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Learning: Neuronal dynamics and perceptual learning
LEARNING
GILBERT D. GILBERT CHARLES D. CHARLEs
LEARNING
Neuronal dynamics and perceptual learning Perceptual learning is accompanied by changes in the properties of individual neurons and in the functional cortical architecture; these are observed in a number of cortical areas, over short and long time scales. Our ability to discriminate virtually any attribute of anything that we sense is subject to learning. Whether dealing with characteristics such as spatial position, direction of movement, pitch, or tactile textures, one can make discriminations of progressively smaller differences by repeatedly performing 'discrimination tasks'. The characteristics of such learning processes suggests that they involve early stages along sensory pathways, in some instances primary sensory cortex, making their neural mechanisms accessible to experimental study. A critical issue in such studies, at both the perceptual and the neuronal level, is the time course over which the learning takes place - learning has been observed to occur over a wide range of time scales, ranging from minutes to months. Various approaches have been used to document the changes in neural specificity and cortical functional architecture that accompany learning, and the time course of the chosen change determines which approach is used. Studies of the neuronal plasticity of adult sensory cortex focused initially not on learning but on the consequences for the cortex of making lesions at early stages in sensory pathways. These experiments were based on the principle that the sensory cortex is topographically mapped: there is a representation of the body surface in the somatosensory cortex, of the cochlea in the auditory cortex - leading to a map of sound frequencies across the cortical surface - and of the retha in the visual cortex. If one amputates a digit - or cuts the peripheral nerve innervating it - the part of the somatosensory cortex that represents that digit will initially be silenced. Remarkably, however, over a period of months, the topography of this region of cortex is reorganized so that it develops a representation of the digits adjacent to the operated one. Similar observations have been made in other sensory systems: retinal lesions lead to a shrinkage in the representation of the lesioned part of the retina and an expansion in the representation of the parts of the retina surrounding the lesion. Although these effects take place over a period of a few months, there are also immediate cortical effects that can be observed over a much shorter time scale. Learning can be thought of, in a way, as a reciprocal phenomenon to peripheral lesions. Rather than receiving a reduced input from a restricted part of the sensory surface, there is an enrichment of input. What happens, then, if an animal is trained to discriminate between differences in the frequency of a tactile vibration stimulus
on one digit? The principal finding is that the cortical area representing the 'trained' digit is increased, with a 1.5- to 3-fold increase in area compared to the other digits. The representation of the trained digit is more complex than that of the other digits, in that it is split up into a number of discrete zones, as opposed to the single, continuous zone that is seen normally. There is also an increase in the sizes of cortical receptive fields for the trained digit, increasing the area of the digit from which each cortical neuron receives input, as well as increased receptive field sizes in the cortical area immediately adjoining the reorganized region [1]. These changes are much larger than those observed after passive stimulation, which do not involve a discrimination task. Similar observations were made concerning the tonotopic organization of auditory cortex. Monkeys were trained to discriminate small differences in the frequency of tones. In these animals, there was roughly four-fold improvement in their threshold discriminable frequency difference after training. Physiologically, although both the cortical representation of the trained frequency and the tuning characteristics of the cells were changed, only the recruitment of cortical territory in the vicinity of the trained frequency was significantly correlated with the behavioral improvement [2]. The most notable change, then, is an increase in the size of the cortical representation of the trained frequency. In these examples, cortical reorganization was observed after a period of training lasting from one to several months. In the visual system, learning effects have also been documented to occur after long periods of training, generally covering days or weeks, but in addition, learning has been shown to occur within a single session of tens or hundreds of trials. There have been observations of neuronal plasticity in adult visual cortex over a wide range of time scales, ranging from minutes to months. As with the other systems, one can also induce plasticity in the visual system with peripheral lesions. Focal retinal lesions, induced with lasers, lead over a period of months to a shrinkage in the cortical representation of the lesioned part of the retina and an increase in the representation of the surrounding retina. The neural substrate of the reorganization probably lies in a plexus of long-range horizontal connections that run parallel to the cortical surface, and these connections may undergo alteration as a result of the lesions, by
© Current Biology 1994, Vol 4 No 7
627
628
