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Artificial neural networks (ANNs) have emerged as an essential tool in machine learning, achieving remarkable success across diverse domains, including image and speech generation, game playing, and robotics.
Neural networks and physical systems with emergent collective computational abilities
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Functional requirements for reward-modulated spike-timing-dependent plasticity
Frémaux, N., Sprekeler, H. & Gerstner, W · 2010
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A history of spike-timing-dependent plasticity
Markram, H., Gerstner, W. & Sjöström, P. J · 2011
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Acute stress enhances adult rat hippocampal neurogenesis and activation of newborn neurons via secreted astrocytic fgf2
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Reinforcement learning of two-joint virtual arm reaching in a computer model of sensorimotor cortex
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Neuronal dynamics: From single neurons to networks and models of cognition (Cambridge University Press, 2014)
Gerstner, W., Kistler, W. M., Naud, R. & Paninski, L · 2014
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Random feedback weights support learning in deep neural networks
Lillicrap, T. P., Cownden, D., Tweed, D. B. & Akerman, C. J · 2014
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Robotics and neuroscience
Floreano, D., Ijspeert, A. J. & Schaal, S · 2014
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Digital implementation of a spiking neural network (snn) capable of spike-timing-dependent plasticity (stdp) learning
Hu, D., Zhang, X., Xu, Z., Ferrari, S. & Mazumder, P · 2014
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Mobile robots modular navigation controller using spiking neural networks
Wang, X., Hou, Z.-G., Lv, F., Tan, M. & Wang, Y · 2014
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Biorobotics: Using robots to emulate and investigate agile locomotion
Ijspeert, A. J · 2014
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‘activity-silent’ working memory in prefrontal cortex: a dynamic coding framework
Stokes, M. G · 2015
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Astrocytes control synapse formation, function, and elimination
Chung, W.-S., Allen, N. J. & Eroglu, C · 2015
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Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip
Akopyan, F. et al · 2015
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Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot
Grinke, E., Tetzlaff, C., Wörgötter, F. & Manoonpong, P · 2015
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Cortical spiking network interfaced with virtual musculoskeletal arm and robotic arm
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Human-level control through deep reinforcement learning
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Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules
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Physical exercise increases adult hippocampal neurogenesis in male rats provided it is aerobic and sustained
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An overview of gradient descent optimization algorithms
Ruder, S · 2016
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De Jong, K · 2016
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Direct feedback alignment provides learning in deep neural networks
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Memory-efficient backpropagation through time
Gruslys, A., Munos, R., Danihelka, I., Lanctot, M. & Graves, A · 2016
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Meta-learning through hebbian plasticity in random networks
Najarro, E. & Risi, S · 2020
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Efficacy of modern neuro-evolutionary strategies for continuous control optimization
Pagliuca, P., Milano, N. & Nolfi, S · 2020
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Neuromorphic engineering: From biological to spike-based hardware nervous systems
Yang, J.-Q. et al · 2020
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Deep reinforcement learning and its neuroscientific implications
Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J. & Kurth-Nelson, Z · 2020
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Brain structural plasticity: from adult neurogenesis to immature neurons
La Rosa, C., Parolisi, R. & Bonfanti, L · 2020
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Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
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Wang, J. X. et al · 2016
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Rl2: Fast reinforcement learning via slow reinforcement learning
Duan, Y. et al · 2016
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J. et al · 2017
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Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks
Miconi, T · 2017
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Evolution strategies as a scalable alternative to reinforcement learning
Salimans, T., Ho, J., Chen, X., Sidor, S. & Sutskever, I · 2017
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On the relationship between the openai evolution strategy and stochastic gradient descent
Zhang, X., Clune, J. & Stanley, K. O · 2017
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A survey of neuromorphic computing and neural networks in hardware
Schuman, C. D. et al · 2017
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Lesort, T. et al · 2020
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Rma: Rapid motor adaptation for legged robots
Kumar, A., Fu, Z., Pathak, D. & Malik, J · 2021
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Compute and energy consumption trends in deep learning inference
Desislavov, R., Martínez-Plumed, F. & Hernández-Orallo, J · 2021
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Energy-efficient deep learning inference on edge devices
