A generative spiking neural-network model of goal-directed behaviour and one-step planning
暂无分享,去创建一个
Andrea Brovelli | Gianluca Baldassarre | Emilio Cartoni | Ruggero Basanisi | G. Baldassarre | A. Brovelli | Emilio Cartoni | Ruggero Basanisi
[1] Richard S. Sutton,et al. Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming , 1990, ML.
[2] A. Pouget,et al. Probabilistic brains: knowns and unknowns , 2013, Nature Neuroscience.
[3] J. Rothwell,et al. A fronto–striato–subthalamic–pallidal network for goal-directed and habitual inhibition , 2015, Nature Reviews Neuroscience.
[4] B. Balleine,et al. Motivational control of goal-directed action , 1994 .
[5] M. Botvinick,et al. Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective , 2009, Cognition.
[6] Jan Peters,et al. Recurrent Spiking Networks Solve Planning Tasks , 2016, Scientific Reports.
[7] B. Love,et al. The myth of computational level theory and the vacuity of rational analysis , 2011, Behavioral and Brain Sciences.
[8] Wei Ji Ma,et al. Efficient Probabilistic Inference in Generic Neural Networks Trained with Non-Probabilistic Feedback , 2018 .
[9] Geoffrey E. Hinton,et al. The Helmholtz Machine , 1995, Neural Computation.
[10] E. Rolls,et al. Computational analysis of the role of the hippocampus in memory , 1994, Hippocampus.
[11] Tiejun Huang,et al. Probabilistic Inference of Binary Markov Random Fields in Spiking Neural Networks through Mean-field Approximation , 2020, Neural Networks.
[12] G. Baldassarre,et al. Goal-Directed Behavior and Instrumental Devaluation: A Neural System-Level Computational Model , 2016, Front. Behav. Neurosci..
[13] Juan M. Corchado,et al. A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking , 2017, Sensors.
[14] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[15] P. Dayan,et al. Cortical substrates for exploratory decisions in humans , 2006, Nature.
[16] Gianluca Baldassarre,et al. Forward and Bidirectional Planning Based on Reinforcement Learning and Neural Networks in a Simulated Robot , 2003, ABiALS.
[17] Wolfgang Maass,et al. Networks of Spiking Neurons: The Third Generation of Neural Network Models , 1996, Electron. Colloquium Comput. Complex..
[18] Gianluca Baldassarre,et al. An Embodied Agent Learning Affordances With Intrinsic Motivations and Solving Extrinsic Tasks With Attention and One-Step Planning , 2019, Front. Neurorobot..
[19] Marc Toussaint,et al. Probabilistic inference for solving discrete and continuous state Markov Decision Processes , 2006, ICML.
[20] W. Gerstner,et al. Spike-Timing-Dependent Plasticity: A Comprehensive Overview , 2012, Front. Syn. Neurosci..
[21] Sophie Denève,et al. Bayesian Inference with Spiking Neurons , 2004, Encyclopedia of Computational Neuroscience.
[22] Alan MacLennan,et al. The artificial life route to artificial intelligence: Building embodied, situated agents , 1996 .
[23] Ben R. Newell,et al. Unpacking the Exploration–Exploitation Tradeoff: A Synthesis of Human and Animal Literatures , 2015 .
[24] B. Balleine,et al. The role of the dorsomedial striatum in instrumental conditioning , 2005, The European journal of neuroscience.
[25] Wofgang Maas,et al. Networks of spiking neurons: the third generation of neural network models , 1997 .
[26] Rajesh P. N. Rao,et al. Probabilistic Models of the Brain: Perception and Neural Function , 2002 .
[27] Jan Peters,et al. Deep spiking networks for model-based planning in humanoids , 2016, 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids).
[28] Gianluca Baldassarre,et al. Planning with neural networks and reinforcement learning , 2001 .
[29] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[30] Rajesh P. N. Rao,et al. Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks , 2014, NIPS.
[31] M. Newman. Complex Systems: A Survey , 2011, 1112.1440.
[32] Andrea Brovelli,et al. Dynamic reconfiguration of visuomotor-related functional connectivity networks. , 2016, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[33] Joseph T. McGuire,et al. A Neural Signature of Hierarchical Reinforcement Learning , 2011, Neuron.
[34] B. Balleine,et al. Goal-directed instrumental action: contingency and incentive learning and their cortical substrates , 1998, Neuropharmacology.
[35] Andrea Brovelli,et al. Understanding the neural computations of arbitrary visuomotor learning through fMRI and associative learning theory. , 2008, Cerebral cortex.
[36] M. V. Velzen,et al. Self-organizing maps , 2007 .
