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[1] N. Qian,et al. Learning and adaptation in a recurrent model of V1 orientation selectivity. , 2003, Journal of neurophysiology.
[2] S. Nelson,et al. An emergent model of orientation selectivity in cat visual cortical simple cells , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[3] W. Ma,et al. Changing concepts of working memory , 2014, Nature Neuroscience.
[4] Ilya Sutskever,et al. Learning Recurrent Neural Networks with Hessian-Free Optimization , 2011, ICML.
[5] Haim Sompolinsky,et al. Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics , 2010, NIPS.
[6] J J Hopfield,et al. Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.
[7] G. La Camera,et al. Stimuli Reduce the Dimensionality of Cortical Activity , 2015, bioRxiv.
[8] S. Kastner,et al. Attention in the real world: toward understanding its neural basis , 2014, Trends in Cognitive Sciences.
[9] S. Luck,et al. Sudden Death and Gradual Decay in Visual Working Memory , 2009, Psychological science.
[10] Misha Tsodyks,et al. Cross-fixation interactions of orientations suggest that orientation decoding occurs in a high-level area of visual working memory , 2020 .
[11] Surya Ganguli,et al. A theory of multineuronal dimensionality, dynamics and measurement , 2017, bioRxiv.
[12] Misha Tsodyks,et al. Visual perception as retrospective Bayesian decoding from high- to low-level features , 2017, Proceedings of the National Academy of Sciences.
[13] Lewis-Sigler. Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics , 2011 .
[14] J. Kingdon,et al. The shape of space , 1995 .
[15] S. Luck,et al. Interactions between visual working memory representations , 2017, Attention, perception & psychophysics.
[16] P. Schiller,et al. Quantitative studies of single-cell properties in monkey striate cortex. II. Orientation specificity and ocular dominance. , 1976, Journal of neurophysiology.
[17] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[18] Rotational remapping between differently prioritized representations in visual working memory , 2021 .
[19] Daniel L. K. Yamins,et al. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition , 2014, PLoS Comput. Biol..
[20] Timothy F. Brady,et al. Hierarchical Encoding in Visual Working Memory , 2010, Psychological science.
[21] N. Cowan. The magical number 4 in short-term memory: A reconsideration of mental storage capacity , 2001, Behavioral and Brain Sciences.
[22] Matthew F. Panichello,et al. Shared mechanisms underlie the control of working memory and attention , 2021, Nature.
[23] Christopher J. Cueva,et al. Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks , 2019, ICLR.
[24] Kuang-Ching Wang,et al. The Design and Operation of CloudLab , 2019, USENIX Annual Technical Conference.
[25] W. Newsome,et al. Context-dependent computation by recurrent dynamics in prefrontal cortex , 2013, Nature.
[26] Alexandra Libby,et al. Rotational Dynamics Reduce Interference Between Sensory and Memory Representations , 2019, Nature Neuroscience.
[27] Nicolas Y. Masse,et al. Reevaluating the Role of Persistent Neural Activity in Short-Term Memory , 2020, Trends in Cognitive Sciences.
[28] Haim Sompolinsky,et al. Interactions between Intrinsic and Stimulus-Evoked Activity in Recurrent Neural Networks , 2009, 0912.3832.
[29] Jonathan I. Flombaum,et al. Why some colors appear more memorable than others: A model combining categories and particulars in color working memory. , 2015, Journal of experimental psychology. General.
[30] Misha Tsodyks,et al. Short-Term Facilitation may Stabilize Parametric Working Memory Trace , 2011, Front. Comput. Neurosci..
[31] John T. Serences,et al. Coexisting representations of sensory and mnemonic information in human visual cortex , 2019, Nature Neuroscience.
[32] J. DiCarlo,et al. Using goal-driven deep learning models to understand sensory cortex , 2016, Nature Neuroscience.
[33] Konrad P. Körding,et al. Structural inference affects depth perception in the context of potential occlusion , 2009, NIPS.
[34] Matthew T. Kaufman,et al. A neural network that finds a naturalistic solution for the production of muscle activity , 2015, Nature Neuroscience.
[35] D. M. Green,et al. Signal detection theory and psychophysics , 1966 .
[36] K. Zhang,et al. Representation of spatial orientation by the intrinsic dynamics of the head-direction cell ensemble: a theory , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[37] G. A. Miller. THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .
[38] Surya Ganguli,et al. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks , 2013, ICLR.
[39] Ranulfo Romo,et al. Flexible Control of Mutual Inhibition: A Neural Model of Two-Interval Discrimination , 2005, Science.
[40] P. Goldman-Rakic,et al. Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. , 2000, Cerebral cortex.
[41] J. Movshon,et al. A new perceptual illusion reveals mechanisms of sensory decoding , 2007, Nature.
[42] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[43] P. Ekman,et al. Unmasking the face : a guide to recognizing emotions from facial clues , 1975 .