000218661 001__ 218661
000218661 005__ 20190317000449.0
000218661 0247_ $$2doi$$a10.1016/j.cobeha.2016.05.012
000218661 02470 $$2ISI$$a000395323700010
000218661 037__ $$aARTICLE
000218661 245__ $$aDoes computational neuroscience need new synaptic learning paradigms?
000218661 269__ $$a2016
000218661 260__ $$bElsevier Science Bv$$c2016$$aAmsterdam
000218661 300__ $$a6
000218661 336__ $$aJournal Articles
000218661 520__ $$aComputational neuroscience is dominated by a few paradigmatic models, but it remains an open question whether the existing modelling frameworks are sufficient to explain observed behavioural phenomena in terms of neural implementation. We take learning and synaptic plasticity as an example and point to open questions, such as one-shot learning and acquiring internal representations of the world for flexible planning.
000218661 700__ $$0249012$$g257047$$aBrea, Johanni Michael
000218661 700__ $$aGerstner, Wulfram$$0240007$$g111732
000218661 773__ $$tCurrent Opinion in Behavioral Sciences
000218661 8564_ $$uhttps://infoscience.epfl.ch/record/218661/files/1-s2.0-S2352154616301048-main.pdf$$zPublisher's version$$s364148$$yPublisher's version
000218661 909C0 $$0252006$$pLCN
000218661 909CO $$pIC$$particle$$ooai:infoscience.tind.io:218661$$qGLOBAL_SET$$pSV
000218661 917Z8 $$x257047
000218661 937__ $$aEPFL-ARTICLE-218661
000218661 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000218661 980__ $$aARTICLE