Current Biology 1994, Vol 4 No 7 collateral sprouting and synaptogenesis [3]. In these lesioned animals, striking changes in receptive field size can be seen even within minutes of making the lesions [4,5]. In fact, even without making retinal lesions, changes in receptive field size can be induced simply by presenting a patterned visual stimulus. Mimicking a retinal lesion with an 'artificial scotoma', a large field of moving lines or blinking dots surrounding an occluded area that is positioned to include a cell's receptive field, causes the receptive field to expand to fill the scotoma [6]. In human psychophysical experiments, viewing an artificial scotoma for periods of as little as a second can cause distortions in spatial perception near the boundary of the scotoma, and within 10-15 seconds this leads to a disappearance of the scotoma, a phenomenon known as perceptual fill-in [7-9]. Extending the findings of visual cortical plasticity that result from focal deprivation into the field of perceptual learning has just begun. Traditional notions of learning have assumed that learning is an attribute only of high order 'association' cortical areas. But current evidence suggests that, on the contrary, neural correlates of learning effects are ubiquitous in cortex, leading one to expect to find the neural substrate for learning at early stages along the visual pathway, as well as in areas that are traditionally associated with the recognition of complex forms. The literature on perceptual learning covers well over 100 years, but it is only within the past few years that the specificity of this learning, indicative of the involvement of early stages in the visual pathway, has been documented. Learning is restricted to the trained location in the visual field, and it does not transfer to other locations, suggesting the involvement of lower level visual areas where receptive fields are small and retinotopic order more rigid. Other evidence for specificity of learning is found in a lack of transfer from one eye to another, from one stimulus orientation to another, and so on. In concordance with the different time scales observed in adult cortical plasticity, there are fast learning effects, occurring within a session [10], as well as slower effects, taking place over many sessions [9,11]. An example of a cortical change in animals trained to make a visual discrimination has been shown in the domain of motion perception [12]. Monkeys were trained to discriminate the direction of motion of an array of dots moving in a background of randomly moving dots. Within sessions, the animals showed improvement in their ability to pick out the appropriate direction of movement of the coherently moving dots within the first few hundred trials. The area of cortex thought to specialize in analysis of motion is an area of the parietal cortex known as MT. Within this area, the directional specificity of neurons was found to improve along with the improvement in the animals' perceptual performance. Evidence supporting the involvement of area MT was derived from the ability of the learning effects to transfer from the conditioned part of a cell's receptive field to unconditioned parts, indicating that the
effect is likely to involve cortical regions with receptive fields as least as large as those found in MT, and not to involve earlier parts of the visual pathway, in which the fields are smaller. It now remains to be seen what form of cortical changes accompany longer term, more sustained improvements in visual discrimination. In attempting to establish a relationship between neuronal activity and perceptual performance, it is important to keep in mind the criteria that are to be used. Effects may be manifest as a recruitment of the cortical territory used to perform the task, as is implied by the Merzenich experiments on tactile and auditory responses [1,2] and those using artificial scotomata [6]. Another possible change is a sharpening in the tuning characteristics of individual neurons, or an improvement in the signal to noise ratio of neurons, as indicated by experiments in both the somatosensory and visual systems. These changes are distributed over a large number of neurons, and the perceptual effect of the changes is sometimes modeled as an average over the populations, using a 'vector' model. But an alternative possibility is that decisions are based only on the largest signal, and thus on a very small number of neurons, as proposed by the 'winner-take-all' model [13]. Whatever alterations may be taking place in neuronal specificity or cortical architecture within one cortical area, we may ultimately find that learning of simple attributes may be correlated with functional changes in a number of cortical areas, extending all the way to highorder areas in a sensory pathway. Inferotemporal cortex, for example, is thought to be involved in the recognition of complex objects, and cells in this area can be made to develop selectivity for an object by training the animal to recognize it [14]. By the same token, however, these cells can be induced to develop selectivity for very simple attributes, such as orientation, by training animals in an orientation-discrimination task [15]. As the study of the neural basis of perceptual learning progresses, therefore, we will see a continuing interplay between local and distributed representations of the learned behavior, and between short-term and long-term time scales of the functional changes. References 1. Recanzone GH, Merzenich MM, Jenkins WM, Grajski KA, Dinse
HR: Topographic reorganization of the hand representation in cortical area 3b of owl monkeys trained ina frequency-discrimination task. JNeurosci 1992, 67:1031-1056. 2. Recanzone GH, Schreiner CE, Merzenich MM: Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. I Neurosci 1993, 13:87-103. 3. Darian-Smith C, Gilbert CD: Axonal sprouting accompanies functional reorganization in adult cat striate cortex. Nature 1994, 368:737-740. 4.
