioning is associated with more AIS GABAergic terminals, less ankyrin G, and more p-p38 protein kinase. The associations between AIS plasticity and successful H-reflex conditioning are consistent with those between AIS plasticity and functional changes in development and disease, and with those predicted by modelling studies in the literature. Motor learning changes neurons and synapses in spinal cord and brain. Because spinal motoneurons are the final common pathway for behaviour, their AIS properties probably reflect the combined impact of all the behaviours that use these motoneurons.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolpaw, Jonathan R</style></author><author><style face="normal" font="default" size="100%">Kamesar, Adam</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Heksor: the central nervous system substrate of an adaptive behaviour.</style></title><secondary-title><style face="normal" font="default" size="100%">J Physiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Physiol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adaptation, Psychological</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">central nervous system</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Neuronal Plasticity</style></keyword><keyword><style  face="normal" font="default" size="100%">Plastics</style></keyword><keyword><style  face="normal" font="default" size="100%">Synapses</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2022</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">600</style></volume><pages><style face="normal" font="default" size="100%">3423-3452</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Over the past half-century, the largely hardwired central nervous system (CNS) of 1970 has become the ubiquitously plastic CNS of today, in which change is the rule not the exception. This transformation complicates a central question in neuroscience: how are adaptive behaviours - behaviours that serve the needs of the individual - acquired and maintained through life? It poses a more basic question: how do many adaptive behaviours share the ubiquitously plastic CNS? This question compels neuroscience to adopt a new paradigm. The core of this paradigm is a CNS entity with unique properties, here given the name heksor from the Greek hexis. A heksor is a distributed network of neurons and synapses that changes itself as needed to maintain the key features of an adaptive behaviour, the features that make the behaviour satisfactory. Through their concurrent changes, the numerous heksors that share the CNS negotiate the properties of the neurons and synapses that they all use. Heksors keep the CNS in a state of negotiated equilibrium that enables each heksor to maintain the key features of its behaviour. The new paradigm based on heksors and the negotiated equilibrium they create is supported by animal and human studies of interactions among new and old adaptive behaviours, explains otherwise inexplicable results, and underlies promising new approaches to restoring behaviours impaired by injury or disease. Furthermore, the paradigm offers new and potentially important answers to extant questions, such as the generation and function of spontaneous neuronal activity, the aetiology of muscle synergies, and the control of homeostatic plasticity.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">15</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thompson, Aiko K</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The simplest motor skill: mechanisms and applications of reflex operant conditioning.</style></title><secondary-title><style face="normal" font="default" size="100%">Exerc Sport Sci Rev</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Exerc Sport Sci Rev</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Conditioning, Operant</style></keyword><keyword><style  face="normal" font="default" size="100%">H-Reflex</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Motor Skills</style></keyword><keyword><style  face="normal" font="default" size="100%">Muscle, Skeletal</style></keyword><keyword><style  face="normal" font="default" size="100%">Neuronal Plasticity</style></keyword><keyword><style  face="normal" font="default" size="100%">Reflex</style></keyword><keyword><style  face="normal" font="default" size="100%">Spinal Cord</style></keyword><keyword><style  face="normal" font="default" size="100%">Spinal Cord Injuries</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/24508738</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">82-90</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Operant conditioning protocols can change spinal reflexes gradually, which are the simplest behaviors. This article summarizes the evidence supporting two propositions: that these protocols provide excellent models for defining the substrates of learning and that they can induce and guide plasticity to help restore skills, such as locomotion, that have been impaired by spinal cord injury or other disorders.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Harnessing neuroplasticity for clinical applications.</style></title><secondary-title><style face="normal" font="default" size="100%">Brain : a journal of neurology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Neuronal Plasticity</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/22374936</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">135</style></volume><pages><style face="normal" font="default" size="100%">e215; author reply e216</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leuthardt, E C</style></author><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author><author><style face="normal" font="default" size="100%">Roland, Jarod</style></author><author><style face="normal" font="default" size="100%">Rouse, Adam</style></author><author><style face="normal" font="default" size="100%">Moran, D</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evolution of brain-computer interfaces: going beyond classic motor physiology.</style></title><secondary-title><style face="normal" font="default" size="100%">Neurosurg Focus</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Neurosurg Focus</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Cerebral Cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Man-Machine Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Motor Cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">Movement</style></keyword><keyword><style  face="normal" font="default" size="100%">Movement Disorders</style></keyword><keyword><style  face="normal" font="default" size="100%">Neuronal Plasticity</style></keyword><keyword><style  face="normal" font="default" size="100%">Prostheses and Implants</style></keyword><keyword><style  face="normal" font="default" size="100%">Research</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Processing, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/19569892</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">E4</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record></records></xml>