{"citation":{"chicago":"Monk, Travis, Cristina Savin, and Jörg Lücke. “Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics,” 29:4285–93. Neural Information Processing Systems, 2016.","short":"T. Monk, C. Savin, J. Lücke, in:, Neural Information Processing Systems, 2016, pp. 4285–4293.","apa":"Monk, T., Savin, C., &#38; Lücke, J. (2016). Neurons equipped with intrinsic plasticity learn stimulus intensity statistics (Vol. 29, pp. 4285–4293). Presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine: Neural Information Processing Systems.","ista":"Monk T, Savin C, Lücke J. 2016. Neurons equipped with intrinsic plasticity learn stimulus intensity statistics. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 4285–4293.","ama":"Monk T, Savin C, Lücke J. Neurons equipped with intrinsic plasticity learn stimulus intensity statistics. In: Vol 29. Neural Information Processing Systems; 2016:4285-4293.","ieee":"T. Monk, C. Savin, and J. Lücke, “Neurons equipped with intrinsic plasticity learn stimulus intensity statistics,” presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine, 2016, vol. 29, pp. 4285–4293.","mla":"Monk, Travis, et al. <i>Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics</i>. Vol. 29, Neural Information Processing Systems, 2016, pp. 4285–93."},"project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"volume":29,"type":"conference","date_published":"2016-01-01T00:00:00Z","status":"public","oa_version":"None","_id":"948","scopus_import":1,"language":[{"iso":"eng"}],"quality_controlled":"1","year":"2016","acknowledgement":"DFG Cluster of Excellence EXC 1077/1 (Hearing4all) and  LU 1196/5-1 (JL and TM), People Programme (Marie Curie Actions) FP7/2007-2013 grant agreement no. 291734 (CS)","intvolume":"        29","page":"4285 - 4293","day":"01","publisher":"Neural Information Processing Systems","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","alternative_title":["Advances in Neural Information Processing Systems"],"title":"Neurons equipped with intrinsic plasticity learn stimulus intensity statistics","date_updated":"2021-01-12T08:22:08Z","publication_status":"published","author":[{"last_name":"Monk","full_name":"Monk, Travis","first_name":"Travis"},{"first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87","full_name":"Savin, Cristina","last_name":"Savin"},{"first_name":"Jörg","full_name":"Lücke, Jörg","last_name":"Lücke"}],"main_file_link":[{"url":"https://papers.nips.cc/paper/6582-neurons-equipped-with-intrinsic-plasticity-learn-stimulus-intensity-statistics"}],"publist_id":"6469","department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:49:21Z","month":"01","conference":{"location":"Barcelona, Spaine","end_date":"2016-12-10","name":"NIPS: Neural Information Processing Systems","start_date":"2016-12-05"},"ec_funded":1,"abstract":[{"lang":"eng","text":"Experience constantly shapes neural circuits through a variety of plasticity mechanisms. While the functional roles of some plasticity mechanisms are well-understood, it remains unclear how changes in neural excitability contribute to learning. Here, we develop a normative interpretation of intrinsic plasticity (IP) as a key component of unsupervised learning. We introduce a novel generative mixture model that accounts for the class-specific statistics of stimulus intensities, and we derive a neural circuit that learns the input classes and their intensities. We will analytically show that inference and learning for our generative model can be achieved by a neural circuit with intensity-sensitive neurons equipped with a specific form of IP. Numerical experiments verify our analytical derivations and show robust behavior for artificial and natural stimuli. Our results link IP to non-trivial input statistics, in particular the statistics of stimulus intensities for classes to which a neuron is sensitive. More generally, our work paves the way toward new classification algorithms that are robust to intensity variations."}]}