| 1 |
At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks |
1170 |
2004 |
| 2 |
Integration of nanoscale memristor synapses in neuromorphic computing architectures |
766 |
2013 |
| 3 |
Long short-term memory and learning-to-learn in networks of spiking neurons |
765 |
2018 |
| 4 |
A solution to the learning dilemma for recurrent networks of spiking neurons |
755 |
2020 |
| 5 |
Edge of chaos and prediction of computational performance for neural circuit models |
629 |
2007 |
| 6 |
Restoring vision in adverse weather conditions with patch-based denoising diffusion models |
507 |
2023 |
| 7 |
Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses |
410 |
2016 |
| 8 |
Deep rewiring: Training very sparse deep networks |
403 |
2017 |
| 9 |
Combining predictions for accurate recommender systems |
376 |
2010 |
| 10 |
A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback |
368 |
2008 |
| 11 |
What can a neuron learn with spike-timing-dependent plasticity? |
299 |
2005 |
| 12 |
Neuromorphic hardware in the loop: Training a deep spiking network on the brainscales wafer-scale system |
248 |
2017 |
| 13 |
Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons |
210 |
2010 |
| 14 |
Branch-specific plasticity enables self-organization of nonlinear computation in single neurons |
201 |
2011 |
| 15 |
Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning |
191 |
2014 |
| 16 |
What makes a dynamical system computationally powerful |
187 |
2007 |
| 17 |
A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task |
175 |
2010 |
| 18 |
Network plasticity as Bayesian inference |
166 |
2015 |
| 19 |
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets |
132 |
2019 |
| 20 |
A compound memristive synapse model for statistical learning through STDP in spiking neural networks |
130 |
2014 |
| 21 |
Reinforcement learning on slow features of high-dimensional input streams |
124 |
2010 |
| 22 |
Spike frequency adaptation supports network computations on temporally dispersed information |
111 |
2021 |
| 23 |
Memory-efficient deep learning on a SpiNNaker 2 prototype |
90 |
2018 |
| 24 |
A new approach towards vision suggested by biologically realistic neural microcircuit models |
76 |
2002 |
| 25 |
On computational power and the order-chaos phase transition in reservoir computing |
76 |
2008 |
| 26 |
Improved neighborhood-based algorithms for large-scale recommender systems |
71 |
2008 |
| 27 |
Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment |
70 |
2014 |
| 28 |
A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning |
69 |
2018 |
| 29 |
Methods for estimating the computational power and generalization capability of neural microcircuits |
65 |
2004 |
| 30 |
Dendritic computing: branching deeper into machine learning |
64 |
2022 |
| 31 |
Efficient reward-based structural plasticity on a SpiNNaker 2 prototype |
64 |
2019 |
| 32 |
Input prediction and autonomous movement analysis in recurrent circuits of spiking neurons |
53 |
2003 |
| 33 |
Advancing spatio-temporal processing through adaptation in spiking neural networks |
38 |
2025 |
| 34 |
Emergence of stable synaptic clusters on dendrites through synaptic rewiring |
37 |
2020 |
| 35 |
Spiking neurons can learn to solve information bottleneck problems and extract independent components |
37 |
2009 |
| 36 |
Training adversarially robust sparse networks via bayesian connectivity sampling |
36 |
2021 |
| 37 |
Long term memory and the densest k-subgraph problem |
34 |
2018 |
| 38 |
A comparison of manual neuronal reconstruction from biocytin histology or 2-photon imaging: morphometry and computer modeling |
32 |
2014 |
| 39 |
Feedback inhibition shapes emergent computational properties of cortical microcircuit motifs |
32 |
2017 |
| 40 |
Synaptic sampling: A Bayesian approach to neural network plasticity and rewiring |
32 |
2015 |
| 41 |
Brain computation: a computer science perspective |
30 |
2019 |
| 42 |
Distributed bayesian computation and self-organized learning in sheets of spiking neurons with local lateral inhibition |
30 |
2015 |
| 43 |
STDP forms associations between memory traces in networks of spiking neurons |
29 |
2020 |
| 44 |
Nanoscale connections for brain-like circuits |
26 |
2015 |
| 45 |
H-mem: Harnessing synaptic plasticity with hebbian memory networks |
24 |
2020 |
| 46 |
Pattern representation and recognition with accelerated analog neuromorphic systems |
24 |
2017 |
| 47 |
Cortical oscillations support sampling-based computations in spiking neural networks |
21 |
2022 |
| 48 |
Improving robustness against stealthy weight bit-flip attacks by output code matching |
21 |
2022 |
| 49 |
Embodied synaptic plasticity with online reinforcement learning |
19 |
2019 |
| 50 |
Foundations for a circuit complexity theory of sensory processing |
19 |
2000 |
| 51 |
On the classification capability of sign-constrained perceptrons |
19 |
2008 |
| 52 |
Rapid learning with phase-change memory-based in-memory computing through learning-to-learn |
19 |
2025 |
| 53 |
Theoretical analysis of learning with reward-modulated spike-timing-dependent plasticity |
17 |
2007 |
| 54 |
Generating conceptual architectural 3D geometries with denoising diffusion models |
16 |
2023 |
| 55 |
Neural circuits for pattern recognition with small total wire length |
16 |
2002 |
| 56 |
Assembly pointers for variable binding in networks of spiking neurons |
14 |
2024 |
| 57 |
Fast learning without synaptic plasticity in spiking neural networks |
14 |
2024 |
| 58 |
