Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Gradient estimation in dendritic reinforcement learning | The Journal of Mathematical Neuroscience SpringerOpen0 This Provisional PDF corresponds to the article as it appeared upon acceptance. Fully formatted PDF and full text HTML versions will be made available soon. Gradient estimation in dendritic reinforcement learning The Journal of Mathematical Neuroscience 2012 2 2 doi 2190-8567-2-2 Mathieu Schiess schiess@ Robert Urbanczik urbanczik@ Walter Senn senn@ ISSN 2190-8567 Article type Research Submission date 12 May 2011 Acceptance date 15 February 2012 Publication date 15 February 2012 Article URL http content 2 1 2 This peer-reviewed article was published immediately upon acceptance. It can be downloaded printed and distributed freely for any purposes see copyright notice below . For information about publishing your research in The Journal of Mathematical Neuroscience go to http authors instructions For information about other SpringerOpen publications go to http 2012 Schiess etal. licensee Springer. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Gradient estimation in dendritic reinforcement learning Mathieu Schiess Robert Urbanczik and Walter Senn Department of Physiology University of Bern Btihlplatz 5 CH-3012 Bern Switzerland Corresponding author senn@ Email addresses MS schiess@ RU urbanczik@ Abstract We study synaptic plasticity in a complex neuronal cell model where NMDA-spikes can arise in certain dendritic zones. In the context of reinforcement learning two kinds of plasticity rules are derived zone reinforcement ZR and cell reinforcement CR which both optimize the expected reward by stochastic gradient ascent. For ZR the .