Daghero, F., Pagliari, D. J. & Poncino, M · 2021
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Spikepropamine: Differentiable plasticity in spiking neural networks
Schmidgall, S., Ashkanazy, J., Lawson, W. & Hays, J · 2021
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Cell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network
Liu, Y. H., Smith, S., Mihalas, S., Shea-Brown, E. & Sümbül, U · 2021
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Testing the genomic bottleneck hypothesis in hebbian meta-learning
Palm, R. B., Najarro, E. & Risi, S · 2021
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Meta learning backpropagation and improving it
Kirsch, L. & Schmidhuber, J · 2021
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Linear transformers are secretly fast weight programmers
Schlag, I., Irie, K. & Schmidhuber, J · 2021
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Evolving interpretable plasticity for spiking networks
Jordan, J., Schmidt, M., Senn, W. & Petrovici, M. A · 2021
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Training learned optimizers with randomly initialized learned optimizers
Metz, L., Freeman, C. D., Maheswaranathan, N. & Sohl-Dickstein, J · 2021
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Shallow unsupervised models best predict neural responses in mouse visual cortex
Nayebi, A. et al · 2021
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Qualitative similarities and differences in visual object representations between brains and deep networks
Jacob, G., Pramod, R., Katti, H. & Arun, S · 2021
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C. & Chen, M · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C. et al · 2022
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Learning robust perceptive locomotion for quadrupedal robots in the wild
Miki, T. et al · 2022
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Deep whole-body control: learning a unified policy for manipulation and locomotion
Fu, Z., Cheng, X. & Pathak, D · 2022
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Biological underpinnings for lifelong learning machines
Kudithipudi, D. et al · 2022
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Evidence for postnatal neurogenesis in the human amygdala
Roeder, S. S. et al · 2022
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Microglia regulation of synaptic plasticity and learning and memory
Cornell, J., Salinas, S., Huang, H.-Y. & Zhou, M · 2022
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Opportunities for neuromorphic computing algorithms and applications
Schuman, C. D. et al · 2022
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Mouse visual cortex as a limited resource system that self-learns an ecologically-general representation
Nayebi, A. et al · 2022
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Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators
Liu, Y. H., Smith, S., Mihalas, S., Shea-Brown, E. & Sümbül, U · 2022
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Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
Liu, Y. H., Ghosh, A., Richards, B. A., Shea-Brown, E. & Lajoie, G · 2022
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Meta-learning synaptic plasticity and memory addressing for continual familiarity detection
Tyulmankov, D., Yang, G. R. & Abbott, L · 2022
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Short-term plasticity neurons learning to learn and forget
Rodriguez, H. G., Guo, Q. & Moraitis, T · 2022
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What can transformers learn in-context? a case study of simple function classes
Garg, S., Tsipras, D., Liang, P. S. & Valiant, G · 2022
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General-purpose in-context learning by meta-learning transformers
Kirsch, L., Harrison, J., Sohl-Dickstein, J. & Metz, L · 2022
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What learning algorithm is in-context learning? investigations with linear models
Akyürek, E., Schuurmans, D., Andreas, J., Ma, T. & Zhou, D · 2022
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Transformers learn in-context by gradient descent
von Oswald, J. et al · 2022
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Eliminating meta optimization through self-referential meta learning
Kirsch, L. & Schmidhuber, J · 2022
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A modern self-referential weight matrix that learns to modify itself
Irie, K., Schlag, I., Csordás, R. & Schmidhuber, J · 2022
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Self-referential meta learning
Kirsch, L. & Schmidhuber, J · 2022
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Discovering evolution strategies via meta-black-box optimization
Lange, R. T. et al · 2022
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Introducing symmetries to black box meta reinforcement learning
Kirsch, L. et al · 2022
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Spike-based local synaptic plasticity: A survey of computational models and neuromorphic circuits
Khacef, L. et al · 2022
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Continual learning for real-world autonomous systems: Algorithms, challenges and frameworks
Shaheen, K., Hanif, M. A., Hasan, O. & Shafique, M · 2022
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A computational model of learning flexible navigation in a maze by layout-conforming replay of place cells
Gao, Y · 2022
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The neuroconnectionist research programme
Doerig, A. et al · 2022
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Palm-e: An embodied multimodal language model
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Spikegpt: Generative pre-trained language model with spiking neural networks
Zhu, R.-J., Zhao, Q. & Eshraghian, J. K · 2023
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Hebbian and gradient-based plasticity enables robust memory and rapid learning in rnns
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