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Why neurons mix: high dimensionality for higher cognition

著者: Stefano Fusi, Earl K. Miller, Mattia Rigotti - Current Opinion in Neurobiology 2016 被引用: 855

Generating CoherentPatterns of Activity…Generating Coherent Patterns of Activity from Chaotic Neural NetworksInternal Representationof Task Rules by…Internal Representation of Task Rules by Recurrent Dynamics: The Importance of the Diversity of Neural ResponsesNeural populationdynamics during reachingNeural population dynamics during reachingComplexity withoutchaos: Plasticity withi…Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor controlThe importance of mixedselectivity in complex…The importance of mixed selectivity in complex cognitive tasksThe Sparseness of MixedSelectivity Neurons…The Sparseness of Mixed Selectivity Neurons Controls the Generalization–Discrimination Trade-OffContext-dependentcomputation by recurren…Context-dependent computation by recurrent dynamics in prefrontal cortexDynamic Coding forCognitive Control in…Dynamic Coding for Cognitive Control in Prefrontal CortexRobust timing and motorpatterns by taming chao…Robust timing and motor patterns by taming chaos in recurrent neural networksA category-free neuralpopulation supports…A category-free neural population supports evolving demands during decision-makingSparseness and Expansionin Sensory…Sparseness and Expansion in Sensory RepresentationsDeep learningDeep learningDifferential Encoding ofTime by Prefrontal and…Differential Encoding of Time by Prefrontal and Striatal Network DynamicsMixed selectivity morphspopulation codes in…Mixed selectivity morphs population codes in prefrontal cortexPerceptual DecisionMaking in Rodents…Perceptual Decision Making in Rodents, Monkeys, and HumansLinking Connectivity,Dynamics, and…Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural NetworksRecurrent neuralnetworks learn robust…Recurrent neural networks learn robust representations by dynamically balancing compression and expansionA Flexible Model ofWorking MemoryA Flexible Model of Working MemoryComplementary roles ofdimensionality and…Complementary roles of dimensionality and population structure in neural computationsMixed Selectivity inMacaque Medial Parietal…Mixed Selectivity in Macaque Medial Parietal Cortex during Eye-Hand ReachingValue and choice asseparable and stable…Value and choice as separable and stable representations in orbitofrontal cortexOrthogonalrepresentations for…Orthogonal representations for robust context-dependent task performance in brains and neural networksSemi-orthogonalsubspaces for value…Semi-orthogonal subspaces for value mediate a binding and generalization trade-offTask structure tailorsthe geometry of neural…Task structure tailors the geometry of neural representations in human lateral prefrontal cortexWhy neurons mix: highdimensionality for…Why neurons mix: high dimensionality for higher cognition過去の参考文献中心の論文この論文を引用する論文古い新しい

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