?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Motor+Learning+Mechanism+on+the+Neuron+Scale+&rft.creator=Liu%2C+Mr.+Peilei&rft.creator=Wang%2C+Prof.+Ting&rft.subject=Behavioral+Neuroscience&rft.subject=Animal+Behavior&rft.subject=Biophysics&rft.subject=Cognitive+Archeology&rft.subject=Computational+Neuroscience&rft.subject=Artificial+Intelligence&rft.subject=Dynamical+Systems&rft.subject=Machine+Learning&rft.subject=Neural+Modelling&rft.description=Based+on+existing+data%2C+we+wish+to+put+forward+a+biological+model+of+motor+system+on+the+neuron+scale.+Then+we+indicate+its+implications+in+statistics+and+learning.+Specifically%2C+neuron%E2%80%99s+firing+frequency+and+synaptic+strength+are+probability+estimates+in+essence.+And+the+lateral+inhibition+also+has+statistical+implications.+From+the+standpoint+of+learning%2C+dendritic+competition+through+retrograde+messengers+is+the+foundation+of+conditional+reflex+and+%E2%80%9Cgrandmother+cell%E2%80%9D+coding.+And+they+are+the+kernel+mechanisms+of+motor+learning+and+sensory-motor+integration+respectively.+Finally%2C+we+compare+motor+system+with+sensory+system.+In+short%2C+we+would+like+to+bridge+the+gap+between+molecule+evidences+and+computational+models.+&rft.date=2014-07-01&rft.type=Preprint&rft.type=NonPeerReviewed&rft.format=application%2Fpdf&rft.identifier=http%3A%2F%2Fcogprints.org%2F9760%2F1%2Fliutextfigs.pdf&rft.identifier=++Liu%2C+Mr.+Peilei+and+Wang%2C+Prof.+Ting++(2014)+Motor+Learning+Mechanism+on+the+Neuron+Scale.++%5BPreprint%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F9760%2F