Development of ocularity domains and growth behaviour of axon terminals

Malsburg, Christoph von der

doi:10.1007/bf00337452

Cited by 105 publications

(17 citation statements)

References 22 publications

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“…Neighboring units in the output layer were connected via excitatory connections, and units further away were connected via inhibitory connections, in keeping with previous models of cortical self-organization (8)(9). (Other architectures would also be consistent with our explanation, e.g., normalization of output activations as opposed to long range inhibitory connections.…”

Section: Neural Network Modelsupporting

confidence: 65%

The neural development and organization of letter recognition: Evidence from functional neuroimaging, computational modeling, and behavioral studies

Polk

Farah

1998

Proc. Natl. Acad. Sci. U.S.A.

155

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Although much of the brain's functional organization is genetically predetermined, it appears that some noninnate functions can come to depend on dedicated and segregated neural tissue. In this paper, we describe a series of experiments that have investigated the neural development and organization of one such noninnate function: letter recognition. Functional neuroimaging demonstrates that letter and digit recognition depend on different neural substrates in some literate adults. How could the processing of two stimulus categories that are distinguished solely by cultural conventions become segregated in the brain? One possibility is that correlation-based learning in the brain leads to a spatial organization in cortex that ref lects the temporal and spatial clustering of letters with letters in the environment. Simulations confirm that environmental co-occurrence does indeed lead to spatial localization in a neural network that uses correlation-based learning. Furthermore, behavioral studies confirm one critical prediction of this co-occurrence hypothesis, namely, that subjects exposed to a visual environment in which letters and digits occur together rather than separately (postal workers who process letters and digits together in Canadian postal codes) do indeed show less behavioral evidence for segregated letter and digit processing.Localization of function is a basic feature of brain organization, revealed by the selectivity of impairments after brain damage and by techniques for recording regional brain activity. A wide variety of behavioral functions are now thought to be localized in the brain, ranging from sensorimotor functions such as motor control and sensation in the various modalities to high level cognitive functions such as explicit learning.What leads such functions to become localized in the brain? For the previous cases, the answer is presumably genetics. Sensorimotor functions, explicit learning, and many other examples of localized functions are old on an evolutionary scale, are shared with other species, provide a clear adaptive advantage, and develop automatically with no systematic training. It is thus possible that the brain has evolved to dedicate tissue to these functions. Consistent with this hypothesis, there is no evidence for the localization of functions like ballet dancing or chess playing: These functions are not old on an evolutionary scale, are not shared with other species, do not provide a clear adaptive advantage, and do not develop automatically without systematic training.There are, however, a few cognitive functions that may violate this simple generalization. For example, handwriting can be impaired selectively by brain damage while other manual motor control tasks are relatively well preserved (1). Indeed, recent evidence suggests that even the writing of cursive vs. print and uppercase vs. lowercase (2) can dissociate after brain damage. The fact that these functions can be impaired selectively suggests that their neural substrates are localized and spatially...

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Section: Neural Network Modelsupporting

confidence: 65%

The neural development and organization of letter recognition: Evidence from functional neuroimaging, computational modeling, and behavioral studies

Polk

Farah

1998

Proc. Natl. Acad. Sci. U.S.A.

155

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“…Early models of synaptic learning used the correlations between the firing rates of pre-and postsynaptic neurons as the signal for plasticity (von der Malsburg 1979;Bienenstock et al 1982;Miller et al 1989). While there were some indications that temporal order might be important for plasticity (Levy and Steward 1983;Gustafsson et al 1987;Debanne et al 1994), initial phenomenological characterizations of long-term synaptic plasticity largely focused on the requirements of coincident activity between pre-and postsynaptic neurons (Barrionuevo and Brown 1983;Malinow and Miller 1986) leading to rate based learning rules.…”

Section: Temporally Asymmetric Plasticitymentioning

confidence: 99%

Spike-timing-dependent plasticity: common themes and divergent vistas

Kepecs

Rossum

Song

et al. 2002

Biological Cybernetics

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Recent experimental observations of spike-timing-dependent synaptic plasticity (STDP) have revitalized the study of synaptic learning rules. The most surprising aspect of these experiments lies in the observation that synapses activated shortly after the occurrence of a postsynaptic spike are weakened. Thus, synaptic plasticity is sensitive to the temporal ordering of pre- and postsynaptic activation. This temporal asymmetry has been suggested to underlie a range of learning tasks. In the first part of this review we highlight some of the common themes from a range of findings in the framework of predictive coding. As an example of how this principle can be used in a learning task, we discuss a recent model of cortical map formation. In the second part of the review, we point out some of the differences in STDP models and their functional consequences. We discuss how differences in the weight-dependence, the time-constants and the non-linear properties of learning rules give rise to distinct computational functions. In light of these computational issues raised, we review current experimental findings and suggest further experiments to resolve some controversies.

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“…We refer to this as a type 1 constraint, and to a multiplicative or subtractive constraint of this type as M1 or S1, respectively. These are frequently used in modeling studies (e.g., M1: Grajski and Merzenich 1990;von der Malsburg 1973von der Malsburg , 1979von der Malsburg and Willshaw 1976;Perez et al 1975;Rochester et al 1956;Whitelaw and Cowan 1981;von der Malsburg 1976, 1979;S1: Miller 1992;Miller et al 1989).3 We define a type 1 constraint more generally as one that conserves the total weighted synaptic strength, 1, w,n, = w . n, where n is a constant vector.…”

Section: 1mentioning

confidence: 99%

The Role of Constraints in Hebbian Learning

1994

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Models of unsupervised, correlation-based (Hebbian) synaptic plasticity are typically unstable: either all synapses grow until each reaches the maximum allowed strength, or all synapses decay to zero strength. A common method of avoiding these outcomes is to use a constraint that conserves or limits the total synaptic strength over a cell. We study the dynamic effects of such constraints.Two methods of enforcing a constraint are distinguished, multiplicative and subtractive. For otherwise linear learning rules, multiplicative enforcement of a constraint results in dynamics that converge to the principal eigenvector of the operator determining unconstrained synaptic development. Subtractive enforcement, in contrast, typically leads to a final state in which almost all synaptic strengths reach either the maximum or minimum allowed value. This final state is often dominated by weight configurations other than the principal eigenvector of the unconstrained operator. Multiplicative enforcement yields a "graded" receptive field in which most mutually correlated inputs are represented, whereas subtractive enforcement yields a receptive field that is "sharpened" to a subset of maximally correlated inputs. If two equivalent input populations (e.g., two eyes) innervate a common target, multiplicative enforcement prevents their segregation (ocular dominance segregation) when the two populations are weakly correlated; whereas subtractive enforcement allows segregation under these circumstances.These results may be used to understand constraints both over output cells and over input cells. A variety of rules that can implement constrained dynamics are discussed.Development in many neural systems appears to be guided by "Hebbian" or similar activity-dependent, correlation-based rules of synaptic modification (reviewed in Miller 1990a). Several lines of reasoning suggest 'Current address: Departments of Physiology and Otolaryngology, University of 'Current address: Radio Astronomy,

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Development of ocularity domains and growth behaviour of axon terminals

Cited by 105 publications

References 22 publications

The neural development and organization of letter recognition: Evidence from functional neuroimaging, computational modeling, and behavioral studies

The neural development and organization of letter recognition: Evidence from functional neuroimaging, computational modeling, and behavioral studies

Spike-timing-dependent plasticity: common themes and divergent vistas

The Role of Constraints in Hebbian Learning

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