[37] D. Feldman. The Spike-Timing Dependence of Plasticity , 2012, Neuron.
[38] Risto Miikkulainen,et al. Computational Maps in the Visual Cortex , 2005 .
[39] P. Dayan,et al. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control , 2005, Nature Neuroscience.
[40] Máté Lengyel,et al. Goal-Directed Decision Making with Spiking Neurons , 2016, The Journal of Neuroscience.
[41] Marc Toussaint,et al. Learned graphical models for probabilistic planning provide a new class of movement primitives , 2013, Front. Comput. Neurosci..
[42] P. Dayan,et al. Goals and Habits in the Brain , 2013, Neuron.
[43] Nikolaus Kriegeskorte,et al. Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .
[44] K. Gurney,et al. The nucleus accumbens as a nexus between values and goals in goal-directed behavior: a review and a new hypothesis , 2013, Front. Behav. Neurosci..
[45] G. Rizzolatti,et al. The Organization of the Frontal Motor Cortex. , 2000, News in physiological sciences : an international journal of physiology produced jointly by the International Union of Physiological Sciences and the American Physiological Society.
[46] T. Ziemke,et al. Theories and computational models of affordance and mirror systems: An integrative review , 2013, Neuroscience & Biobehavioral Reviews.
[47] Vicenç Gómez,et al. Optimal control as a graphical model inference problem , 2009, Machine Learning.
[48] Wolfgang Maass,et al. Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity , 2013, PLoS Comput. Biol..
[49] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 2005, IEEE Transactions on Neural Networks.
[50] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[51] Y. Dan,et al. Spike Timing-Dependent Plasticity of Neural Circuits , 2004, Neuron.
[52] Wolfgang Maass,et al. Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons , 2011, PLoS Comput. Biol..
[53] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[54] Wolfgang Maass,et al. On the Computational Power of Winner-Take-All , 2000, Neural Computation.
[55] Andrea Brovelli,et al. Differential roles of caudate nucleus and putamen during instrumental learning , 2011, NeuroImage.
[56] Karol Gregor,et al. Temporal Difference Variational Auto-Encoder , 2018, ICLR.
[57] Charles Kemp,et al. Bayesian models of cognition , 2008 .
[58] Mehdi Khamassi,et al. Modeling choice and reaction time during arbitrary visuomotor learning through the coordination of adaptive working memory and reinforcement learning , 2015, Front. Behav. Neurosci..
[59] M. Botvinick,et al. Planning as inference , 2012, Trends in Cognitive Sciences.
[60] Raymond Klein,et al. Inhibition of return , 2000, Trends in Cognitive Sciences.
[61] Anil K. Seth,et al. Learning action-oriented models through active inference , 2019, bioRxiv.
[62] Kenji Doya,et al. Hierarchical control of goal-directed action in the cortical–basal ganglia network , 2015, Current Opinion in Behavioral Sciences.
[63] Wulfram Gerstner,et al. Predicting spike timing of neocortical pyramidal neurons by simple threshold models , 2006, Journal of Computational Neuroscience.
[64] Alec Solway,et al. Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates. , 2012, Psychological review.
[65] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[66] Michael J. Frank,et al. Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia , 2006, Neural Computation.
[67] Daniel Chicharro,et al. Characterization of Cortical Networks and Corticocortical Functional Connectivity Mediating Arbitrary Visuomotor Mapping , 2015, The Journal of Neuroscience.
[68] Michael A. Arbib,et al. The super-learning hypothesis: Integrating learning processes across cortex, cerebellum and basal ganglia , 2019, Neuroscience & Biobehavioral Reviews.
[69] Andrea Brovelli,et al. A spiking neural-network model of goal-directed behaviour , 2019, bioRxiv.
[70] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[71] J. Gibson. The Ecological Approach to Visual Perception , 1979 .
[72] P. Moral. Nonlinear filtering : Interacting particle resolution , 1997 .
[73] Francesco Mannella,et al. General differential Hebbian learning: Capturing temporal relations between events in neural networks and the brain , 2018, PLoS Comput. Biol..
[74] Stefan Habenschuss,et al. Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral Inhibition , 2015, PloS one.
[75] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[76] R. Passingham,et al. The Neurobiology of the Prefrontal Cortex: Anatomy, Evolution, and the Origin of Insight , 2012 .
[77] Chris Eliasmith,et al. A Spiking Neural Bayesian Model of Life Span Inference , 2017, CogSci.
[78] Karl J. Friston. The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.
[79] Rajesh P. N. Rao,et al. Bayesian brain : probabilistic approaches to neural coding , 2006 .
[80] David Kappel,et al. STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning , 2014, PLoS Comput. Biol..