Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks
暂无分享,去创建一个
[1] R. Romo,et al. Neuronal Population Coding of Parametric Working Memory , 2010, The Journal of Neuroscience.
[2] Wolfgang Maass,et al. A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control Task , 2010, The Journal of Neuroscience.
[3] W. Gerstner,et al. Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements , 2014, Neuron.
[4] Razvan V. Florian,et al. Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity , 2007, Neural Computation.
[5] Jochen J. Steil,et al. Solving the Distal Reward Problem with Rare Correlations , 2013, Neural Computation.
[6] Barak A. Pearlmutter. Gradient calculations for dynamic recurrent neural networks: a survey , 1995, IEEE Trans. Neural Networks.
[7] David J. Freedman,et al. in Inferior Temporal and Prefrontal Cortex Dynamic Population Coding of Category Information , 2015 .
[8] György Buzsáki,et al. Micro-, Meso- and Macro-Dynamics of the Brain , 2016, Research and Perspectives in Neurosciences.
[9] Matthew T. Kaufman,et al. A neural network that finds a naturalistic solution for the production of muscle activity , 2015, Nature Neuroscience.
[10] Thomas Miconi,et al. Training recurrent neural networks with sparse, delayed rewards for flexible decision tasks , 2015, 1507.08973.
[11] Xiao-Jing Wang,et al. The importance of mixed selectivity in complex cognitive tasks , 2013, Nature.
[12] Jan Peters,et al. Reinforcement learning in robotics: A survey , 2013, Int. J. Robotics Res..
[13] M. Stokes. ‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework , 2015, Trends in Cognitive Sciences.
[14] Stanislas Dehaene,et al. Decoding the Dynamics of Conscious Perception: The Temporal Generalization Method , 2016 .
[15] Paul Miller,et al. Heterogenous Population Coding of a Short-Term Memory and Decision Task , 2010, The Journal of Neuroscience.
[16] W. Newsome,et al. Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.
[17] L. F. Abbott,et al. Generating Coherent Patterns of Activity from Chaotic Neural Networks , 2009, Neuron.
[18] Anca Velisar,et al. Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model , 2015, Computer methods in biomechanics and biomedical engineering.
[19] Matthew T. Kaufman,et al. Neural population dynamics during reaching , 2012, Nature.
[20] N. Sigala,et al. Dynamic Coding for Cognitive Control in Prefrontal Cortex , 2013, Neuron.
[21] L. Abbott,et al. From fixed points to chaos: Three models of delayed discrimination , 2013, Progress in Neurobiology.
[22] H. Seung,et al. Model of birdsong learning based on gradient estimation by dynamic perturbation of neural conductances. , 2007, Journal of neurophysiology.
[23] Wulfram Gerstner,et al. Code-specific policy gradient rules for spiking neurons , 2009, NIPS.
[24] D. Thelen. Adjustment of muscle mechanics model parameters to simulate dynamic contractions in older adults. , 2003, Journal of biomechanical engineering.
[25] Scott L. Delp,et al. A Model of the Upper Extremity for Simulating Musculoskeletal Surgery and Analyzing Neuromuscular Control , 2005, Annals of Biomedical Engineering.
[26] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[27] Wolfgang Maass,et al. Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning. , 2014, Cerebral cortex.
[28] Alex Graves,et al. Recurrent Models of Visual Attention , 2014, NIPS.
[29] Sommers,et al. Chaos in random neural networks. , 1988, Physical review letters.
[30] Herbert Jaeger,et al. The''echo state''approach to analysing and training recurrent neural networks , 2001 .
[31] W. Maass,et al. State-dependent computations: spatiotemporal processing in cortical networks , 2009, Nature Reviews Neuroscience.
[32] Guangyu R. Yang,et al. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework , 2016, PLoS Comput. Biol..
[33] Dean V. Buonomano,et al. ROBUST TIMING AND MOTOR PATTERNS BY TAMING CHAOS IN RECURRENT NEURAL NETWORKS , 2012, Nature Neuroscience.
[34] Matthew T. Kaufman,et al. A category-free neural population supports evolving demands during decision-making , 2014, Nature Neuroscience.
[35] Christopher D. Harvey,et al. Recurrent Network Models of Sequence Generation and Memory , 2016, Neuron.
[36] Francesca Mastrogiuseppe,et al. Intrinsically-generated fluctuating activity in excitatory-inhibitory networks , 2016, PLoS Comput. Biol..
[37] Stefan Schaal,et al. 2008 Special Issue: Reinforcement learning of motor skills with policy gradients , 2008 .
[38] E. Izhikevich. Solving the distal reward problem through linkage of STDP and dopamine signaling , 2007, BMC Neuroscience.
[39] Henry Markram,et al. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.
[40] Ila R Fiete,et al. Gradient learning in spiking neural networks by dynamic perturbation of conductances. , 2006, Physical review letters.
[41] Henning Sprekeler,et al. Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity , 2010, The Journal of Neuroscience.