Information bottleneck optimization and independent component extraction with spiking neurons |
14 |
2006 |
| 59 |
Adversarially robust spiking neural networks through conversion |
13 |
2023 |
| 60 |
A model for structured information representation in neural networks of the brain |
13 |
2020 |
| 61 |
Eligibility traces provide a data-inspired alternative to backpropagation through time |
13 |
2019 |
| 62 |
Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning |
13 |
2009 |
| 63 |
A probabilistic model for learning in cortical microcircuit motifs with data-based divisive inhibition |
12 |
2017 |
| 64 |
Memory-dependent computation and learning in spiking neural networks through Hebbian plasticity |
12 |
2023 |
| 65 |
Spike-frequency adaptation provides a long short-term memory to networks of spiking neurons |
12 |
2020 |
| 66 |
The location of the axon initial segment affects the bandwidth of spike initiation dynamics |
12 |
2020 |
| 67 |
Classification with deep neural networks on an accelerated analog neuromorphic system |
11 |
2017 |
| 68 |
Reward-based stochastic self-configuration of neural circuits |
11 |
2017 |
| 69 |
Bayesian modelling of student misconceptions in the one-digit multiplication with probabilistic programming |
10 |
2016 |
| 70 |
Many-joint robot arm control with recurrent spiking neural networks |
10 |
2021 |
| 71 |
A criterion for the convergence of learning with spike timing dependent plasticity |
9 |
2005 |
| 72 |
CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling |
9 |
2016 |
| 73 |
Context association in pyramidal neurons through local synaptic plasticity in apical dendrites |
8 |
2024 |
| 74 |
Wire length as a circuit complexity measure |
8 |
2005 |
| 75 |
AI-Infused Design: Merging parametric models for architectural design |
5 |
2024 |
| 76 |
Classification of Whisker deflections from evoked responses in the somatosensory barrel cortex with spiking neural networks |
5 |
2022 |
| 77 |
Context-dependent computations in spiking neural networks with apical modulation |
5 |
2023 |
| 78 |
Revisiting the role of synaptic plasticity and network dynamics for fast learning in spiking neural networks |
4 |
2021 |
| 79 |
Dynamic action inference with recurrent spiking neural networks |
3 |
2021 |
| 80 |
Focus on algorithms for neuromorphic computing |
3 |
2023 |
| 81 |
Non-synaptic plasticity enables memory-dependent local learning |
3 |
2025 |
| 82 |
Quantized rewiring: hardware-aware training of sparse deep neural networks |
3 |
2023 |
| 83 |
A scalable hybrid training approach for recurrent spiking neural networks |
2 |
2025 |
| 84 |
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural networks |
2 |
2019 |
| 85 |
Fast learning synapses with molecular spin valves via selective magnetic potentiation |
2 |
2019 |
| 86 |
Interaction of generalization and out-of-distribution detection capabilities in deep neural networks |
2 |
2023 |
| 87 |
Preserving real-world robustness of neural networks under sparsity constraints |
2 |
2024 |
| 88 |
Privacy-Aware Lifelong Learning |
2 |
2025 |
| 89 |
Spike-based symbolic computations on bit strings and numbers |
2 |
2022 |
| 90 |
The wire-length complexity of neural networks |
2 |
2016 |
| 91 |
Variable Binding through Assemblies in Spiking Neural Networks |
2 |
2016 |
| 92 |
Additional material to the paper: What can a neuron learn with spike-timing-dependent plasticity |
1 |
2004 |
| 93 |
Adversarially Robust Spiking Neural Networks with Sparse Connectivity |
1 |
2025 |
| 94 |
Assembly projections support the assignment of thematic roles to concepts in networks of spiking neurons |
1 |
2016 |
| 95 |
Controlling ReRAMâs switching characteristics with shadow memory for continual learning |
1 |
2025 |
| 96 |
Eliminating the teacher in reservoir computing |
1 |
2011 |
| 97 |
Fault pruning: Robust training of neural networks with memristive weights |
1 |
2023 |
| 98 |
Reinforcement learning on complex visual stimuli |
1 |
2009 |
| 99 |
Self-supervised learning of probabilistic prediction through synaptic plasticity in apical dendrites: A normative model |
1 |
2023 |
| 100 |
Slowness in hierarchical networks for visual processing |
1 |
2009 |
| 101 |
Slow processes of neurons enable a biologically plausible approximation to policy gradient |
1 |
2019 |
| 102 |
Total wire length as a salient circuit complexity measure for sensory processing |
1 |
2001 |
| 103 |
A biologically motivated rule for supervised learning with spiking neurons |
0 |
2020 |
| 104 |
A functional role of cortical oscillations for probabilistic computation in spiking neural networks |
0 |
2021 |
| 105 |
A normative framework for learning top-down predictions through synaptic plasticity in apical dendrites |
0 |
2021 |
| 106 |
Cortical oscillations support sampling-based computations in spiking neural networks |
0 |
2021 |
| 107 |
Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing |
0 |
2024 |
| 108 |
on STORE-RECALL task |
0 |
2021 |
| 109 |
Recurrent network models, reservoir computing |
0 |
2022 |
| 110 |
Simulating Barrel Cortex Oscillations Using Spiking Neural Networks |
0 |
2022 |
| 111 |
Spike-based symbolic computations on symbol sequences |
0 |
2021 |
| 112 |
Spike-frequency adaptation contributes long short-term memory to networks of spiking neurons |
0 |
2020 |
| 113 |
Spike frequency adaptation supports network computations |
0 |
2021 |
| 114 |
Structured Information Representation with Assemblies of Spiking Neurons |
0 |
2021 |
| 115 |
Workshop on Spiking Neural Networks |
0 |
2024 |