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CogNet

MIT Press CogNet is the essential research tool for scholars in the brain and cognitive sciences. Visit the CogNet information page for more information.
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Winning the Lottery With Neural Connectivity Constraints: Faster Learning Across Cognitive Tasks With Spatially Constrained Sparse RNNs
Mikail Khona; Sarthak Chandra; Joy J. Ma; Ila R. Fiete
10.1162/neco_a_01613
Optimal Feedback Control for the Proportion of Energy Cost in the Upper-Arm Reaching Movement
Yoshiaki Taniai
10.1162/neco_a_01614
A Tutorial on the Spectral Theory of Markov Chains
Eddie Seabrook; Laurenz Wiskott
10.1162/neco_a_01611
Reducing Catastrophic Forgetting With Associative Learning: A Lesson From Fruit Flies
Yang Shen; Sanjoy Dasgupta; Saket Navlakha
10.1162/neco_a_01615
Self-Organization of Nonlinearly Coupled Neural Fluctuations Into Synergistic Population Codes
Hengyuan Ma; Yang Qi; Pulin Gong; Jie Zhang; Wen-lian Lu; Jianfeng Feng
10.1162/neco_a_01612
Grid Cell Percolation
Yuri Dabaghian
10.1162/neco_a_01606
Exploring Trade-Offs in Spiking Neural Networks
Florian Bacho; Dominique Chu
10.1162/neco_a_01609
Learning Intention-Aware Policies in Deep Reinforcement Learning
T. Zhao; S. Wu; G. Li; Y. Chen; G. Niu; Masashi Sugiyama
10.1162/neco_a_01607
Transfer Learning With Singular Value Decomposition of Multichannel Convolution Matrices
Tak Shing Au Yeung; Ka Chun Cheung; Michael K. Ng; Simon See; Andy Yip
10.1162/neco_a_01608
Composite Optimization Algorithms for Sigmoid Networks
Huixiong Chen; Qi Ye
10.1162/neco_a_01603
On an Interpretation of ResNets via Gate-Network Control
Changcun Huang
10.1162/neco_a_01600
Mirror Descent of Hopfield Model
Hyungjoon Soh; Dongyeob Kim; Juno Hwang; Junghyo Jo
10.1162/neco_a_01602
A Noise-Based Novel Strategy for Faster SNN Training
Chunming Jiang; Yilei Zhang
10.1162/neco_a_01604
Mean-Field Approximations With Adaptive Coupling for Networks With Spike-Timing-Dependent Plasticity
Benoit Duchet; Christian Bick; Áine Byrne
10.1162/neco_a_01601
Graph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Modeling of Multiway Data on Graphs
Yao Lei Xu; Kriton Konstantinidis; Danilo P. Mandic
10.1162/neco_a_01598
Maximal Memory Capacity Near the Edge of Chaos in Balanced Cortical E-I Networks
Takashi Kanamaru; Takao K. Hensch; Kazuyuki Aihara
10.1162/neco_a_01596
Optimal Burstiness in Populations of Spiking Neurons Facilitates Decoding of Decreases in Tonic Firing
Sylvia C. L. Durian; Mark Agrios; Gregory W. Schwartz
10.1162/neco_a_01595
Attention in a Family of Boltzmann Machines Emerging From Modern Hopfield Networks
Toshihiro Ota; Ryo Karakida
10.1162/neco_a_01597
Dynamic Modeling of Spike Count Data With Conway-Maxwell Poisson Variability
Ganchao Wei; Ian H. Stevenson
10.1162/neco_a_01593
Conductance-Based Phenomenological Nonspiking Model: A Dimensionless and Simple Model That Reliably Predicts the Effects of Conductance Variations on Nonspiking Neuronal Dynamics
Loïs Naudin; Laetitia Raison-Aubry; Laure Buhry
10.1162/neco_a_01589

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  • Online ISSN 1530-888X
  • Print ISSN 0899-7667
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