Gilbert CD, Wiesel TN: Receptive field dynamics in adult primary visual cortex. Nature 1992, 356:150-152.
5. Chino YM, Kaas JH, Smith EL, Langston AL, Cheng H: Rapid reorganization of cortical maps in adult cats following restricted 6.
deafferentiation in retina. Vision Res 1992, 32:789-796. Pettet MW, Gilbert CD: Dynamic changes in receptive field size in cat primary visual cortex. Proc Natl Acad Sci U 5SA 1992,
89:8366-8370.
DISPATCH Ramachandran VS, Gregory TL: Perceptual filling-in of artificially induced scotomas in human vision. Nature 1991, 350: 699-702. 8. Paradiso MA, Nakayama K: Brightness perception and filling-in. Vision Res 1991, 31:1221-1236. 9. Kapadia MK, Gilbert CD, Westheimer G: A quantitative measure for short-term cortical plasticity in human vision. J Neurosci 1994, 14:451-457. 10. Poggio T, Fahle M, Edelman S: Fast perceptual learning in visual hyperaccuity. Science 1992, 256:1018-1021. 11. Karni A, Sagi D: Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. Proc Natl Acad Sci U S A 1992, 88:4966-4970. 12. Zohary E, Celebrini S, Britten KH, Newsome W: Neuronal plasticity 7.
that underlies improvement in perceptual performance. Science 1994, 263:1288-1292. 13. Salzman CD, Newsome WT: Neural mechanisms for forming a perceptual decision. Science 1994, 264:231-237. 14. Kobatake E, Tanaka K, Tamori Y: Long-term learning changes the stimulus selectivity of cells in the inferotemporal cortex of adult monkeys. Neurosci Res Supp 1992, 17:S237. 15. Vogels R, Orban GA: Training affects task-related properties of inferotemporal neurons. Abst Soc Neurosci 1991, 17:1283.
Charles D. Gilbert, The Rockefeller University, 1230 York Avenue, New York, New York 10021-6399, USA.
THE AUGUST 1994 ISSUE (VOL. 4, NO. 4) OF CURRENT OPINION IN NEUROBIOLOGY will include the following reviews, edited by A.J. Hudspeth and Michael Stryker, on Sensory Systems: Regeneration of hair cells in the vestibulocochlear system of birds and mammals by Douglas A. Cotanche and Kenneth H. Lee Neural substrates of sound localization by Michael S. Brainard Noise, neural codes and cortical organization by Michael N. Shadlen and William T. Newsome Survival factors in retinal degeneration by Roy H. Steinberg Fast active processes in the cochlea by Jonathan F. Ashmore and Paul J. Kolston Differences in transduction between rod and cone photoreceptors: an exploration of the role of calcium homeostasis by James L. Miller, Arturo Picones and Juan I. Korenbrot Sensory gating mechanisms of the thalamus by David A. McCormick and Thierry Bal The pathophysiology of chronic pain - increased sensitivity to low threshold AP-fibre inputs by Clifford J. Woolf and Tim P. Doubell Linearity of synaptic interactions in the assembly of receptive fields in cat visual cortex by David Ferster Calcium signalling in hair cells: multiple roles in a compact cell by David Lenzi and William M. Roberts Cortical circuitry in a dish by Jurgen Bolz Termination of photoreceptor responses by James B. Hurley Plasticity in auditory cortical circuitry by Ehud Ahissar and Merav Ahissar Chemosensing and signal transduction in bacteria byJeffStock, Michael Surette and Peter Park Development, critical period plasticity, and adult reorganization of mammalian somatosensory systems by Dennis DM O'Leary, Naomi L. Ruff and Richard H. Dyck Compartments in the olfactory periphery by Linda Buck
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GILBERT D. GILBERT CHARLES D. CHARLEs
LEARNING
Neuronal dynamics and perceptual learning Perceptual learning is accompanied by changes in the properties of individual neurons and in the functional cortical architecture; these are observed in a number of cortical areas, over short and long time scales. Our ability to discriminate virtually any attribute of anything that we sense is subject to learning. Whether dealing with characteristics such as spatial position, direction of movement, pitch, or tactile textures, one can make discriminations of progressively smaller differences by repeatedly performing 'discrimination tasks'. The characteristics of such learning processes suggests that they involve early stages along sensory pathways, in some instances primary sensory cortex, making their neural mechanisms accessible to experimental study. A critical issue in such studies, at both the perceptual and the neuronal level, is the time course over which the learning takes place - learning has been observed to occur over a wide range of time scales, ranging from minutes to months. Various approaches have been used to document the changes in neural specificity and cortical functional architecture that accompany learning, and the time course of the chosen change determines which approach is used. Studies of the neuronal plasticity of adult sensory cortex focused initially not on learning but on the consequences for the cortex of making lesions at early stages in sensory pathways. These experiments were based on the principle that the