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Abstract Active learning of neural circuits is emerging as a viable strategy for fast acquisition of complex behaviour. A common
example of a potentially fruitful neural circuit architecture is a recurrent network. Learning of a recurrent network is
computationally demanding and difficult to infer. Most machine learning methods could not solve this problem without a priori
assumptions about the architecture of the network. We show that learning of a simple recurrent network (SRN) can be
efficiently achieved by solving a specific inference problem. Many spatial configurations of SRNs are theoretically equivalent.
In one of these equivalent configurations, called here the equivalent configuration, the weight matrix possesses an "optimal"
property called coherent weights. We hypothesize that learning of any SRN in this equivalent configuration must be possible. If
the hidden and the output units are separable (same size), the training algorithm can be simplified to a single gradient descent.
We formalize these ideas and derive a learning algorithm that converges to the optimal configuration in all bilaterally connected
SRN classes. We show experimentally that this algorithm can learn SRNs in the equivalent configuration. Pegahban A (2014)
Unsupervised learning of recurrent network dynamics using invariant attractors. ArXiv preprint arXiv:1403.4162.Aconitum
japonicum Aconitum japonicum is a species of flowering plant in the family Ranunculaceae. It is found only in Japan. It isthreatened by habitat loss. References Sources IUCN Plant Specialist Group (1996). Aconitum japonicum. 2006 IUCN Red Listof Threatened Species. Downloaded on 20 July 2007. japonicum Category:Plants described in 1826 Category:Taxonomy articles
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for their youth academy. He made his professional debut on 26 August 2008, in a 0–1 home loss against NK Široki Brijeg. Inlate 2008, Senica was renamed Lechia Gdańsk, and Mulić became their first ever player. He made his debut for the f678ea9f9e
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