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Items: 1 to 20 of 115

1.

Learning, memory, and the role of neural network architecture.

Hermundstad AM, Brown KS, Bassett DS, Carlson JM.

PLoS Comput Biol. 2011 Jun;7(6):e1002063. doi: 10.1371/journal.pcbi.1002063. Epub 2011 Jun 30.

2.

Learning grammatical structure with Echo State Networks.

Tong MH, Bickett AD, Christiansen EM, Cottrell GW.

Neural Netw. 2007 Apr;20(3):424-32. Epub 2007 May 3.

PMID:
17556116
3.

Long short-term memory.

Hochreiter S, Schmidhuber J.

Neural Comput. 1997 Nov 15;9(8):1735-80.

PMID:
9377276
4.

Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition.

Kasabov N, Dhoble K, Nuntalid N, Indiveri G.

Neural Netw. 2013 May;41:188-201. doi: 10.1016/j.neunet.2012.11.014. Epub 2012 Dec 20.

PMID:
23340243
5.

The loading problem for recursive neural networks.

Gori M, Sperduti A.

Neural Netw. 2005 Oct;18(8):1064-79. Epub 2005 Sep 29.

PMID:
16198537
6.
7.

Reward-dependent learning in neuronal networks for planning and decision making.

Dehaene S, Changeux JP.

Prog Brain Res. 2000;126:217-29. Review.

PMID:
11105649
8.

Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

Darsey JA, Griffin WO, Joginipelli S, Melapu VK.

Methods Mol Biol. 2015;1260:269-83. doi: 10.1007/978-1-4939-2239-0_17.

PMID:
25502388
9.

A modular neural network architecture for step-wise learning of grasping tasks.

Molina-Vilaplana J, Feliu-Batlle J, López-Coronado J.

Neural Netw. 2007 Jul;20(5):631-45. Epub 2007 Mar 18.

PMID:
17442538
10.

Performance of a Computational Model of the Mammalian Olfactory System.

Benjaminsson S, Herman P, Lansner A.

In: Persaud KC, Marco S, Gutiérrez-Gálvez A, editors. Neuromorphic Olfaction. Boca Raton (FL): CRC Press/Taylor & Francis; 2013. Chapter 6.

11.

Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

Ellefsen KO, Mouret JB, Clune J.

PLoS Comput Biol. 2015 Apr 2;11(4):e1004128. doi: 10.1371/journal.pcbi.1004128. eCollection 2015 Apr.

12.

Incremental learning by message passing in hierarchical temporal memory.

Rehn EM, Maltoni D.

Neural Comput. 2014 Aug;26(8):1763-809. doi: 10.1162/NECO_a_00617. Epub 2014 May 30.

PMID:
24877728
13.

PSECMAC: a novel self-organizing multiresolution associative memory architecture.

Teddy SD, Quek C, Lai EK.

IEEE Trans Neural Netw. 2008 Apr;19(4):689-712. doi: 10.1109/TNN.2007.912300.

PMID:
18390313
14.

Dynamical synapses enhance neural information processing: gracefulness, accuracy, and mobility.

Fung CC, Wong KY, Wang H, Wu S.

Neural Comput. 2012 May;24(5):1147-85. doi: 10.1162/NECO_a_00269. Epub 2012 Feb 1.

PMID:
22295986
15.

Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations.

Kühn S, Beyn WJ, Cruse H.

Biol Cybern. 2007 May;96(5):455-70. Epub 2007 Jan 9.

PMID:
17211628
16.

Ensemble learning in fixed expansion layer networks for mitigating catastrophic forgetting.

Coop R, Mishtal A, Arel I.

IEEE Trans Neural Netw Learn Syst. 2013 Oct;24(10):1623-34. doi: 10.1109/TNNLS.2013.2264952.

PMID:
24808599
17.

Hierarchical Error Representation: A Computational Model of Anterior Cingulate and Dorsolateral Prefrontal Cortex.

Alexander WH, Brown JW.

Neural Comput. 2015 Nov;27(11):2354-410. doi: 10.1162/NECO_a_00779. Epub 2015 Sep 17.

PMID:
26378874
18.

NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data.

Kasabov NK.

Neural Netw. 2014 Apr;52:62-76. doi: 10.1016/j.neunet.2014.01.006. Epub 2014 Jan 20. Review.

PMID:
24508754
19.

Computational modeling of neural plasticity for self-organization of neural networks.

Chrol-Cannon J, Jin Y.

Biosystems. 2014 Nov;125:43-54. doi: 10.1016/j.biosystems.2014.04.003. Epub 2014 Apr 24. Review.

PMID:
24769242
20.

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