sensory cortex is topographically mapped: there is a representation of the body surface in the somatosensory cortex, of the cochlea in the auditory cortex - leading to a map of sound frequencies across the cortical surface - and of the retha in the visual cortex. If one amputates a digit - or cuts the peripheral nerve innervating it - the part of the somatosensory cortex that represents that digit will initially be silenced. Remarkably, however, over a period of months, the topography of this region of cortex is reorganized so that it develops a representation of the digits adjacent to the operated one. Similar observations have been made in other sensory systems: retinal lesions lead to a shrinkage in the representation of the lesioned part of the retina and an expansion in the representation of the parts of the retina surrounding the lesion. Although these effects take place over a period of a few months, there are also immediate cortical effects that can be observed over a much shorter time scale. Learning can be thought of, in a way, as a reciprocal phenomenon to peripheral lesions. Rather than receiving a reduced input from a restricted part of the sensory surface, there is an enrichment of input. What happens, then, if an animal is trained to discriminate between differences in the frequency of a tactile vibration stimulus
on one digit? The principal finding is that the cortical area representing the 'trained' digit is increased, with a 1.5- to 3-fold increase in area compared to the other digits. The representation of the trained digit is more complex than that of the other digits, in that it is split up into a number of discrete zones, as opposed to the single, continuous zone that is seen normally. There is also an increase in the sizes of cortical receptive fields for the trained digit, increasing the area of the digit from which each cortical neuron receives input, as well as increased receptive field sizes in the cortical area immediately adjoining the reorganized region [1]. These changes are much larger than those observed after passive stimulation, which do not involve a discrimination task. Similar observations were made concerning the tonotopic organization of auditory cortex. Monkeys were trained to discriminate small differences in the frequency of tones. In these animals, there was roughly four-fold improvement in their threshold discriminable frequency difference after training. Physiologically, although both the cortical representation of the trained frequency and the tuning characteristics of the cells were changed, only the recruitment of cortical territory in the vicinity of the trained frequency was significantly correlated with the behavioral improvement [2]. The most notable change, then, is an increase in the size of the cortical representation of the trained frequency. In these examples, cortical reorganization was observed after a period of training lasting from one to several months. In the visual system, learning effects have also been documented to occur after long periods of training, generally covering days or weeks, but in addition, learning has been shown to occur within a single session of tens or hundreds of trials. There have been observations of neuronal plasticity in adult visual cortex over a wide range of time scales, ranging from minutes to months. As with the other systems, one can also induce plasticity in the visual system with peripheral lesions. Focal retinal lesions, induced with lasers, lead over a period of months to a shrinkage in the cortical representation of the lesioned part of the retina and an increase in the representation of the surrounding retina. The neural substrate of the reorganization probably lies in a plexus of long-range horizontal connections that run parallel to the cortical surface, and these connections may undergo alteration as a result of the lesions, by
© Current Biology 1994, Vol 4 No 7
627
628
Current Biology 1994, Vol 4 No 7 collateral sprouting and synaptogenesis [3]. In these lesioned animals, striking changes in receptive field size can be seen even within minutes of making the lesions [4,5]. In fact, even without making retinal lesions, changes in receptive field size can be induced simply by presenting a patterned visual stimulus. Mimicking a retinal lesion with an 'artificial scotoma', a large field of moving lines or blinking dots surrounding an occluded area that is positioned to include a cell's receptive field, causes the receptive field to expand to fill the scotoma [6]. In human psychophysical experiments, viewing an artificial scotoma for periods of as little as a second can cause distortions in spatial perception near the boundary of the scotoma, and within 10-15 seconds this leads to a disappearance of the scotoma, a phenomenon known as perceptual fill-in [7-9]. Extending the findings of visual cortical plasticity that result from focal deprivation into the field of perceptual learning has just