Memory capacity of a random, recurrently connected network of neurons with multiple, biologically realistic facilitation and adaptation profiles

Brian D DePasquale; Stefano Fusi

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Memory capacity of a random, recurrently connected network of neurons with multiple, biologically realistic facilitation and adaptation profiles

机译：具有多个生物学上现实的促进和适应特征的随机，反复连接的神经元网络的记忆能力

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《BMC Neuroscience》 |2011年第1期|共页
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Brian D DePasquale; Stefano Fusi;
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中图分类人类学;
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1. Mean field and capacity in realistic networks of spiking neurons storing sparsely coded random memories [J] . Curti E, Mongillo G, La Camera G, Neural computation . 2004,第12期

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2. Biologically Realistic Mean-Field Models of Conductance-Based Networks of Spiking Neurons with Adaptation [J] . di Volo Matteo, Romagnoni Alberto, Capone Cristiano, Neural computation . 2019,第4期

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3. Biologically Realistic Mean-Field Models of Conductance-Based Networks of Spiking Neurons with Adaptation [J] . di Volo Matteo, Romagnoni Alberto, Capone Cristiano, Neural computation . 2019,第4期

机译：具有适应性的尖刺神经元的生物学现实意义的平均型号模型
4. Non-equilibrium statistical mechanics of recurrent networks with realistic neurons [C] . V. Del Prete, A. C. C. Coolen Computational Neuroscience Meeting . 2004

机译：具有现实神经元的经常性网络的非平衡统计力学
5. Architecture optimization, training convergence and network estimation robustness of a fully connected recurrent neural network. [D] . Wang, Xiaoyu. 2010

机译：完全连接的递归神经网络的体系结构优化，训练收敛和网络估计的鲁棒性。
6. Memory capacity of a random recurrently connected network of neurons with multiple biologically realistic facilitation and adaptation profiles [O] . Brian D DePasquale, Stefano Fusi 2011

机译：具有多个生物学上现实的促进和适应特征的随机反复连接的神经元网络的记忆能力
7. Memory capacity of a random, recurrently connected network of neurons with multiple, biologically realistic facilitation and adaptation profiles [O] . Brian D DePasquale, Stefano Fusi 2011

机译：具有多个生物学上现实的促进和适应特征的随机，反复连接的神经元网络的记忆能力

Memory capacity of a random, recurrently connected network of neurons with multiple, biologically realistic facilitation and adaptation profiles

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