Computational approaches to sensorimotor transformations

By Alexandre Pouget, Lawrence H. Snyder. 2000

390 citations recorded by the source corpus.

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In Cognitive Neuroscience

Citations in the corpus

Motor Task Variation Induces Structural Learning (2009, neutral): “Typically, learning the mapping from sensory inputs X to a motor command U has been conceptualized as a mapping U = P i wigiðXÞ, where gið,Þ are so-called basis functions and wi are the adjustable parameters of the mapping [2, 6, 15, 16].”
Adaptive Tracking Control for Robots with Unknown Kinematic and Dynamic Properties (2006, background): “The ability of sensing and responding to changes without an accurate knowledge of sensorimotor transformation (Pouget and Snyder 2000) gives us a high degree of flexibility in dealing with unforseen changes in the real world.”
Structure Learning in a Sensorimotor Association Task (2010, methodology): “More recent neural network models such as back-propagation [8,9] and basis function networks [10,11] are simply non-linear extensions of the originally proposed models in the sense that they implement a mapping from stimulus to motor response by adapting (synaptic) weights in networks with fixed top”
GRAIL: A Goal-Discovering Robotic Architecture for Intrinsically-Motivated Learning (2016, methodology): “This input is encoded with Gaussian radial basis functions [63] with centres on the equally distributed vertexes of a 4-D grid having five elements per dimension (the 5 units cover the range of one arm joint)”
Computational approaches to motor control. (2001, background): “Sensorimotor transformations can be organized in a general context of basis functions [28], which are flexible intermediate representations for generating arbitrary nonlinear coordinate transformations as well as for learning and working memory.”
Random feedback weights support learning in deep neural networks (2014, methodology): “More-plausible algorithms have been devised 9,10,13,14,22,26,27,28,29,30 , but these either fall far short of backprop’s speed or call for a lot of additional processing 10,11,12,13,22 .”
Fast Remapping of Sensory Stimuli onto Motor Actions on the Basis of Contextual Modulation (2004, background): “, 1997; McAdams and Maunsell, 1999; Treue and Martı́nezTrujillo, 1999), which are directly combined with spatial sensory information to produce a change in reference frame (Andersen et al., 1993; Pouget and Snyder, 2000; Salinas and Thier, 2000; Salinas and Abbott, 2001).”
Basis functions for object-centered representations. (2003, background): “…variability can be filtered out optimally in a basis called a gain field, a response pattern that has been function network by adding lateral and feedback con- reported throughout the parietal lobe and premotor cornections and tuning them appropriately (Deneve et al., tex (Pouget and Snyder, 2000).”
Computational Principles of Supervised Learning in the Cerebellum (2018, neutral): “…of nonlinear mapping functions by the network if the granule cell layer provided an appropriate basis set (see Section 2.2), because it is possible to approximate any nonlinear function with a linear combination of basis functions (Dean & Porrill 2011, Pouget & Snyder 2000, Spanne & Jorntell 2013).”
Vestibular system: the many facets of a multimodal sense. (2008, neutral): “All sensorimotor systems that require the encoding or decoding of spatial information must face the issue of reference frames (for reviews, see Cohen & Andersen 2002, Pouget & Snyder 2000).”
The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain (2010, methodology): “The router circuit in our model builds on previous computational models which have studied the role of contextual signals on transient sensory-motor mappings [30,33,121,122].”
A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection (2020, methodology): “…led theorists to propose gain fields for the implementation of such coordinate transformations (Andersen et al., 1993; Pouget and Sejnowski, 1997; Pouget and Snyder, 2000; Salinas and Abbott, 2001; Zipser and Andersen, 1988), but the predicted neural circuit connectivity has not been directly…”
Spatiotemporal architecture of cortical receptive fields and its impact on multisensory interactions (2009, neutral): “multisensory cortical domains (Salinas and Abbott 2001; Pouget and Snyder 2000; Deneve et al. 2007; Porter and Groh 2006).”