begun. Traditional notions of learning have assumed that learning is an attribute only of high order 'association' cortical areas. But current evidence suggests that, on the contrary, neural correlates of learning effects are ubiquitous in cortex, leading one to expect to find the neural substrate for learning at early stages along the visual pathway, as well as in areas that are traditionally associated with the recognition of complex forms. The literature on perceptual learning covers well over 100 years, but it is only within the past few years that the specificity of this learning, indicative of the involvement of early stages in the visual pathway, has been documented. Learning is restricted to the trained location in the visual field, and it does not transfer to other locations, suggesting the involvement of lower level visual areas where receptive fields are small and retinotopic order more rigid. Other evidence for specificity of learning is found in a lack of transfer from one eye to another, from one stimulus orientation to another, and so on. In concordance with the different time scales observed in adult cortical plasticity, there are fast learning effects, occurring within a session [10], as well as slower effects, taking place over many sessions [9,11]. An example of a cortical change in animals trained to make a visual discrimination has been shown in the domain of motion perception [12]. Monkeys were trained to discriminate the direction of motion of an array of dots moving in a background of randomly moving dots. Within sessions, the animals showed improvement in their ability to pick out the appropriate direction of movement of the coherently moving dots within the first few hundred trials. The area of cortex thought to specialize in analysis of motion is an area of the parietal cortex known as MT. Within this area, the directional specificity of neurons was found to improve along with the improvement in the animals' perceptual performance. Evidence supporting the involvement of area MT was derived from the ability of the learning effects to transfer from the conditioned part of a cell's receptive field to unconditioned parts, indicating that the
effect is likely to involve cortical regions with receptive fields as least as large as those found in MT, and not to involve earlier parts of the visual pathway, in which the fields are smaller. It now remains to be seen what form of cortical changes accompany longer term, more sustained improvements in visual discrimination. In attempting to establish a relationship between neuronal activity and perceptual performance, it is important to keep in mind the criteria that are to be used. Effects may be manifest as a recruitment of the cortical territory used to perform the task, as is implied by the Merzenich experiments on tactile and auditory responses [1,2] and those using artificial scotomata [6]. Another possible change is a sharpening in the tuning characteristics of individual neurons, or an improvement in the signal to noise ratio of neurons, as indicated by experiments in both the somatosensory and visual systems. These changes are distributed over a large number of neurons, and the perceptual effect of the changes is sometimes modeled as an average over the populations, using a 'vector' model. But an alternative possibility is that decisions are based only on the largest signal, and thus on a very small number of neurons, as proposed by the 'winner-take-all' model [13]. Whatever alterations may be taking place in neuronal specificity or cortical architecture within one cortical area, we may ultimately find that learning of simple attributes may be correlated with functional changes in a number of cortical areas, extending all the way to highorder areas in a sensory pathway. Inferotemporal cortex, for example, is thought to be involved in the recognition of complex objects, and cells in this area can be made to develop selectivity for an object by training the animal to recognize it [14]. By the same token, however, these cells can be induced to develop selectivity for very simple attributes, such as orientation, by training animals in an orientation-discrimination task [15]. As the study of the neural basis of perceptual learning progresses, therefore, we will see a continuing interplay between local and distributed representations of the learned behavior, and between short-term and long-term time scales of the functional changes. References 1. Recanzone GH, Merzenich MM, Jenkins WM, Grajski KA, Dinse
HR: Topographic reorganization of the hand representation in cortical area 3b of owl monkeys trained ina frequency-discrimination task. JNeurosci 1992, 67:1031-1056. 2. Recanzone GH, Schreiner CE, Merzenich MM: Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. I Neurosci 1993, 13:87-103. 3. Darian-Smith C, Gilbert CD: Axonal sprouting accompanies functional reorganization in adult cat striate cortex. Nature 1994, 368:737-740. 4.
