Trends in Neurosciences
Viewing and doing: similar cortical mechanisms for perceptual and motor learning
Section snippets
Learning-related changes in tuning values
The description of neuronal activity is commonly reduced from an equation specifying the full tuning curve (Figure 1) to a single ‘tuning value’, which commonly corresponds to the greatest discharge rate and is referred to as the ‘preferred’ or ‘best’ value. For example, A1 neurons have a best frequency (BF) for responding to tones, V1 cells have a preferred orientation (PO) for responding to lines and bars, and M1 cells have a preferred direction (PD) for reaching movements. This compact
Which neurons change?
Another aspect of learning involves the selection of a subpopulation of neurons involved in a change in tuning properties. In V1, observed shifts in PO after adaptation to one orientation occur only in cells with nearby POs. Moreover, the larger the difference between the PO of a cell and the training orientation, the smaller the shift (Figure 2a) [25]. In the auditory system, the situation is similar: maximal shifts in BF were observed for cells with BFs close to the training BF (but not for
Shape of the tuning curve and implications for improved coding
Learning in sensory systems might affect the shape of tuning curves. For example, the slope of the curve might change at a particular point along the curve, even without a significant change in the tuning value (the center or peak of the curve) or the amplitude of the peak. This possibility, which is illustrated in Figure 1b, has several implications for neural coding. In one view, neuronal tuning curves encode the value of a stimulus by signifying their preferred value and the population
Contextual specificity and complexity of responses
Although many neurons in sensory areas are tuned to the types of low-level stimulus features discussed so far, they can also be tuned to complex interactions of low-level features that cannot be predicted from their linear combinations, including specific visual objects [43] and natural, complex acoustic signals 44, 45. In the motor system, an example of this phenomenon comes from studies of bimanual arm movements. The responses of M1 neurons cannot be explained as a linear combination of their
Concluding remarks
William of Ockham (c. 1285–1349), the medieval philosopher famous for formulating the principle of parsimony (‘Ockham's razor’), would have wanted a unified account of perceptual and motor learning. The brain must solve problems within the constraints of its hardware: neurons and plastic synapses. It should not be surprising, therefore, that the solutions it has found for perceptual and motor learning resemble each other at Marr's level of implementation. The similarities discussed here for the
Acknowledgements
We thank Reza Shadmehr and Paul Cisek for their comments on an earlier version of this paper. This work was supported in part by a Center for Excellence grant (8006/00) administered by the Israel Science Foundation (ISF), by the Bundesministerium für Bildung und Forschung–Deutsch-Israelische Projektkooperation(BMBF–DIP), by grant 2001073 administrated by the Binational Science Foundation (BSF), and by a special contribution of the Golden Charitable Trust. R.P. was supported by a Constantiner
References (65)
The neural basis of perceptual learning
Neuron
(2001)Adaptation-induced plasticity of orientation tuning in adult visual cortex
Neuron
(2000)Pairing-induced changes of orientation maps in cat visual cortex
Neuron
(2001)Physiological memory in primary auditory cortex: characteristics and mechanisms
Neurobiol. Learn. Mem.
(1998)Plasticity and corticofugal modulation for hearing in adult animals
Neuron
(2002)Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field
Neuron
(2001)Neuronal correlates of kinematics-to-dynamics transformation in the supplementary motor area
Neuron
(2002)Neural population codes
Curr. Opin. Neurobiol.
(2003)Internal models for motor control and trajectory planning
Curr. Opin. Neurobiol.
(1999)Prediction precedes control in motor learning
Curr. Biol.
(2003)
Associative and perceptual learning and the concept of memory systems
Cognit. Brain Res.
Multiple paired forward and inverse models for motor control
Neural Netw.
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Task difficulty and the specificity of perceptual learning
Nature
Computational approaches to sensorimotor transformations
Nat. Neurosci.
Direct visuomotor transformations for reaching
Nature
Constructive incremental learning from only local information
Neural Comput.
Quantifying generalization from trial-by-trial behavior of adaptive systems that learn with basis functions: theory and experiments in human motor control
J. Neurosci.
Spatial generalization of learning in smooth pursuit eye movements: implications for the coordinate frame and sites of learning
J. Neurosci.
Learning of action through adaptive combination of motor primitives
Nature
Cerebellar Purkinje cell simple spike discharge encodes movement velocity in primates during visuomotor arm tracking
J. Neurosci.
Plasticity of orientation preference maps in the visual cortex of adult cats
Proc. Natl. Acad. Sci. U. S. A.
Evolution of directional preferences in the supplementary eye field during acquisition of conditional oculomotor associations
J. Neurosci.
Changes in motor cortical activity during visuomotor adaptation
Exp. Brain Res.
Direct cortical control of 3D neuroprosthetic devices
Science
Learning to control a brain–machine interface for reaching and grasping by primates
PLoS Biol.
Preparatory activity in motor cortex reflects learning of local visuomotor skills
Nat. Neurosci.
Overlap of internal models in motor cortex for mechanical loads during reaching
Nature
Rapid plasticity of human cortical movement representation induced by practice
J. Neurophysiol.
Dynamics of neuronal sensitivity in visual cortex and local feature discrimination
Nat. Neurosci.
Plasticity of the cochleotopic (frequency) map in specialized and nonspecialized auditory cortices
Proc. Natl. Acad. Sci. U. S. A.
Learning of visuomotor transformations for vectorial planning of reaching trajectories
J. Neurosci.
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