Gilbert CD, Wiesel TN: Receptive field dynamics in adult primary visual cortex. Nature 1992, 356:150-152.
5. Chino YM, Kaas JH, Smith EL, Langston AL, Cheng H: Rapid reorganization of cortical maps in adult cats following restricted 6.
deafferentiation in retina. Vision Res 1992, 32:789-796. Pettet MW, Gilbert CD: Dynamic changes in receptive field size in cat primary visual cortex. Proc Natl Acad Sci U 5SA 1992,
89:8366-8370.
DISPATCH Ramachandran VS, Gregory TL: Perceptual filling-in of artificially induced scotomas in human vision. Nature 1991, 350: 699-702. 8. Paradiso MA, Nakayama K: Brightness perception and filling-in. Vision Res 1991, 31:1221-1236. 9. Kapadia MK, Gilbert CD, Westheimer G: A quantitative measure for short-term cortical plasticity in human vision. J Neurosci 1994, 14:451-457. 10. Poggio T, Fahle M, Edelman S: Fast perceptual learning in visual hyperaccuity. Science 1992, 256:1018-1021. 11. Karni A, Sagi D: Where practice makes perfect in texture discrimination: evidence for primary visual cortex plasticity. Proc Natl Acad Sci U S A 1992, 88:4966-4970. 12. Zohary E, Celebrini S, Britten KH, Newsome W: Neuronal plasticity 7.
that underlies improvement in perceptual performance. Science 1994, 263:1288-1292. 13. Salzman CD, Newsome WT: Neural mechanisms for forming a perceptual decision. Science 1994, 264:231-237. 14. Kobatake E, Tanaka K, Tamori Y: Long-term learning changes the stimulus selectivity of cells in the inferotemporal cortex of adult monkeys. Neurosci Res Supp 1992, 17:S237. 15. Vogels R, Orban GA: Training affects task-related properties of inferotemporal neurons. Abst Soc Neurosci 1991, 17:1283.
Charles D. Gilbert, The Rockefeller University, 1230 York Avenue, New York, New York 10021-6399, USA.
THE AUGUST 1994 ISSUE (VOL. 4, NO. 4) OF CURRENT OPINION IN NEUROBIOLOGY will include the following reviews, edited by A.J. Hudspeth and Michael Stryker, on Sensory Systems: Regeneration of hair cells in the vestibulocochlear system of birds and mammals by Douglas A. Cotanche and Kenneth H. Lee Neural substrates of sound localization by Michael S. Brainard Noise, neural codes and cortical organization by Michael N. Shadlen and William T. Newsome Survival factors in retinal degeneration by Roy H. Steinberg Fast active processes in the cochlea by Jonathan F. Ashmore and Paul J. Kolston Differences in transduction between rod and cone photoreceptors: an exploration of the role of calcium homeostasis by James L. Miller, Arturo Picones and Juan I. Korenbrot Sensory gating mechanisms of the thalamus by David A. McCormick and Thierry Bal The pathophysiology of chronic pain - increased sensitivity to low threshold AP-fibre inputs by Clifford J. Woolf and Tim P. Doubell Linearity of synaptic interactions in the assembly of receptive fields in cat visual cortex by David Ferster Calcium signalling in hair cells: multiple roles in a compact cell by David Lenzi and William M. Roberts Cortical circuitry in a dish by Jurgen Bolz Termination of photoreceptor responses by James B. Hurley Plasticity in auditory cortical circuitry by Ehud Ahissar and Merav Ahissar Chemosensing and signal transduction in bacteria byJeffStock, Michael Surette and Peter Park Development, critical period plasticity, and adult reorganization of mammalian somatosensory systems by Dennis DM O'Leary, Naomi L. Ruff and Richard H. Dyck Compartments in the olfactory periphery by Linda Buck
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